System for converting healthcare contract language into executable reimbursement logic

Abstract

A healthcare contract analysis system is provided. The healthcare contract analysis system includes one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to receive a healthcare payer contract document and extract structured payment terms from the healthcare payer contract document. The system compiles the structured payment terms into a set of machine-executable payment rules, wherein each payment rule comprises typed parameters, one or more applicability conditions, and an execution priority. The system receives patient encounter data and applies the set of machine-executable payment rules to the patient encounter data according to the execution priorities to generate an expected reimbursement result. The system generates an output based on the expected reimbursement result.

Description

Docket No.: BLUE-01 -PCTSYSTEM FOR CONVERTING HEALTHCARE CONTRACT LANGUAGE INTO EXECUTABLE REIMBURSEMENT LOGICCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from U.S. Provisional Application Serial No.63 / 788.052 filed on April 13, 2025 which is incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not Applicable.INTRODUCTION

[0003] Healthcare providers engage in contractual relationships with health insurance payers that govern reimbursement for healthcare services. These healthcare payer contracts may contain payment terms spanning multiple pages and may include varying payment methodologies, fee schedules, discount structures, carve-out provisions, stop-loss thresholds, and other reimbursement parameters. The payment methodologies defined in healthcare payer contracts may include diagnosis-related group based payment, ambulatory payment classification based payment, fee schedule based payment, per diem payment, percentage-of-charge payment, and combinations thereof.

[0004] Healthcare payer contracts may be structured in various formats and may include amendments, exhibits, appendices, rate tables, and related supporting materials. The payment terms within these contracts may define complex relationships among base rates, multipliers, applicability conditions, and adjustment provisions. Healthcare providers may maintain contracts with multiple payer organizations, each with distinct payment terms and reimbursement methodologies.

[0005] Existing approaches to healthcare contract analysis may involve manual review and interpretation of contract documents. Manual contract abstraction workflows may require personnel to read contract materials, identify payment-relevant provisions, and translate contract language into operational payment logic. Such manual processes may be time-consuming and may require specialized knowledge of healthcare reimbursement concepts. Manual interpretation of contract terms at the time of reimbursement calculation may introduce vari ability in how payment provisions are applied across different patient encounters.Docket No.: BLUE-01 -PCT

[0006] Healthcare providers may seek to compare expected reimbursement amounts calculated from contract terms against actual reimbursement amounts received from payers. Identifying variances between expected and actual reimbursement may involve reviewing individual patient encounters and associated remittance data. Healthcare providers may also seek to evaluate the financial impact of proposed contract term modifications or to compare reimbursement outcomes across multiple payer contracts.

[0007] Accordingly, there remains ongoing interest in systems and methods for healthcare contract analysis and reimbursement calculation.SUMMARY

[0008] According to an aspect of the present disclosure, a healthcare contract analysis system is provided. The system includes one or more processors and one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to receive a healthcare payer contract document and extract structured payment terms from the healthcare payer contract document. The system compiles the structured payment terms into a set of machine-executable payment rules, wherein each payment rule comprises typed parameters, one or more applicability conditions, and an execution priority. The system receives patient encounter data and applies the set of machine-executable payment rules to the patient encounter data according to the execution priorities to generate an expected reimbursement result. The system generates an output based on the expected reimbursement result. The system operates by transforming unstructured healthcare payer contract documents into structured, machine-executable payment rules that can be systematically applied to patient encounter data, thereby enabling automated calculation of expected reimbursement amounts based on contractual terms.

[0009] According to other aspects of the present disclosure, the healthcare contract analysis system may provide technical effects beyond reimbursement calculation. The system may enable identification of payment variances between expected and actual reimbursement, facilitate contract negotiation through financial impact analysis, and support billing optimization across multiple payer contracts.

[0010] According to other aspects of the present disclosure, the healthcare contract analysis system may include one or more of the following features. The system may validate the structured payment terms against one or more validation criteria and normalize the structured payment terms to a standardized format prior to compiling the structured payment terms into the set of machine-executable payment rules. Extracting the structured paymentDocket No.: BLUE-01 -PCTterms may comprise preprocessing the healthcare payer contract document to identify document structure including sections, tables, and appendices, performing semantic analysis on the preprocessed document to identify payment-relevant content, and extracting payment term data from the identified payment-relevant content. Extracting the structured payment terms may comprise processing the healthcare payer contract document using a large language model configured with healthcare payment domain context. The healthcare payment domain context may compnse one or more of a healthcare payment terminology dictionary, contract structure templates, payment methodology patterns, and payer-specific term definitions. The system may determine a confidence score for each extracted structured payment term and route structured payment terms having confidence scores below a threshold to a human review workflow.

[0011] The typed parameters may comprise one or more of base rates, multipliers, discount percentages, can e-out provisions, stop-loss thresholds, and outlier provisions. The one or more applicability conditions may comprise one or more of service type constraints, diagnosis code requirements, procedure code applicability, date range limitations, patient type restrictions, and facility-specific terms. The set of machine-executable payment rules may comprise one or more rule types selected from base payment rules, discount rules, carve-out rules, stop-loss rules, outlier adjustment rules, multiple procedure discount rules, and lesser-of rules. Applying the set of machine-executable payment rules according to the execution priorities may comprise applying rules in a hierarchical sequence comprising applying carve-out rules, applying base payment rules, applying discount rules, applying multiple procedure rules, applying stop-loss rules, and applying lesser-of rules. The base payment rules may implement one or more payment methodologies selected from diagnosis-related group based payment, ambulatory payment classification based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment.

[0012] The system may receive remittance data associated with the patient encounter data, compare the expected reimbursement result to an actual reimbursement amount indicated in the remittance data, determine a variance between the expected reimbursement result and the actual reimbursement amount, and generate an underpayment indication when the variance exceeds a threshold. The system may classify the variance according to one or more underpayment categories and generate appeal documentation based on the classification. The system may receive a proposed modification to one or more of the structured payment terms, compile the proposed modification into a modified set of machineexecutable payment rules, apply the modified set of machine-executable payment rules to aDocket No.: BLUE-01 -PCThistorical encounter population, and generate a financial impact analysis comparing reimbursement outcomes under the set of machine-executable payment rules and the modified set of machine-executable payment rules.

[0013] The system may compile a first set of machine-executable payment rules from a first healthcare payer contract, compile a second set of machine-executable payment rules from a second healthcare payer contract, apply the first set of machine-executable payment rules to the patient encounter data to generate a first expected reimbursement result, apply the second set of machine-executable payment rules to the patient encounter data to generate a second expected reimbursement result, and generate a comparative analysis identifying a reimbursement difference between the first expected reimbursement result and the second expected reimbursement result. The first healthcare payer contract may be associated with a first Blue Cross Blue Shield entity and the second healthcare payer contract may be associated with a second Blue Cross Blue Shield entity, and the comparative analysis may comprise a billing entity recommendation. The comparative analysis may further comprise evaluation of one or more decision factors selected from payment amount, processing time, and denial risk. The system may generate an audit trail comprising rule-by-rule impact analysis linking each applied payment rule to a corresponding portion of the healthcare payer contract document.

[0014] Compiling the structured payment terms into the set of machine-executable payment rules may comprise generating a rule dependency graph and determining the execution priority for each payment rule based on the rule dependency graph. The system may detect an unresolved dependency in the structured payment terms, wherein the unresolved dependency comprises a reference to an unresolved fee schedule, exhibit, or attachment, and block compilation of a payment rule associated with the unresolved dependency until the unresolved dependency is resolved. The system may maintain a plurality of fee schedule versions associated with the healthcare payer contract, determine an encounter date from the patient encounter data, and select a fee schedule version from the plurality of fee schedule versions based on the encounter date. The system may receive an updated extraction of structured payment terms from the healthcare payer contract document, compare the updated extraction to previously compiled payment rules, and reconcile the updated extraction with the previously compiled payment rules while preserving manually adjusted rule parameters. The system may receive a proposed set of payment rules, apply the proposed set of payment rules to a historical encounter population in a shadow calculation mode without persisting results, and generate a comparison between outcomes of theDocket No.: BLUE-01 -PCTproposed set of payment rules and outcomes of the set of machine-executable payment rules.

[0015] The patient encounter data may comprise one or more of patient demographic information, diagnosis codes, procedure codes, service dates, facility identifiers, billing form type, and charge amounts. The output may comprise one or more of an expected payment amount, a patient financial responsibility estimate, a reimbursement variance report, and a contract performance analysis.

[0016] According to another aspect of the present disclosure, a method for converting healthcare contract language into executable reimbursement logic is provided. The method comprises receiving, by one or more processors, a healthcare payer contract document and extracting, by the one or more processors, structured payment terms from the healthcare payer contract document. The method comprises validating and normalizing, by the one or more processors, the structured payment terms and compiling, by the one or more processors, the validated and normalized structured payment terms into a set of machineexecutable payment rules, wherein each payment rule comprises typed parameters, one or more applicability conditions, and an execution priority. The method comprises receiving, by the one or more processors, patient encounter data and identifying, by the one or more processors, applicable payment rules from the set of machine-executable payment rules based on the patient encounter data and the one or more applicability conditions. The method comprises applying, by the one or more processors, the applicable payment rules to the patient encounter data according to the execution priorities to generate an expected reimbursement result and outputting, by the one or more processors, the expected reimbursement result. The method operates by systematically converting unstructured contract language into validated, normalized payment rules that can be matched to specific patient encounters and executed in priority order to produce accurate reimbursement calculations.

[0017] According to other aspects of the present disclosure, the method may include one or more of the following features. Extracting the structured payment terms may comprise performing optical character recognition on the healthcare payer contract document, identifying structural elements including sections, tables, and fee schedules, segmenting the healthcare payer contract document based on semantic boundaries, and extracting payment term data from identified payment sections. Extracting the structured payment terms may comprise providing the healthcare payer contract document to a large language model, providing healthcare payment domain context to the large language model, and receiving extracted payment terms from the large language model. Validating and normalizing theDocket No.: BLUE-01 -PCTstructured payment terms may comprise standardizing terminology to an internal schema, converting textual amounts to decimal values, standardizing percentage representations, and performing consistency checks across extracted terms.

[0018] Compiling the validated and normalized structured payment terms may comprise assigning each payment rule to a rule category, defining typed parameters for each payment rule, defining one or more applicability conditions for each payment rule, and assigning an execution priority to each payment rule. The rule category may be selected from diagnosis-related group based reimbursement, ambulatory payment classification based reimbursement, fee schedule reimbursement, per diem reimbursement, percentage-of-charge reimbursement, carve-out rules, outlier adjustments, stop-loss rules, lesser-of rules, and multiple procedure adjustments. Applying the applicable payment rules according to the execution priorities may comprise applying carve-out rules to identify services subject to separate payment terms, applying base payment rules to establish a foundational reimbursement calculation, applying discount rules to modify the foundational reimbursement calculation, applying multiple procedure rules to adjust reimbursement for multiple procedures performed during a same encounter, applying stop-loss rules to limit payment adjustments, and applying lesser-of rules to select an appropriate payment amount based on contract terms.

[0019] The method may further comprise receiving remittance data corresponding to the patient encounter data, determining an actual reimbursement amount from the remittance data, calculating a variance between the expected reimbursement result and the actual reimbursement amount, and generating an underpayment signal when the variance indicates the actual reimbursement amount is less than the expected reimbursement result by more than a threshold amount. The method may further comprise classifying a root cause of the underpayment and generating appeal evidence documentation based on the root cause classification and the applicable payment rules. The method may further comprise receiving a proposed contract term modification, generating a modified set of machine-executable payment rules incorporating the proposed contract term modification, applying the modified set of machine-executable payment rules to a historical encounter population, calculating aggregate financial impact of the proposed contract term modification, and generating a contract negotiation report comprising the aggregate financial impact.

[0020] The method may further comprise compiling a first set of machineexecutable payment rules from a first healthcare payer contract, compiling a second set of machine-executable payment rules from a second healthcare payer contract, applying the firstDocket No.: BLUE-01 -PCTset of machine-executable payment rules to an encounter to generate a first expected reimbursement, applying the second set of machine-executable payment rules to the encounter to generate a second expected reimbursement, comparing the first expected reimbursement and the second expected reimbursement, and generating a payer routing recommendation based on the comparison. The first healthcare payer contract may be associated with a Blue Cross entity and the second healthcare payer contract may be associated with a Blue Shield entity, and the payer routing recommendation may identify which entity to bill for an out-of-state patient. The method may further comprise generating an execution path record tracking each payment rule applied to the patient encounter data and storing the execution path record for audit purposes. The method may further comprise detecting an unresolved reference to a fee schedule, exhibit, or attachment in the structured payment terms and blocking compilation of an associated payment rule until the unresolved reference is resolved.

[0021] The method may further comprise maintaining a plurality7of versioned fee schedules, determining an encounter date from the patient encounter data, and selecting an applicable fee schedule version based on the encounter date. The method may further comprise receiving an updated extraction of structured payment terms, identifying differences between the updated extraction and previously compiled payment rules, preserving manually adjusted parameters in the previously compiled payment rules, and updating the previously- compiled payment rules with non-conflicting terms from the updated extraction. The method may further comprise performing batch calculation of expected reimbursement results across a population of patient encounters and generating population-level reimbursement analytics. The expected reimbursement result may comprise a calculated payment amount, an identification of each payment rule applied, and a contribution of each applied payment rule to the calculated payment amount.

[0022] According to another aspect of the present disclosure, a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations is provided. The operations comprise receiving a healthcare payer contract document and extracting structured payment terms from the healthcare payer contract document using a term extraction module. The operations comprise compiling the structured payment terms into a set of machineexecutable payment rules using a rule compilation module, wherein each payment rule comprises typed parameters defining payment amounts, percentages, or thresholds, one or more applicability conditions defining when the payment rule applies, and an executionDocket No.: BLUE-01 -PCTpriority defining an order of application relative to other payment rules. The operations comprise storing the set of machine-executable payment rules in a rule repositor}’, receiving patient encounter data, and applying the set of machine-executable payment rules to the patient encounter data using a calculation engine, wherein the calculation engine applies the payment rules according to the execution priorities. The operations comprise generating an expected reimbursement output. The non-transitory computer-readable medium operates by providing executable instructions that enable automated transformation of contract documents into structured payment rules and subsequent application of those rules to generate accurate reimbursement calculations.

[0023] According to other aspects of the present disclosure, the non-transitory' computer-readable medium may include one or more of the following features. The term extraction module may comprise a large language model configured with healthcare reimbursement domain context. The operations may further comprise validating the structured payment terms using a validation module and normalizing the structured payment terms to a standardized representation using a normalization module. The operations may further comprise determining a confidence score for each extracted structured payment term, comparing the confidence score to a confidence threshold, and routing structured payment terms having confidence scores below the confidence threshold to a human review interface.

[0024] The set of machine-executable payment rules may comprise carve-out rules having a first execution priority, base payment rules having a second execution priority- loyver than the first execution priority, discount rules having a third execution priority' loyver than the second execution priority', multiple procedure rules having a fourth execution priority' lower than the third execution priority, stop-loss rules having a fifth execution priority lower than the fourth execution priority’, and lesser-of rules having a sixth execution priority lower than the fifth execution priority. The base payment rules may implement one or more healthcare payment methodologies selected from diagnosis-related group based payment methodology, ambulatory’ payment classification based payment methodology, fee schedule based payment methodology, per diem payment methodology, and percentage-of-charge payment methodology.

[0025] The operations may further comprise receiving remittance data associated yy ith the patient encounter data, determining an actual payment amount from the remittance data, calculating a variance between the expected reimbursement output and the actual payment amount, determining whether the variance exceeds a variance threshold, and generating an underpayment alert when the variance exceeds the variance threshold. TheDocket No.: BLUE-01 -PCToperations may further comprise classifying the underpayment according to a root cause category and generating appeal documentation comprising contract rule citations supporting the expected reimbursement output. The operations may further comprise receiving a contract modification proposal, generating a modified set of machine-executable payment rules based on the contract modification proposal, applying the modified set of machine-executable payment rules to a historical encounter dataset, calculating a financial impact of the contract modification proposal, and generating a contract simulation report comprising the financial impact.

[0026] The operations may further comprise compiling a first set of machineexecutable payment rules from a first payer contract, compiling a second set of machineexecutable payment rules from a second payer contract, applying the first set of machineexecutable payment rules to the patient encounter data to generate a first expected reimbursement, applying the second set of machine-executable payment rules to the patient encounter data to generate a second expected reimbursement, performing a comparative analysis of the first expected reimbursement and the second expected reimbursement, and generating a billing optimization recommendation based on the comparative analysis. The first payer contract may be associated with a first Blue Cross Blue Shield entity and the second payer contract may be associated with a second Blue Cross Blue Shield entity, and the billing optimization recommendation may identify an optimal billing entity for an out-of-state patient encounter. The comparative analysis may evaluate one or more factors selected from expected payment amount, estimated processing time, and estimated denial risk. The operations may further comprise generating an audit trail linking each applied payment rule to a source location in the healthcare payer contract document.

[0027] The operations may further comprise detecting an unresolved dependency in the structured payment terms, blocking compilation of a payment rule dependent on the unresolved dependency, and generating a notification indicating the unresolved dependency. The operations may further comprise storing a plurality of fee schedule versions with associated effective date ranges, determining an encounter date from the patient encounter data, and selecting an applicable fee schedule version based on the encounter date falling within an associated effective date range. The operations may further comprise receiving updated structured payment terms extracted from the healthcare payer contract document, comparing the updated structured payment terms to previously stored payment rules, identifying manually adjusted parameters in the previously stored payment rules, and reconciling the updated structured payment terms with the previously stored payment rulesDocket No.: BLUE-01 -PCTwhile preserving the manually adjusted parameters. The operations may further comprise receiving a proposed rule modification, executing a shadow calculation applying the proposed rule modification to a historical encounter population without persisting results, and generating a shadow calculation report comparing outcomes under the proposed rule modification to outcomes under current payment rules.

[0028] According to another aspect of the present disclosure, a healthcare reimbursement optimization system is provided. The system comprises a contract ingestion module configured to receive healthcare payer contract documents in one or more document formats and a term extraction module configured to extract structured payment terms from the healthcare payer contract documents. The system comprises a validation module configured to validate the structured payment terms against validation criteria and normalize the structured payment terms. The system comprises a rule compilation module configured to compile the validated and normalized structured payment terms into machine-executable payment rules, wherein each machine-executable payment rule comprises ty ped parameters, applicability conditions, and an execution priority. The system comprises a rule repository configured to store the machine-executable payment rules. The system comprises a calculation engine configured to receive patient encounter data, identify applicable payment rules from the rule repository7based on the patient encounter data and the applicability conditions, apply the applicable payment rules to the patient encounter data according to the execution priorities, and generate expected reimbursement results. The system comprises an output module configured to generate outputs based on the expected reimbursement results. The system operates by providing a modular architecture that transforms healthcare payer contracts into executable payment logic and applies that logic to patient encounters to generate accurate reimbursement calculations.

[0029] According to other aspects of the present disclosure, the healthcare reimbursement optimization system may include one or more of the following features. The term extraction module may comprise a large language model processor configured with healthcare payment domain context including one or more of healthcare payment terminology, contract structure templates, payment methodology patterns, and payer-specific definitions. The system may further comprise a confidence scoring module configured to determine confidence scores for extracted structured payment terms and a review interface configured to present structured payment terms having confidence scores below a threshold for human review.

[0030] The machine-executable payment rules may comprise rule types includingDocket No.: BLUE-01 -PCTcarve-out rules configured to identify services subject to separate payment terms, base payment rules configured to calculate foundational reimbursement amounts using one or more payment methodologies, discount rules configured to apply contractual discounts to calculated amounts, multiple procedure rules configured to adjust reimbursement for multiple procedures, stop-loss rules configured to limit payment adjustments, and lesser-of rules configured to select payment amounts based on comparative calculations. The calculation engine may be configured to apply the applicable payment rules in a hierarchical sequence comprising applying carve-out rules at a highest priority, applying base payment rules, applying discount rules, applying multiple procedure rules, applying stop-loss rules, and applying lesser-of rules at a lowest priority. The base payment rules may implement payment methodologies including one or more of diagnosis-related group based payment, ambulatory payment classification based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment.

[0031] The system may further comprise a variance analysis module configured to receive remittance data associated with the patient encounter data, compare the expected reimbursement results to actual reimbursement amounts from the remittance data, calculate variance between expected and actual reimbursement, and generate underpayment signals when variance exceeds a threshold. The variance analysis module may be further configured to classify underpayments according to root cause categories and generate appeal documentation comprising rule-based payment rationale. The system may further comprise a contract modeling module configured to receive proposed modifications to payment terms, generate modified payment rules incorporating the proposed modifications, apply the modified payment rules to historical encounter data, and generate financial impact analyses comparing outcomes under current and modified payment rules.

[0032] The system may further comprise a multi-payer comparison module configured to compile payment rules from a plurality of payer contracts, apply each set of payment rules to common encounter data, compare expected reimbursement results across the plurality of payer contracts, and generate payer routing recommendations. The plurality of payer contracts may comprise contracts from different Blue Cross Blue Shield entities, and the payer routing recommendations may identify an optimal billing entity for out-of-state patient encounters. The system may further comprise an audit module configured to generate audit trails linking applied payment rules to source contract provisions. The rule compilation module may be configured to detect unresolved dependencies in the structured payment terms and block compilation of payment rules having unresolved dependencies until theDocket No.: BLUE-01 -PCTdependencies are resolved. The rule repository may be configured to store a plurality of fee schedule versions with associated effective date ranges, and the calculation engine may be configured to select an applicable fee schedule version based on an encounter date. The rule compilation module may be configured to receive updated extracted payment terms, compare the updated extracted payment terms to previously compiled payment rules, presen e manually adjusted parameters in the previously compiled payment rules, and update the previously compiled payment rules with non-conflicting terms from the updated extracted payment terms.

[0033] According to another aspect of the present disclosure, a healthcare contract reimbursement analysis system is provided. The system comprises a contract ingestion module configured to receive healthcare payer contract documents and a term extraction module configured to extract structured payment terms from the healthcare payer contract documents using a language model processor. The system comprises a validation and normalization module configured to validate and normalize the structured payment terms. The system comprises a rule compilation module configured to compile the validated and normalized structured payment terms into machine-executable payment rules, wherein each machine-executable payment rule comprises typed parameters, applicability conditions, and an execution priority7. The system comprises a rule repository7configured to store the machine-executable payment rules. The system comprises a calculation engine configured to receive patient encounter data, identify applicable payment rules from the rule repository based on the patient encounter data, apply the applicable payment rules to the patient encounter data according to the execution priorities, and generate expected reimbursement results. The system comprises a variance analysis module configured to compare the expected reimbursement results to actual reimbursement amounts and generate underpayment signals. The system comprises a contract modeling module configured to generate financial impact analyses for proposed modifications to payment terms. The system comprises an output module configured to generate outputs based on the expected reimbursement results. The system operates by providing an integrated architecture that combines contract parsing, rule compilation, reimbursement calculation, variance analysis, and contract modeling capabilities to enable comprehensive healthcare reimbursement management.

[0034] These and other features, aspects and advantages of the present teachings will become better understood with reference to the following description, examples and appended claims.Docket No.: BLUE-01 -PCTDRAWINGS

[0035] Those of skill in the art will understand that the drawings, described below, are for illustrative purposes only. The drawings are not intended to limit the scope of the present teachings in any way.

[0036] FIG. 1 illustrates a block diagram of a healthcare contract analysis system architecture.

[0037] FIG. 2 depicts a flowchart of a contract parsing workflow.

[0038] FIG. 3 illustrates a flowchart of a rules engine processing flow.

[0039] FIG. 4 depicts a flowchart of an optimization decision process.DETAILED DESCRIPTION

[0040] All patents, applications, published applications and other publications cited herein are incorporated by reference in their entirety. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs.

[0041] Any methods, devices and materials similar or equivalent to those described herein can be used in the practice of this invention. The following definitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the present disclosure. In the event that there is a plurality of definitions for a term herein, those in this section prevail unless stated otherwise. Headings used herein are for organizational purposes only and in no way limit the invention described herein.

[0042] Abbreviations and Definitions

[0043] To facilitate understanding of the invention, a number of terms and abbreviations as used herein are defined below as follows:

[0044] Large Language Model (LLM): As used herein, the term "Large Language Model" or "LLM" is broadly defined to include any artificial intelligence model optimized for natural language understanding, generation, or processing, including but not limited to transformer-based architectures such as GPT-4 and GPT-3.5 developed by OpenAI, L.P., Claude developed by Anthropic, PBC, Gemini and PaLM developed by Google LLC, LLaMA developed by Meta Platforms, Inc., Mistral developed by Mistral Al, BERT developed by Google LLC, and T5 developed by Google LLC. The term further encompasses encoder-decoder models, autoregressive language models, instruction-tuned models, retrieval-augmented generation systems, and any proprietary, open-source, or fine-tunedDocket No.: BLUE-01 -PCTcommercial equivalents capable of processing natural language inputs, including models available through Amazon Web Services, Inc. (Amazon Bedrock), Microsoft Corporation (Azure OpenAI Service), and Hugging Face, Inc. The term encompasses models deployed via cloud-based APIs, on-premise installations, edge deployments, or hybrid configurations.

[0045] Machine-Executable Payment Rule: As used herein, the term "machineexecutable payment rule" refers to a structured, computer-interpretable representation of a healthcare payment term that can be programmatically applied to patient encounter data to calculate reimbursement amounts. A machine-executable payment rule may include typed parameters, applicability conditions, execution priority metadata, and source linkage information, and may be represented in formats including but not limited to JSON schemas, object-oriented data structures, rule definition languages, or database records.

[0046] Healthcare Payer Contract Document: As used herein, the term "healthcare payer contract document" is broadly defined to include any document, file, or data source containing terms governing healthcare reimbursement between a healthcare provider and a payer entity, including but not limited to negotiated contracts, amendments, exhibits, fee schedules, rate tables, appendices, letters of agreement, addenda, policy documents, and related supporting materials, in formats including PDF, DOCX, scanned images, text files, spreadsheets, or structured data imports.

[0047] Patient Encounter Data: As used herein, the term "patient encounter data" refers to information associated with a healthcare service event, including but not limited to patient demographic information, diagnosis codes (e.g., ICD-10-CM, ICD-10-PCS), procedure codes (e.g., CPT, HCPCS), sendee dates, facility' identifiers, provider identifiers, billing form types (e.g., UB-04, CMS-1500), charge amounts, length of stay, discharge status, and related clinical or administrative data elements used in reimbursement calculations.

[0048] Rules Engine: As used herein, the term "rules engine" refers to a software component or system configured to evaluate conditions, match applicable rules to input data, and execute rule logic according to defined priorities or sequences. A rules engine may be implemented using in-memory evaluation frameworks, business rule management systems, decision engines, inference engines, or custom rule execution architectures.

[0049] Payment Methodology: As used herein, the term "payment methodology" is broadly defined to include any method, formula, or approach for calculating healthcare reimbursement amounts, including but not limited to diagnosis-related group (DRG) based payment, ambulatory payment classification (APC) based payment, fee schedule based payment, per diem payment, percentage-of-charge payment, case rate payment, capitation,Docket No.: BLUE-01 -PCTbundled payment, episode-based payment, and related reimbursement calculation approaches.

[0050] Structured Payment Term: As used herein, the term "structured payment term" refers to a payment-related provision extracted or derived from a healthcare payer contract document and represented in a normalized, machine-readable format suitable for compilation into executable payment rules. Structured payment terms may include base rates, multipliers, discount percentages, carve-out provisions, stop-loss thresholds, outlier provisions, effective date ranges, and applicability conditions.

[0051] Calculation Engine: As used herein, the term "calculation engine" refers to a software component or system configured to perform reimbursement calculations by applying payment rules to patient encounter data. A calculation engine may support batch processing, single-encounter evaluation, parallel computation, and may implement decimalbased arithmetic, payer-specific rounding methodologies, and complex payment formulas.

[0052] Remittance Data: As used herein, the term "remittance data" refers to information received from a payer indicating payment disposition for healthcare claims, including but not limited to Electronic Remittance Advice (ERA) data, EDI X12835 transaction data. Explanation of Benefits (EOB) information, payment amounts, adjustment codes, denial codes, and related payment response information.

[0053] Variance: As used herein, the term "variance" refers to a difference between an expected reimbursement result calculated by the system and an actual reimbursement amount indicated in remittance data or other payment records. Variance may be expressed as an absolute amount, a percentage, or other comparative measure.

[0054] Fee Schedule: As used herein, the term "fee schedule" is broadly defined to include any listing, table, database, or data structure that associates healthcare services, procedures, or codes with corresponding payment amounts, rates, or reimbursement values, including but not limited to Medicare fee schedules, Medicaid fee schedules, commercial payer fee schedules, facility-specific fee schedules, and chargemasters.

[0055] Applicability Condition: As used herein, the term "applicability' condition" refers to a predicate, constraint, or criterion that determines when a payment rule applies to a given patient encounter. Applicability conditions may include service type constraints, diagnosis code requirements, procedure code applicability, date range limitations, patient type restrictions, facility-specific terms, payer or plan characteristics, and billing form or code-based conditions.

[0056] Execution Priority: As used herein, the term "execution priority" refers to metadata or ordering information that governs the sequence in which payment rules areDocket No.: BLUE-01 -PCTapplied during reimbursement calculation. Execution priorities may be expressed as numerical values, hierarchical levels, dependency relationships, or rule category orderings.

[0057] Module: As used herein, the term "module" may include a unit implemented in hardware, software, firmware, or any combination thereof, and may interchangeably be used with other terms such as "component," "unit," "logic," "logic block," "engine," "subsystem," or "circuitry." A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. A module may be implemented in a form of an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a general-purpose processor executing software instructions, a microcontroller, or any combination thereof.

[0058] Processor: As used herein, the term "processor" is broadly defined to include any computing device or component capable of executing instructions, including but not limited to central processing units (CPUs) such as those manufactured by Intel Corporation (e.g., Intel Core, Intel Xeon), Advanced Micro Devices, Inc. (e.g., AMD Ryzen, AMD EPYC), and ARM Holdings (e.g.. ARM Cortex); graphics processing units (GPUs) such as those manufactured by NVIDIA Corporation (e.g., NVIDIA Al 00, NVIDIA Hl 00) and Advanced Micro Devices, Inc. (e.g., AMD Radeon, AMD Instinct); tensor processing units (TPUs) such as those developed by Google LLC; neural processing units (NPUs) such as those manufactured by Apple Inc., Qualcomm Incorporated, and Intel Corporation; application-specific integrated circuits (ASICs) such as those manufactured by Broadcom Inc., Marvell Technology, Inc., and custom silicon providers; field-programmable gate arrays (FPGAs) such as those manufactured by Xilinx, Inc. (now part of Advanced Micro Devices, Inc ), Intel Corporation (formerly Altera), and Lattice Semiconductor Corporation; digital signal processors (DSPs) such as those manufactured by Texas Instruments Incorporated and Analog Devices, Inc.; microcontrollers such as those manufactured by Microchip Technology Inc., STMicroelectronics N.V., andNXP Semiconductors N.V.; system-on-chip (SoC) devices such as those manufactured by Qualcomm Incorporated, Apple Inc., and Samsung Electronics Co., Ltd.; and cloud-based or distributed computing resources such as those provided by Amazon Web Services, Inc., Microsoft Corporation (Azure), Google LLC (Google Cloud Platform), and Oracle Corporation.

[0059] Non-Transitory Computer-Readable Medium: As used herein, the term "non-transitory computer-readable medium" refers to any tangible storage medium capable of storing instructions executable by a processor, including but not limited to solid-state drives (SSDs), hard disk drives (HDDs), flash memory, read-only memory' (ROM), random-accessDocket No.: BLUE-01 -PCTmemory' (RAM), optical discs, magnetic storage media, and cloud-based or network-attached storage systems. The term expressly excludes transitory signals per se.

[0060] BlueCard Program: As used herein, the term "BlueCard Program" refers to the national program administered by the Blue Cross Blue Shield Association that enables members of one Blue Cross Blue Shield plan to receive healthcare services while traveling or living in another Blue Cross Blue Shield plan's service area, with claims processed through inter-plan arrangements.

[0061] Confidence Score: As used herein, the term "confidence score" refers to a numerical or categorical value indicating the degree of certainty' or reliability' associated with an extracted payment term, classification, or system output. Confidence scores may be derived from language model certainty’ indicators, pattern matching results, consistency checks, or other algorithmic assessments.

[0062] System for Converting Healthcare Contract Language into Executable Reimbursement Logic

[0063] The present disclosure relates to a healthcare contract analysis system configured to convert healthcare payer contract language into executable reimbursement logic. Referring to FIG. 1, a healthcare contract analysis system may comprise a layered architecture including a user interface layer 100, an application services layer 110, a core processing engine 120. and a data storage layer 130. The healthcare contract analysis system may receive healthcare payer contract documents, extract structured payment terms from the healthcare payer contract documents, and compile the structured payment terms into machine-executable payment rules. The healthcare contract analysis system may then apply the machine-executable payment rules to patient encounter data to generate expected reimbursement results.

[0064] In some aspects, the healthcare contract analysis system may address challenges associated with converting complex payer contracts into operational payment logic. Healthcare payer contracts may contain payment terms spanning multiple pages with varying payment methodologies, fee schedules, carve-out provisions, discount structures, and other reimbursement parameters. The healthcare contract analysis system may transform such unstructured contract language into structured, machine-executable payment rules that can be systematically applied to patient encounter data.

[0065] With continued reference to FIG. 1, the core processing engine 120 may include a contract parser 121 configured to process healthcare payer contract documents andDocket No.: BLUE-01 -PCTextract structured payment terms. The core processing engine 120 may further include a rule generator 124 configured to compile the extracted structured payment terms into machineexecutable payment rules. Each machine-executable payment rule may comprise typed parameters, one or more applicability conditions, and an execution priority. The typed parameters may define payment amounts, percentages, multipliers, thresholds, or other payment variables. The applicability conditions may define when a payment rule applies to a given patient encounter. The execution priority may govern the sequence in which payment rules are applied during reimbursement calculation.

[0066] In some cases, the healthcare contract analysis system may apply the machine-executable payment rules to patient encounter data through a rules engine 122 and a payment calculator 123. The rules engine 122 may identify applicable payment rules based on the patient encounter data and the applicability conditions, and may apply the applicable payment rules according to the execution priorities. The payment calculator 123 may perform reimbursement calculations based on the applied rules to generate expected reimbursement results.

[0067] The healthcare contract analysis system may support multiple downstream applications using the expected reimbursement results. In some aspects, the healthcare contract analysis system may compare expected reimbursement results to actual reimbursement amounts indicated in remittance data to identify payment variances and generate underpayment signals. In some cases, the healthcare contract analysis system may apply modified payment rules to historical encounter data to generate financial impact analyses for proposed contract term modifications. In some aspects, the healthcare contract analysis system may compile payment rules from multiple payer contracts and compare expected reimbursement results across the multiple payer contracts to generate payer routing recommendations.

[0068] The healthcare contract analysis system may provide a reproducible and auditable approach to reimbursement analysis by converting structured contract terms into executable rule representations. The healthcare contract analysis system may generate audit trails linking applied payment rules to source contract provisions, thereby preserving traceability between reimbursement calculations and originating contract materials. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0069] System Architecture OverviewDocket No.: BLUE-01 -PCT

[0070] Referring to FIG. 1, a healthcare contract analysis system may comprise a layered architecture including a user interface layer 100, an application services layer 110, a core processing engine 120, and a data storage layer 130. The layered architecture may provide separation of concerns between user interaction, application logic, processing operations, and data persistence.

[0071] The user interface layer 100 may include a user interface 101 that provides a primary interface for user interaction with the healthcare contract analysis system. The user interface 101 may receive user inputs related to contract management, encounter data submission, and report generation. The user interface 101 may connect to the application services layer 110 to transmit user requests and receive processing results.

[0072] With continued reference to FIG. 1, the application services layer 110 may include a contract management 111, an encounter management 112, and a reports and analytics 114. The contract management 111 may handle operations related to healthcare payer contract documents, including receiving contract uploads, managing contract versions, and coordinating contract processing workflows. The encounter management 112 may manage patient encounter data, including receiving encounter submissions, validating encounter data formats, and coordinating encounter processing operations. The reports and analytics 114 may generate outputs and analytical reports based on processing results from the core processing engine 120. Each of the contract management 111, the encounter management 112, and the reports and analytics 114 may receive input from the user interface 101 and communicate with components in the core processing engine 120.

[0073] The core processing engine 120 may include a contract parser 121, a rule generator 124, a rules engine 122, a payment calculator 123, an optimization engine 125, and an underpayment detection 126. The contract parser 121 may receive input from the contract management 111 and process healthcare payer contract documents to extract structured payment terms. In some aspects, the contract parser 121 may perform document preprocessing, semantic analysis, and term extraction operations on received contract documents. The contract parser 121 may connect to the rule generator 124, which may compile the extracted structured payment terms into machine-executable payment rules. Each machine-executable payment rule may comprise typed parameters, one or more applicability7conditions, and an execution priority.

[0074] As further shown in FIG. 1, the rules engine 122 may receive input from the encounter management 112 and apply compiled payment rules to patient encounter data according to execution priorities. The rules engine 122 may identify applicable payment rulesDocket No.: BLUE-01 -PCTbased on patient encounter characteristics and applicability conditions defined in the payment rules. The payment calculator 123 may perform reimbursement calculations based on the applied rules to generate expected reimbursement results. In some cases, the payment calculator 123 may support multiple healthcare payment methodologies including diagnosis-related group based payment, ambulatory7payment classification based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment.

[0075] The optimization engine 125 may receive input from the reports and analytics 114 and perform comparative analysis across multiple payer rule sets. In some aspects, the optimization engine 125 may compile payment rules from a plurality7of payer contracts, apply each set of payment rules to common encounter data, and compare expected reimbursement results across the plurality of payer contracts to generate payer routing recommendations. The underpayment detection 126 may compare expected reimbursement results to actual reimbursement amounts indicated in remittance data to identify payment variances. In some cases, the underpayment detection 126 may generate underpayment signals when variance between expected and actual reimbursement exceeds a threshold.

[0076] With continued reference to FIG. 1, the data storage layer 130 may include a contract repository 131, an encounter database 132, and a results store 133. The contract repository7131 may store healthcare payer contract documents and compiled payment rules, receiving data from the rule generator 124. In some aspects, the contract repository 131 may maintain a plurality of fee schedule versions with associated effective date ranges. The encounter database 132 may store patient encounter data and may receive input from the optimization engine 125. The results store 133 may store expected reimbursement results, variance analyses, audit trails, and other system outputs.

[0077] The healthcare contract analysis system may support multiple deployment configurations. In some aspects, the healthcare contract analysis system may be deployed as a software-as-a-service (SaaS) deployment in which the system components are hosted on cloud infrastructure and accessed by users through network connections. In some cases, the healthcare contract analysis system may be deployed as an on-premise deployment in which the system components are installed and operated within a healthcare provider's local computing environment. In some aspects, the healthcare contract analysis system may provide application programming interface (API) based integration with existing billing or revenue-cycle systems, enabling the healthcare contract analysis system to receive encounter data from and provide reimbursement results to external healthcare information systems. The healthcare contract analysis system may support retrospective audit workflows, real-time orDocket No.: BLUE-01 -PCTnear-real-time reimbursement analysis workflows, and batch processing across encounter populations. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0078] Contract Ingestion and Document Preprocessing

[0079] Referring to FIG. 2. a contract parsing workflow may begin with a Contract Upload 201 in which a healthcare payer contract document is received by the healthcare contract analysis system. The contract parsing workflow may then proceed to a Document Preprocessing 202 in which document preprocessing operations are performed on the uploaded contract document to prepare the contract document for subsequent analysis and term extraction.

[0080] In some aspects, a contract ingestion module may receive healthcare payer contract documents in one or more document formats. The contract ingestion module may accept healthcare payer contract documents in formats including Portable Document Format (PDF), Microsoft Word Document format (DOCX), text files, scanned images, and other document formats. The contract ingestion module may further receive related reimbursement materials including amendments, exhibits, fee schedules, rate tables, appendices, letters of agreement, addenda, and other supporting materials associated with the healthcare payer contract.

[0081] With continued reference to FIG. 2, the Document Preprocessing 202 may include LLM-enhanced optical character recognition (OCR) for scanned documents. In some cases, the LLM-enhanced OCR may incorporate healthcare-specific term recognition to improve accuracy when processing scanned contract materials containing healthcare payment terminology. The LLM-enhanced OCR may convert scanned images and image-based PDF documents into machine-readable text w hile preserving document structure information.

[0082] In some aspects, the Document Preprocessing 202 may include LLM-powcrcd structural analysis to identify sections, tables, and appendices within the healthcare payer contract document. The LLM-powered structural analysis may analyze the document layout and content organization to identify structural elements including section headings, paragraph boundaries, table structures, and appendix locations. The structural analysis may generate a document structure representation that maps the organization of the healthcare payer contract document.

[0083] The Document Preprocessing 202 may further include neural table extraction for complex fee schedules and payment rate tables. In some cases, the neural table extractionDocket No.: BLUE-01 -PCTmay process tabular data structures within the healthcare payer contract document to extract fee schedule information, payment rate tables, and other structured payment data. The neural table extraction may identify table boundaries, column headers, row labels, and cell values to generate structured representations of tabular payment information.

[0084] As further shown in FIG. 2, the Document Preprocessing 202 may include document segmentation based on semantic boundaries. In some aspects, the semantic boundary segmentation may divide the healthcare payer contract document into semantically coherent segments based on content meaning and topic transitions rather than relying solely on visual formatting cues. The semantic boundary segmentation may identify transitions between different payment topics, reimbursement methodologies, and contract provisions to generate document segments suitable for subsequent analysis.

[0085] In some cases, the Document Preprocessing 202 may include automated metadata extraction using specialized LLM prompts. The automated metadata extraction may extract contract identification information including contract numbers, effective dates, termination dates, contracting parties, and other administrative metadata from the healthcare payer contract document. The extracted metadata may be associated with the preprocessed document content for use in subsequent processing operations.

[0086] The contract parsing workflow7may proceed from the Document Preprocessing 202 to a Semantic Chunking 203 in which semantic chunking is applied to segment the preprocessed document based on semantic boundaries. The workflow may then continue to a Section Analysis 204 in which section analysis is performed to identify payment-relevant sections within the document. The identified payment-relevant sections may be routed to subsequent LLM Processing 205 and Term Extraction 206 operations for extraction of structured payment terms. Those of skill in the art will recognize other solutions w hich are intended to be w ithin the scope of the present disclosure.

[0087] Semantic Analysis and LLM Processing

[0088] Referring to FIG. 2. the contract parsing workflow may proceed from the Document Preprocessing 202 to a Semantic Chunking 203 in which semantic chunking is applied to segment the preprocessed healthcare payer contract document based on semantic boundaries. The Semantic Chunking 203 may divide the preprocessed document into semantically coherent segments based on content meaning and topic transitions. In some aspects, the Semantic Chunking 203 may identify transitions between different payment topics, reimbursement methodologies, and contract provisions to generate documentDocket No.: BLUE-01 -PCTsegments suitable for subsequent section analysis and term extraction operations.

[0089] With continued reference to FIG. 2, the contract parsing workflow may proceed from the Semantic Chunking 203 to a Section Analysis 204 in which section analysis is performed to identify payment-relevant sections within the segmented document. The Section Analysis 204 may analyze each semantic segment to determine whether the segment contains payment-related content suitable for term extraction. In some cases, the Section Analysis 204 may classify segments according to content type, including payment methodology sections, fee schedule sections, discount provision sections, carve-out sections, and administrative sections. The Section Analysis 204 may route identified payment-relevant sections to subsequent LLM Processing 205 operations while filtering non-payment sections from the extraction pipeline.

[0090] As further shown in FIG. 2, the contract parsing workflow may proceed from the Section Analysis 204 to an LLM Processing 205 in which large language model processing is applied to extract payment-related content from the identified payment-relevant sections. The LLM Processing 205 may employ a large language model configured with healthcare payment domain context to perform semantic analysis on the payment-relevant sections. In some aspects, the healthcare payment domain context may comprise one or more of a healthcare payment terminology dictionary', contract structure templates, payment methodology patterns, and payer-specific term definitions.

[0091] The healthcare payment terminology dictionary may provide the large language model with definitions and relationships among healthcare reimbursement terms, including terms related to diagnosis-related group based payment, ambulatory payment classification based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment. The contract structure templates may provide the large language model with representations of common healthcare payer contract organizational patterns, enabling the large language model to recognize and interpret standard contract structures. The payment methodology patterns may provide the large language model with representations of common payment calculation approaches and formula structures used in healthcare reimbursement. The payer-specific term definitions may provide the large language model with terminology conventions and language patterns associated with particular payer organizations.

[0092] In some cases, the LLM Processing 205 may employ multi-stage prompting with progressive refinement to extract payment-related content from the identified paymentrelevant sections. The multi-stage prompting may comprise an initial pass for sectionDocket No.: BLUE-01 -PCTclassification, targeted extraction for identified payment sections, and a verification pass to resolve ambiguities.

[0093] During the initial pass for section classification, the large language model may analyze each payment-relevant section to classify the section according to payment content type. The section classification may identify sections containing base payment terms, discount provisions, carve-out provisions, stop-loss provisions, outlier adjustment provisions, multiple procedure discount provisions, lesser-of provisions, and other payment-related content. The section classification may generate classification metadata that guides subsequent targeted extraction operations.

[0094] During the targeted extraction for identified payment sections, the large language model may apply extraction prompts tailored to the classified section type. In some aspects, the targeted extraction may employ extraction templates corresponding to different payment methodologies, including diagnosis-related group based payment extraction templates, ambulatory payment classification based payment extraction templates, fee schedule extraction templates, and per diem rate extraction templates. The targeted extraction may extract payment term data including base rates, multipliers, discount percentages, carve-out provisions, stop-loss thresholds, outlier provisions, effective date ranges, and applicability conditions from the classified payment sections.

[0095] During the verification pass to resolve ambiguities, the large language model may analyze extracted payment terms to identify ambiguous, incomplete, or inconsistent extractions. The verification pass may compare extracted terms against expected value ranges, cross-reference related terms for consistency, and flag extractions requiring human review. In some cases, the verification pass may generate clarification queries that prompt for additional information when extracted terms cannot be resolved with sufficient confidence. The multi-stage prompting with progressive refinement may improve extraction accuracy by applying iterative analysis and validation to the payment-relevant sections.

[0096] The LLM Processing 205 may generate structured payment term representations from the extracted payment-related content. The structured payment term representations may include payment methodology identifiers, reimbursement amounts, percentages, multipliers, carve-out specifications, threshold values, exclusion criteria, modifier specifications, effective dates, and related payment attributes. The structured payment term representations may be formatted for subsequent Term Extraction 206 and Validation & Review 207 operations in the contract parsing workflow. Those of skill in the art will recognize other solutions which are intended to be within the scope of the presentDocket No.: BLUE-01 -PCTdisclosure.

[0097] Term Extraction and Structured Payment Term Generation

[0098] Referring to FIG. 2, the contract parsing workflow may proceed from the LLM Processing 205 to a Term Extraction 206 in which payment term data is extracted from the identified payment-relevant content. The Term Extraction 206 may employ methodologyspecific extraction templates to extract structured payment terms from the processed payment-relevant sections. In some aspects, the methodology-specific extraction templates may include a diagnosis-related group (DRG) based payment extraction template, an ambulatory payment classification (APC) based payment extraction template, a fee schedule extraction template, and a per diem rate extraction template. Each methodology-specific extraction template may define extraction patterns, expected data fields, and validation criteria corresponding to the associated payment methodology.

[0099] With continued reference to FIG. 2, the Term Extraction 206 may extract contract identification information from the healthcare payer contract document. The contract identification information may include contract numbers, effective dates, termination dates, and contracting parties. The contract identification information may be associated with extracted payment terms to maintain traceability between structured payment terms and originating contract materials.

[0100] In some cases, the Term Extraction 206 may extract BlueCard-specific terms from healthcare payer contract documents associated with Blue Cross Blue Shield entities. The BlueCard-specific terms may include inter-plan arrangement provisions, multi-plan reimbursement routing parameters, and Blue Cross Blue Shield Association program terms. The extracted BlueCard-specific terms may be used in subsequent multi-payer comparison operations to evaluate reimbursement across multiple Blue plan contracts.

[0101] The Term Extraction 206 may implement a clause-first extraction architecture in which structured contract terms are extracted first and then deterministically compiled into executable rules. In the clause-first extraction architecture, the Term Extraction 206 may identify and extract individual payment clauses, provisions, and terms from the healthcare payer contract document as discrete structured elements. The extracted structured elements may then be passed to a rule compilation module for deterministic compilation into machine-executable payment rules. The clause-first extraction architecture may separate the extraction operation from the compilation operation, enabling validation and review of extracted terms prior to rule generation.Docket No.: BLUE-01 -PCT

[0102] As further shown in FIG. 2, the Term Extraction 206 may extract structured payment terms comprising one or more of base rates, multipliers, discount percentages, carve-out provisions, stop-loss thresholds, and outlier provisions. Base rates may define foundational reimbursement amounts for healthcare services under one or more payment methodologies. Multipliers may define scaling factors applied to base rates or other calculated amounts. Discount percentages may define percentage reductions applied to calculated reimbursement amounts. Carve-out provisions may define services subject to separate payment terms outside standard reimbursement calculations. Stop-loss thresholds may define limits on payment adjustments or provider liability. Outlier provisions may define payment adjustments for cases exceeding defined cost or length-of-stay thresholds.

[0103] In some aspects, the Term Extraction 206 may further extract payment methodology identifiers, reimbursement amounts, threshold values, exclusion criteria, modifier specifications, effective dates, and applicability conditions from the healthcare payer contract document. The extracted structured payment terms may be formatted in a normalized, machine-readable format suitable for subsequent validation, normalization, and compilation operations. The Term Extraction 206 may generate structured payment term representations that preserve the semantic content of the originating contract language while enabling programmatic processing and rule generation.

[0104] The contract parsing workflow may proceed from the Term Extraction 206 to a Validation & Review 207 in which validation and review operations are performed on the extracted structured payment terms. The validated structured payment terms may then proceed to a Rule Generation 208 in which the structured payment terms are compiled into machine-executable payment rules. The generated payment rules may be stored in a Rule Repository 209 for subsequent application to patient encounter data. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0105] Validation, Normalization, and Confidence Scoring

[0106] Referring to FIG. 2. the contract parsing workflow may proceed from the Term Extraction 206 to a Validation & Review 207 in which validation and review operations are performed on the extracted structured payment terms. The Validation & Review 207 may validate extracted structured payment terms against one or more validation criteria and normalize the extracted structured payment terms to a standardized format prior to compilation into machine-executable payment rules.Docket No.: BLUE-01 -PCT

[0107] In some aspects, the Validation & Review 207 may perform validation operations including historical pattern validation, industry range validation, and internal consistency checks. Historical pattern validation may compare extracted payment terms against historical contract data patterns to identify extractions that deviate from previously observed pay ment term values. Industry range validation may compare extracted pay ment terms against industry standard value ranges to identify extractions that fall outside expected ranges for healthcare reimbursement parameters. Internal consistency checks may analyze relationships among extracted payment terms to identify inconsistencies, contradictions, or logical conflicts within the extracted term set.

[0108] With continued reference to FIG. 2, the Validation & Review 207 may perform normalization operations on the extracted structured payment terms. The normalization operations may include standardizing terminology to an internal schema, converting textual amounts to decimal values, and standardizing percentage representations. Standardizing terminology to an internal schema may align extracted payment terminology with a defined internal vocabulary to enable consistent processing across healthcare payer contracts from different payer organizations. Converting textual amounts to decimal values may transform textual representations of payment amounts, such as written dollar amounts or formatted currency strings, into decimal numeric values suitable for programmatic calculation. Standardizing percentage representations may convert percentage values expressed in varying formats into a consistent numeric representation.

[0109] In some cases, the Validation & Review 207 may perform additional normalization operations including alignment of date formats, standardization of code references, and normalization of modifier specifications. The normalization operations may generate normalized structured payment term representations that preserve the semantic content of the extracted terms while enabling consistent programmatic processing and rule compilation.

[0110] As further shown in FIG. 2, the Validation & Review 207 may implement a multi-factor confidence scoring algorithm to determine confidence scores for extracted structured payment terms. The multi-factor confidence scoring algorithm may calculate confidence scores based on one or more of large language model certainty indicators, pattern matching confidence, presence of ambiguous language, and consistency with expected values. Large language model certainty indicators may reflect the degree of certainty reported by the large language model during extraction operations. Pattern matching confidence may reflect the degree to which extracted terms match expected extraction patterns for theDocket No.: BLUE-01 -PCTassociated payment methodology. Presence of ambiguous language may reflect whether the source contract text contains language that admits multiple interpretations. Consistency with expected values may reflect whether extracted values fall within expected ranges based on historical data and industry standards.

[0111] In some aspects, the multi-factor confidence scoring algorithm may combine the individual confidence factors to generate an aggregate confidence score for each extracted structured payment term. The aggregate confidence score may be expressed as a numerical value, a categorical classification, or other representation indicating the reliability of the extraction. The Validation & Review 207 may compare the aggregate confidence score to a confidence threshold to determine whether the extracted structured payment term requires human review.

[0112] The Validation & Review 207 may implement a human review workflow for extracted structured payment terms having confidence scores below the confidence threshold. The human review workflow may include automatic flagging of low-confidence extractions to route the low-confidence extractions to a review interface. The review interface may provide side-by-side presentation of source text and extracted terms, enabling a reviewer to compare the extracted structured payment term against the originating contract language. The review interface may further provide term validation controls that enable the reviewer to approve, modify, or reject extracted structured payment terms.

[0113] In some cases, the Validation & Review 207 may implement an extraction learning system that improves extraction accuracy based on human corrections. The extraction learning system may receive feedback from the human review workflow indicating corrections made by reviewers to extracted structured payment terms. The extraction learning system may analyze the corrections to identify patterns in extraction errors and may update extraction parameters, prompts, or models to reduce similar errors in subsequent extraction operations. The extraction learning system may enable continuous improvement of extraction accuracy through outcome feedback loops.

[0114] The contract parsing workflow may proceed from the Validation & Review 207 to a Rule Generation 208 in which the validated and normalized structured payment terms are compiled into machine-executable payment rules. The generated payment rules may be stored in a Rule Repository 209 for subsequent application to patient encounter data. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.Docket No.: BLUE-01 -PCT

[0115] Rule Compilation and Generation

[0116] Referring to FIG. 2. the contract parsing workflow may proceed from the Validation & Review 207 to a Rule Generation 208 in which validated and normalized structured payment terms are compiled into machine-executable payment rules. A rule compilation module may convert the validated and normalized structured payment terms into formal, machine-executable rule representations suitable for application to patient encounter data.

[0117] In some aspects, the rule compilation module may implement a rule definition layer comprising JSON schema-based rule definitions with strict typing. The JSON schema-based rule definitions may define data types, required fields, value constraints, and structural relationships for each machine-executable payment rule. The strict typing may enforce type consistency for payment amounts, percentages, multipliers, thresholds, and other payment variables, thereby reducing errors during subsequent rule execution operations.

[0118] With continued reference to FIG. 2, the rule compilation module may¬ employ an object-oriented representation of healthcare payment concepts. The object-oriented representation may define class hierarchies corresponding to healthcare reimbursement methodologies, payment rule types, applicability conditions, and related payment constructs. In some cases, the object-oriented representation may enable inheritance relationships among payment rule types, allowing specialized rule types to extend base rule definitions while preserving common attributes and behaviors.

[0119] Each machine-executable payment rule generated by the rule compilation module may comprise typed parameters, one or more applicability conditions, and an execution priority. The typed parameters may define payment amounts, percentages, multipliers, thresholds, schedules, or related payment variables associated with the payment rule. The applicability conditions may define predicates or constraints that determine when the payment rule applies to a given patient encounter. The execution priority may define an order of application relative to other payment rules during reimbursement calculation.

[0120] In some aspects, the rule compilation module may implement a rule versioning system for tracking rule changes over contract amendments. The rule versioning system may maintain version history for each machine-executable payment rule, recording changes to ty ped parameters, applicability conditions, and execution priorities over time. The rule versioning system may associate each rule version with corresponding contract amendment dates, enabling the healthcare contract analysis system to apply rule versions appropriate to specific encounter dates. In some cases, the rule versioning system mayDocket No.: BLUE-01 -PCTpreserve prior rule versions to support retrospective reimbursement analysis using historical rule configurations.

[0121] As further shown in FIG. 2, the rule compilation module may construct a rule dependency graph during compilation of the machine-executable payment rules. The rule dependency graph may represent relationships among payment rules, including dependencies between rules that reference common fee schedules, rules that modify outputs of other rules, and rules that share applicability conditions. In some aspects, the rule compilation module may analyze the rule dependency graph to determine execution priorities for the machineexecutable payment rules. The rule dependency graph may identify' rules that must be applied before other rules based on data dependencies, enabling the rule compilation module to assign execution priorities that preserve correct calculation sequencing.

[0122] The rule compilation module may further use the rule dependency graph for optimization of rule execution. In some cases, the rule dependency graph may identify independent rule branches that may be evaluated in parallel during reimbursement calculation. The rule dependency graph may also identify shared intermediate calculations that may be cached and reused across multiple rule evaluations.

[0123] In some aspects, the rule compilation module may implement fail-closed compilation and dependency handling. The fail-closed compilation may block compilation of a payment rule when the payment rule references an unresolved fee schedule, exhibit, or attachment. The rule compilation module may detect unresolved dependencies in the structured payment terms during compilation and may prevent generation of incomplete or invalid payment rules. In some cases, the rule compilation module may generate a notification indicating the unresolved dependency and may route the unresolved dependency to a review workflow for resolution. The fail-closed compilation may ensure that the healthcare contract analysis system does not apply payment rules having incomplete or missing reference data.

[0124] With continued reference to FIG. 2, the rule compilation module may implement facility scope canonicalization to convert facility' references in the structured payment terms into executable identifiers. The facility scope canonicalization may map facility names, facility codes, and facility descriptions appearing in healthcare payer contract documents to standardized facility' identifiers used by the healthcare contract analysis system. In some aspects, the facility scope canonicalization may resolve ambiguous facility¬ references by matching facility- attributes against a facility- registry maintained by the healthcare contract analysis system. In some cases, the rule compilation module may routeDocket No.: BLUE-01 -PCTunresolved facility references to a review workflow when the facility scope canonicalization cannot resolve the facility reference to a standardized facility identifier.

[0125] The rule compilation module may generate source linkage metadata for each machine-executable payment rule. The source linkage metadata may associate each payment rule with a corresponding source location in the healthcare payer contract document from which the payment rule was derived. In some aspects, the source linkage metadata may include document identifiers, page numbers, section references, and text excerpts sufficient to preserve traceability between the machine-executable payment rule and the originating contract language. The source linkage metadata may support audit trail generation and payment explanation operations in subsequent processing stages.

[0126] As further shown in FIG. 2, the generated machine-executable payment rules may be stored in a Rule Repository 209 for subsequent application to patient encounter data. The Rule Repository 209 may organize the machine-executable payment rules by contract, payer, rule type, and effective date range. In some aspects, the Rule Repository 209 may maintain indexes enabling efficient retrieval of applicable payment rules based on patient encounter characteristics. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0127] Rule Repository and Fee Schedule Version Management

[0128] Referring to FIG. 2. a Rule Repository 209 may store machine-executable payment rules generated by the Rule Generation 208. The Rule Repository 209 may organize the machine-executable payment rules by contract, payer, rule type, and effective date range. In some aspects, the Rule Repository 209 may maintain indexes enabling efficient retrieval of applicable payment rules based on patient encounter characteristics. The Rule Repository 209 may store rule metadata including source linkage information, version identifiers, and compilation timestamps for each machine-executable payment rule.

[0129] With continued reference to FIG. 2, the Rule Repository 209 may store a plurality of fee schedule versions with associated effective date ranges. Healthcare payer contracts may reference fee schedules that change over time due to annual updates, mid-year amendments, or contract renegotiations. The Rule Repository 209 may maintain multiple fee schedule versions corresponding to different effective date periods, enabling the healthcare contract analysis system to apply fee schedule rates appropriate to specific encounter dates.

[0130] In some aspects, the healthcare contract analysis system may implement validated fee schedule binding using contract-linked candidates and scope / date validation.Docket No.: BLUE-01 -PCTThe validated fee schedule binding may identify candidate fee schedules associated with a healthcare payer contract and validate each candidate fee schedule against scope criteria and date criteria. The scope criteria may include facility scope, service type scope, and payer plan scope. The date criteria may include effective date ranges and termination dates. The validated fee schedule binding may bind a machine-executable payment rule to a validated fee schedule that satisfies both the scope criteria and the date criteria for the associated healthcare payer contract.

[0131] As further shown in FIG. 2, the healthcare contract analysis system may implement encounter-date-aware schedule version selection across multiple related fee schedule versions. When the healthcare contract analysis system receives patient encounter data, the healthcare contract analysis system may determine an encounter date from the patient encounter data. The healthcare contract analysis system may then select an applicable fee schedule version from the plurality of fee schedule versions stored in the Rule Repository 209 based on the encounter date falling within an associated effective date range. In some cases, the encounter-date-aware schedule version selection may evaluate multiple fee schedule versions having overlapping or adjacent effective date ranges to identify the fee schedule version applicable to the specific encounter date.

[0132] Referring to FIG. 1, the contract repository' 131 in the data storage layer 130 may store healthcare payer contract documents and compiled payment rules. The contract repository 131 may maintain associations between fee schedule versions and originating contract materials, preserving traceability between fee schedule data and source contract documents. In some aspects, the contract repository' 131 may store fee schedule version metadata including version identifiers, effective start dates, effective end dates, and amendment references.

[0133] With continued reference to FIG. 1, the rule generator 124 in the core processing engine 120 may implement a reconciliation process for updated extracted payment terms. When the healthcare contract analysis system receives an updated extraction of structured payment terms from a healthcare payer contract document, the rule generator 124 may compare the updated extraction to previously compiled payment rules stored in the Rule Repository 209. The reconciliation process may identify differences between the updated extraction and the previously compiled payment rules, including new terms, modified terms, and removed terms.

[0134] In some aspects, the reconciliation process may implement deterministic reextraction reconciliation that preserves manually tuned rule state where appropriate. TheDocket No.: BLUE-01 -PCTreconciliation process may identify manually adjusted parameters in the previously compiled payment rules. Manually adjusted parameters may include parameters that were modified by a user after initial compilation, parameters that were validated through human review workflows, and parameters that were flagged as user-confirmed values. The reconciliation process may preserve the manually adjusted parameters in the previously compiled payment rules when reconciling with the updated extraction.

[0135] The reconciliation process may update the previously compiled payment rules with non-conflicting terms from the updated extraction. Non-conflicting terms may include new terms not present in the previously compiled payment rules and updated terms that do not conflict with manually adjusted parameters. In some cases, the reconciliation process may generate conflict notifications when the updated extraction contains terms that conflict with manually adjusted parameters, enabling a user to review and resolve the conflicts through a review interface.

[0136] As further shown in FIG. 1, the rules engine 122 may retrieve applicable payment rules from the Rule Repository 209 based on patient encounter characteristics and fee schedule version selection. The rules engine 122 may apply the retrieved payment rules to patient encounter data according to execution priorities, using fee schedule rates from the selected fee schedule version. The payment calculator 123 may perform reimbursement calculations using the fee schedule rates corresponding to the encounter date, thereby- generating expected reimbursement results that reflect the fee schedule terms in effect at the time of the patient encounter. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0137] Rule Types and Payment Methodologies

[0138] Referring to FIG. 3, the set of machine-executable payment rules stored in the Rule Repository 209 may comprise one or more rule types corresponding to different healthcare reimbursement provisions. The rule ty pes may include base payment rules, discount rules, carve-out rules, stop-loss rules, outlier adjustment rules, multiple procedure discount rules, and lesser-of rules. Each rule type may define a category of payment logic that addresses a particular aspect of healthcare reimbursement calculation.

[0139] In some aspects, base payment rules may define foundational reimbursement calculations using one or more payment methodologies. Base payment rules may establish the primary reimbursement amount for a patient encounter before application of adjustments, discounts, or other modifying rules. The base payment rules may implement paymentDocket No.: BLUE-01 -PCTmethodologies including diagnosis-related group (DRG) based payment, ambulatory payment classification (APC) based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment.

[0140] With continued reference to FIG. 3, discount rules may define percentage reductions or fixed amount reductions applied to calculated reimbursement amounts.Discount rules may modify base payment calculations according to contractual discount provisions negotiated between healthcare providers and payer organizations. In some cases, discount rules may apply percentage discounts to specific service categories, procedure types, or facility locations as defined in the healthcare payer contract document.

[0141] Carve-out rules may identify services subject to separate payment terms outside standard reimbursement calculations. Carve-out rules may define services, procedures, or supply categories that are excluded from bundled payment arrangements and reimbursed according to distinct payment provisions. In some aspects, carve-out rules may specify separate fee schedules, percentage-of-charge calculations, or fixed payment amounts for carved-out services.

[0142] As further shown in FIG. 3, stop-loss rules may define limits on payment adjustments or provider liability. Stop-loss rules may establish threshold amounts beyond which payment adjustments cease to apply or beyond which alternative payment provisions take effect. In some cases, stop-loss rules may protect healthcare providers from excessive financial exposure on high-cost cases by capping provider liability at defined threshold amounts.

[0143] Outlier adjustment rules may define payment adjustments for cases exceeding defined cost thresholds or length-of-stay thresholds. Outlier adjustment rules may provide additional reimbursement for patient encounters that exceed expected resource utilization based on diagnosis, procedure, or other clinical characteristics. In some aspects, outlier adjustment rules may calculate additional payments based on costs exceeding a fixed loss threshold or a percentage of the base payment amount.

[0144] Multiple procedure discount rules may define adjustments to reimbursement when multiple procedures are performed during a same patient encounter. Multiple procedure discount rules may apply percentage reductions to secondary and subsequent procedures based on the premise that certain fixed costs are shared across procedures performed during a single encounter. In some cases, multiple procedure discount rules may apply graduated discount percentages, with higher discounts applied to procedures ranked lower in payment amount.Docket No.: BLUE-01 -PCT

[0145] Referring to FIG. 3, lesser-of rules may select an appropriate payment amount based on comparative calculations defined in the healthcare payer contract document. Lesser-of rules may compare two or more calculated payment amounts and select the lesser amount as the reimbursement result. In some aspects, lesser-of rules may compare a calculated contract payment amount to billed charges and select the lesser of the two amounts as the final reimbursement.

[0146] The base payment rules may implement one or more healthcare payment methodologies to calculate foundational reimbursement amounts. Diagnosis-related group (DRG) based payment may calculate reimbursement based on a patient's diagnosis, procedures performed, and other clinical factors that classify the patient encounter into a payment category. DRG-based payment may assign a base rate multiplied by a relative weight corresponding to the assigned diagnosis-related group. In some cases, DRG-based payment may include adjustments for geographic factors, teaching status, disproportionate share, and other facility characteristics.

[0147] With continued reference to FIG. 3, ambulatory payment classification (APC) based payment may calculate reimbursement for outpatient services based on procedure codes and service classifications. APC-based payment may group outpatient procedures into payment categories with assigned payment rates. In some aspects, APC-based payment may apply packaging rules that bundle certain ancillary services into the primary procedure payment and may apply status indicators that determine payment treatment for individual service codes.

[0148] Fee schedule based payment may calculate reimbursement by applying defined payment amounts to specific procedure codes, service codes, or supply codes. Fee schedule based payment may reference a fee schedule table that associates healthcare service codes with corresponding payment amounts. In some cases, fee schedule based payment may apply geographic adjustments, modifier-based adjustments, or facility-specific rate variations to the base fee schedule amounts.

[0149] Per diem payment may calculate reimbursement based on a daily rate multiplied by the number of days of service. Per diem payment may apply to inpatient stays, skilled nursing facility services, or other services measured in daily increments. In some aspects, per diem payment may apply different daily rates for different service levels, acuity categories, or phases of treatment.

[0150] As further shown in FIG. 3, percentage-of-charge payment may calculate reimbursement as a percentage of billed charges submitted by the healthcare provider.Docket No.: BLUE-01 -PCTPercentage-of-charge payment may apply a contractual discount percentage to the total billed charges to determine the reimbursement amount. In some cases, percentage-of-charge payment may apply different discount percentages to different service categories, revenue codes, or charge ranges.

[0151] The rule compilation module may assign each machine-executable payment rule to a rule category during compilation. The rule categories may include DRG-based reimbursement, APC-based reimbursement, fee schedule reimbursement, per diem reimbursement, percentage-of-charge reimbursement, carve-out rules, outlier adjustments, stop-loss rules, lesser-of rules, and multiple procedure adjustments. In some aspects, the rule category assignment may determine the typed parameters, applicability conditions, and execution priority associated with each machine-executable payment rule.

[0152] Referring to FIG. 1, the payment calculator 123 in the core processing engine 120 may implement calculation logic corresponding to each rule category. The pay ment calculator 123 may support DRG-based reimbursement calculations including base rate application, relative weight multiplication, and policy adjustments. The payment calculator 123 may support APC-based reimbursement calculations including status indicator evaluation, packaging determination, and composite APC handling. In some cases, the payment calculator 123 may support fee schedule reimbursement calculations including code-to-rate lookup, modifier application, and geographic adjustment.

[0153] With continued reference to FIG. 1, the payment calculator 123 may support per diem reimbursement calculations including daily rate application, length-of-stay determination, and tiered rate handling. The payment calculator 123 may support percentage-of-charge reimbursement calculations including charge aggregation, discount percentage application, and charge category differentiation. In some aspects, the payment calculator 123 may support can e-out rule calculations, outlier adjustment calculations, stop-loss rule calculations, lesser-of rule calculations, and multiple procedure adjustment calculations as defined by the compiled machine-executable payment rules.

[0154] The healthcare contract analysis system may support contract-specific variants of the rule categories and payment methodologies. In some cases, healthcare payer contracts may define hybrid payment methodologies that combine elements of multiple base payment approaches. The rule compilation module may generate machine-executable payment rules that implement contract-specific payment logic while preserving the rule category classification for execution priority determination and audit trail generation. Those of skill in the art will recognize other solutions which are intended to be within the scope ofDocket No.: BLUE-01 -PCTthe present disclosure.

[0155] Applicability Conditions and Patient Encounter Data

[0156] Referring to FIG. 3, each machine-executable payment rule stored in the Rule Repository7209 may comprise one or more applicability conditions that define when the payment rule applies to a given patient encounter. The applicability conditions may comprise predicates, constraints, or criteria that the healthcare contract analysis system evaluates against patient encounter data to determine whether a payment rule is applicable to a specific patient encounter. The rules engine 122 may evaluate the applicability conditions for each payment rule against received patient encounter data to identify applicable payment rules for reimbursement calculation.

[0157] In some aspects, the applicability conditions may comprise service type constraints that define categories of healthcare services to which a payment rule applies. Service type constraints may specify inpatient services, outpatient services, emergency¬ services, ambulatory surgical services, skilled nursing services, or other service classifications. The rules engine 122 may compare service type indicators in the patient encounter data against service type constraints in the applicability conditions to determine payment rule applicability-.

[0158] With continued reference to FIG. 3, the applicability conditions may comprise diagnosis code requirements that define diagnosis classifications to which a payment rule applies. Diagnosis code requirements may specify7International Classification of Diseases (ICD) codes, diagnosis-related group (DRG) assignments, or diagnosis categories that must be present in the patient encounter data for the payment rule to apply. In some cases, diagnosis code requirements may specify inclusion criteria identifying diagnoses that trigger rule application, exclusion criteria identifying diagnoses that prevent rule application, or combinations of inclusion and exclusion criteria.

[0159] The applicability conditions may further comprise procedure code applicability that defines procedures to which a payment rule applies. Procedure code applicability may specify Current Procedural Terminology (CPT) codes, Healthcare Common Procedure Coding System (HCPCS) codes, or procedure categories that must be present in the patient encounter data for the payment rule to apply. In some aspects, procedure code applicability may define primary procedure requirements, secondary procedure requirements, or modifier-based conditions that affect payment rule application.

[0160] As further shown in FIG. 3, the applicability conditions may comprise dateDocket No.: BLUE-01 -PCTrange limitations that define temporal boundaries for payment rule application. Date range limitations may specify effective start dates, effective end dates, or date ranges during which a payment rule applies to patient encounters. The rules engine 122 may compare service dates in the patient encounter data against date range limitations in the applicability conditions to determine whether a payment rule is temporally applicable to a specific patient encounter.

[0161] In some cases, the applicability conditions may comprise patient type restrictions that define patient classifications to which a payment rule applies. Patient type restrictions may specify Medicare patients, Medicaid patients, commercial insurance patients, self-pay patients, pediatric patients, adult patients, or other patient classifications. The rules engine 122 may evaluate patient demographic information and payer information in the patient encounter data against patient type restrictions to determine payment rule applicability.

[0162] The applicability conditions may further comprise facility-specific terms that define healthcare facilities to which a payment rule applies. Facilit -specific terms may specify facility identifiers, facility types, facility locations, or facility characteristics that must match the patient encounter data for the payment rule to apply. In some aspects, facilityspecific terms may' define rules applicable to specific hospital campuses, outpatient clinics, ambulatory' surgery' centers, or other healthcare facility' ty pes.

[0163] Referring to FIG. 1. the applicability conditions may further comprise payer or plan characteristics, product line specifications, and billing form or code-based conditions. Payer or plan characteristics may specify insurance plan types, payer organizations, or benefit plan categories to which a payment rule applies. Product line specifications may define service lines or clinical programs to which a payment rule applies. Billing form or code-based conditions may specify billing form types, revenue codes, or other coding-based criteria that affect payment rule applicability.

[0164] With continued reference to FIG. 1, the encounter management 112 may receive patient encounter data from external healthcare information systems or through the user interface 101. Patient encounter data may comprise information associated with a healthcare service event that the healthcare contract analysis system uses to identify applicable payment rules and calculate expected reimbursement. The encounter database 132 may store received patient encounter data for processing by the rules engine 122 and the payment calculator 123.

[0165] In some aspects, patient encounter data may comprise patient demographic information including patient identifiers, patient age, patient gender, patient address, andDocket No.: BLUE-01 -PCTpatient insurance information. Patient demographic information may be used to evaluate patient type restrictions and payer or plan characteristics in the applicability conditions of machine-executable payment rules.

[0166] As further shown in FIG. 1, patient encounter data may comprise diagnosis codes including International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for diagnoses and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS) codes for inpatient procedures. The diagnosis codes may identify the medical conditions and clinical circumstances associated with the patient encounter. The rules engine 122 may evaluate diagnosis codes against diagnosis code requirements in the applicability conditions to identify applicable payment rules.

[0167] Patient encounter data may further comprise procedure codes including Current Procedural Terminology7(CPT) codes and Healthcare Common Procedure Coding System (HCPCS) codes. Procedure codes may identify the medical procedures, services, and supplies provided during the patient encounter. In some cases, procedure codes may include modifier codes that provide additional information about the circumstances of service delivery. The rules engine 122 may evaluate procedure codes and modifier codes against procedure code applicability7in the applicability7conditions to identify applicable payment rules.

[0168] Referring to FIG. 3. patient encounter data may comprise service dates including admission dates, discharge dates, service dates, and date ranges associated with the patient encounter. Service dates may be used to evaluate date range limitations in the applicability conditions and to select applicable fee schedule versions from the plurality7of fee schedule versions stored in the Rule Repository 209. In some aspects, the healthcare contract analysis system may determine an encounter date from the service dates and use the encounter date for encounter-date-aware schedule version selection.

[0169] Patient encounter data may further comprise facility identifiers including facility codes, facility names, National Provider Identifier (NPI) numbers, and facility location information. Facility identifiers may be used to evaluate facility-specific terms in the applicability conditions and to apply facility7scope canonicalization during rule matching operations. In some cases, the rules engine 122 may map facility7identifiers in the patient encounter data to standardized facility identifiers used by the healthcare contract analysis system.

[0170] With continued reference to FIG. 3, patient encounter data may compriseDocket No.: BLUE-01 -PCTbilling form type indicators that identify the claim form format associated w ith the patient encounter. Billing form type indicators may specify UB-04 (CMS- 1450) forms for institutional claims or CMS-1500 forms for professional claims. The billing form type may affect which payment rules apply to the patient encounter and may influence the payment methodology used for reimbursement calculation.

[0171] Patient encounter data may further comprise charge amounts including lineitem charges, total charges, and charge breakdowns by revenue code or service category. Charge amounts may be used in percentage-of-charge payment calculations, lesser-of rule evaluations, and other reimbursement calculations that reference billed charges. In some aspects, patient encounter data may include length of stay, discharge status, and related clinical or administrative data elements used in reimbursement calculations.

[0172] Referring to FIG. 1 , the encounter management 112 may perform encounter data normalization to convert received patient encounter data to standardized formats suitable for processing by the rules engine 122. Encounter data normalization may include standardization of code formats, alignment of date representations, conversion of charge amounts to decimal values, and mapping of facility identifiers to standardized identifiers. The encounter data normalization may generate normalized encounter data representations that enable consistent rule matching and reimbursement calculation across patient encounters received from different source systems.

[0173] In some aspects, the healthcare contract analysis system may implement encounter-type invariants that define constraints on patient encounter data that must be satisfied before rule execution proceeds. Encounter-type invariants may include requirements for presence of diagnosis codes, procedure codes, service dates, or other data elements in the patient encounter data. The rules engine 122 may evaluate encounter-type invariants as execution gating conditions that prevent rule execution when patient encounter data does not satisfy the defined constraints. In some cases, the rules engine 122 may generate notifications indicating missing or invalid data elements when encounter-type invariants are not satisfied.

[0174] As further shown in FIG. 1, the healthcare contract analysis system may implement per-line-item pricing visibility that maintains visibility into pricing calculations at the individual line-item level within a patient encounter. Per-line-item pricing visibility may track reimbursement calculations for each procedure code, revenue code, or service line within the patient encounter data. In some aspects, per-line-item pricing visibility' may enable detailed audit trail generation and payment explanation at the line-item level.

[0175] The healthcare contract analysis system may further implement fallbackDocket No.: BLUE-01 -PCTsuppression to avoid double-counting during reimbursement calculation. Fallback suppression may prevent application of multiple payment rules to the same line item or service when the payment rules would result in duplicative reimbursement. In some cases, fallback suppression may identify when a primary payment rule has been applied to a line item and suppress application of fallback payment rules that would otherwise apply to the same line item. The fallback suppression may preserve calculation accuracy by preventing over-counting of reimbursement amounts across overlapping payment rule applications. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0176] Rules Engine Processing and Rule Matching

[0177] Referring to FIG. 3, a rules engine processing flow- may begin with a Patient Encounter 301 in which patient encounter data is received by the healthcare contract analysis system. The rules engine processing flow may then proceed to a Rule Matching & Priority Determination 302 in which the healthcare contract analysis system identifies applicable payment rules from the Rule Repository 209 and determines the order in which the applicable payment rules will be applied based on execution priorities.

[0178] In some aspects, a rules engine 122 may receive patient encounter data from the encounter management 112 and identify applicable payment rules based on the patient encounter data and applicability conditions defined in the machine-executable payment rules stored in the Rule Repository 209. The rules engine 122 may evaluate applicability conditions for each machine-executable payment rule against the received patient encounter data to determine whether the payment rule applies to the specific patient encounter. The applicability conditions may include service type constraints, diagnosis code requirements, procedure code applicability, date range limitations, patient type restrictions, and facilityspecific terms.

[0179] With continued reference to FIG. 1, the rules engine 122 may implement inmemory rule evaluation for high-performance processing. In-memory rule evaluation may load applicable payment rules into system memory for rapid evaluation against patient encounter data. In some cases, the in-memory rule evaluation may maintain cached rule representations that enable repeated rule evaluations without requiring retrieval from persistent storage for each evaluation operation. The in-memory rule evaluation may support high-throughput processing of patient encounter populations by reducing latency associated with rule retrieval and loading operations.Docket No.: BLUE-01 -PCT

[0180] The rules engine 122 may implement dynamic rule binding based on encounter characteristics. Dynamic rule binding may associate patient encounter data with applicable payment rules at runtime based on the encounter characteristics and the applicability conditions defined in the machine-executable payment rules. In some aspects, dynamic rule binding may evaluate encounter attributes including service type, diagnosis codes, procedure codes, service dates, facility identifiers, and payer information to identify payment rules having applicability conditions satisfied by the encounter attributes. Dynamic rule binding may enable the rules engine 122 to apply contract-specific payment logic to patient encounters without requiring pre-configuration of encounter-to-rule associations.

[0181] As further shown in FIG. 1, the rules engine 122 may implement parallel evaluation of independent rule branches. Parallel evaluation may identify payment rules that do not have dependencies on the outputs of other payment rules and may evaluate the independent payment rules concurrently. In some cases, the rules engine 122 may analyze the rule dependency graph constructed during rule compilation to identify independent rule branches suitable for parallel evaluation. Parallel evaluation of independent rule branches may reduce total processing time for patient encounters subject to multiple payment rules by executing non-dependent rule evaluations simultaneously.

[0182] The rules engine 122 may implement a calculation caching mechanism for repeated calculations. The calculation caching mechanism may store intermediate calculation results generated during rule evaluation and reuse the stored intermediate results when the same calculations are required for subsequent rule evaluations. In some aspects, the calculation caching mechanism may cache fee schedule lookups, base rate calculations, and other intermediate values that are referenced by multiple payment rules during reimbursement calculation for a patient encounter. The calculation caching mechanism may also cache calculation results across patient encounters when the patient encounters share common calculation parameters, such as common procedure codes or common fee schedule references.

[0183] With continued reference to FIG. 3, the Rule Matching & Priority Determination 302 may determine execution priorities for the identified applicable payment rules. The execution priorities may govern the sequence in which the applicable payment rules are applied during subsequent rule execution operations. In some cases, the rules engine 122 may retrieve execution priority metadata associated with each applicable payment rule and order the applicable payment rules according to the execution priority metadata. The rules engine 122 may further evaluate rule dependencies identified in the rule dependencyDocket No.: BLUE-01 -PCTgraph to verify that the execution priority ordering preserves correct calculation sequencing.

[0184] The rules engine 122 may proceed from the Rule Matching & Priority Determination 302 to a Rule Execution 310 in which the applicable payment rules are applied to the patient encounter data according to the determined execution priorities. The Rule Execution 310 may apply payment rules in a hierarchical sequence to generate expected reimbursement results for the patient encounter. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0185] Hierarchical Rule Execution and Priority System

[0186] Referring to FIG. 3. following the Rule Matching & Priority Determination 302, the rules engine processing flow may proceed to a Rule Execution 310 in which applicable payment rules are applied to patient encounter data according to determined execution priorities. The Rule Execution 310 may implement an adaptive multi-tier priority system that governs the sequence in which different rule types are applied during reimbursement calculation. The adaptive multi-tier priority system may support dynamic hierarchical rule application with configurable precedence to accommodate variations in healthcare payer contract structures.

[0187] In some aspects, the Rule Execution 310 may apply payment rules in a hierarchical sequence comprising multiple rule tiers. The hierarchical sequence may begin with Carve-Out Rules 311 at a highest priority level. Carve-Out Rules 311 may identify services subject to separate payment terms outside standard reimbursement calculations. The healthcare contract analysis system may apply Carve-Out Rules 311 before other rule types to identify' carved-out services that are excluded from bundled payment arrangements and reimbursed according to distinct payment provisions.

[0188] With continued reference to FIG. 3, the hierarchical sequence may proceed from Carve-Out Rules 311 to Base Payment Rules 312 at a second priority level lower than the first priority' level. Base Payment Rules 312 may establish foundational reimbursement calculations using one or more payment methodologies including diagnosis-related group based payment, ambulatory payment classification based payment, fee schedule based payment, per diem payment, and percentage-of-charge payment. The healthcare contract analysis system may apply Base Payment Rules 312 to calculate primary reimbursement amounts for patient encounters after carved-out services have been identified.

[0189] The hierarchical sequence may proceed from Base Payment Rules 312 to Discount Rules 313 at a third priority level lower than the second priority level. DiscountDocketNo.: BLUE-01 -PCTRules 313 may apply contractual discounts to modify calculated reimbursement amounts. In some cases, Discount Rules 313 may apply percentage reductions or fixed amount reductions to base payment calculations according to discount provisions defined in the healthcare payer contract document.

[0190] As further shown in FIG. 3, the hierarchical sequence may proceed from Discount Rules 313 to Multiple Procedure Rules 314 at a fourth priority level lower than the third priority level. Multiple Procedure Rules 314 may adjust reimbursement for multiple procedures performed during a same patient encounter. In some aspects, Multiple Procedure Rules 314 may apply graduated discount percentages to secondary and subsequent procedures based on the premise that certain fixed costs are shared across procedures performed during a single encounter.

[0191] The hierarchical sequence may proceed from Multiple Procedure Rules 314 to Stop Loss Rules 315 at a fifth priority level lower than the fourth priority level. Stop Loss Rules 315 may limit payment adjustments according to contract provisions. Stop Loss Rules 315 may establish threshold amounts beyond which payment adjustments cease to apply or beyond which alternative payment provisions take effect.

[0192] With continued reference to FIG. 3, the hierarchical sequence may conclude with Lesser-Of Rules 316 at a sixth priority level, which may be a lowest priority level in the hierarchical sequence. Lesser-Of Rules 316 may select appropriate payment amounts based on comparative calculations defined in the healthcare payer contract document. In some cases, Lesser-Of Rules 316 may compare a calculated contract payment amount to billed charges and select the lesser of the two amounts as the final reimbursement. Following application of Lesser-Of Rules 316, the Rule Execution 310 may generate a Payment Result 320 comprising the expected reimbursement result for the patient encounter.

[0193] In some aspects, the healthcare contract analysis system may implement contract-specific priority detection using large language model analysis of explicit sequencing terms in the healthcare payer contract document. The large language model may analyze contract language to identify’ explicit sequencing terms that indicate the order in which payment provisions should be applied. Explicit sequencing terms may include language such as "first," "then," "before," "after," "prior to," "following," "subsequently," and related temporal or sequential indicators. The large language model may extract priority relationships from the explicit sequencing terms and generate priority metadata for use during rule compilation.

[0194] The healthcare contract analysis system may further implement payer-Docket No.: BLUE-01 -PCTspecific customizable rule priority templates. Payer-specific customizable rule priority templates may define default priority orderings for payment rules associated with particular payer organizations. In some cases, the payer-specific customizable rule priority templates may reflect established payment processing conventions used by specific payer organizations. The healthcare contract analysis system may apply payer-specific customizable rule priority templates when compiling payment rules from healthcare payer contract documents associated with the corresponding payer organizations.

[0195] Referring to FIG. 1, the rule generator 124 may implement a learning system that identifies implicit rule precedence from contract language. The learning system may analyze patterns in healthcare payer contract documents to identify implicit priority relationships that are not expressed through explicit sequencing terms. In some aspects, the learning system may identify implicit rule precedence based on structural relationships within the contract document, contextual associations between payment provisions, and historical patterns observed across multiple healthcare payer contracts from similar payer organizations. The learning system may generate inferred priority metadata based on the identified implicit rule precedence.

[0196] In some cases, the healthcare contract analysis system may implement priority' override capabilities for special contract terms. Priority' override capabilities may enable modification of default priority orderings when healthcare payer contract documents contain provisions that require non-standard rule application sequences. The priority override capabilities may allow a user to specify custom priority orderings through a review- interface, or may allow' the rule compilation module to detect and apply priority' overrides based on contract language indicating exceptions to standard priority hierarchies.

[0197] With continued reference to FIG. 1, the rules engine 122 may implement conflict resolution algorithms using both explicit contract language and industry standard practices. The conflict resolution algorithms may resolve situations in which multiple payment rules have conflicting applicability conditions, overlapping scope, or contradictory' payment calculations. In some aspects, the conflict resolution algorithms may first evaluate explicit contract language to determine whether the healthcare payer contract document specifies how conflicts should be resolved. When explicit contract language does not address a conflict, the conflict resolution algorithms may apply industry' standard practices to determine which payment rule takes precedence.

[0198] The healthcare contract analysis system may further implement rule interaction modeling to handle complex dependencies among payment rules. Rule interactionDocket No.: BLUE-01 -PCTmodeling may analyze relationships among payment rules to identify dependencies in which the output of one payment rule serves as an input to another payment rule. In some cases, rule interaction modeling may identify circular dependencies, cascading rule effects, and conditional rule activations that affect the sequence and outcome of rule application. The rule interaction modeling may generate dependency metadata that the rules engine 122 uses to coordinate rule execution and preserve correct calculation sequencing.

[0199] As further shown in FIG. 1, the healthcare contract analysis system may implement automated precedence testing to validate rule application sequence. Automated precedence testing may execute test calculations using defined test encounter data to verify that payment rules are applied in the correct sequence according to the determined execution priorities. In some aspects, automated precedence testing may compare test calculation results against expected results to identify’ discrepancies that indicate incorrect rule ordering.Automated precedence testing may generate validation reports indicating whether the rule application sequence produces expected outcomes for the test encounter data.

[0200] In some cases, automated precedence testing may be performed during rule compilation to validate newly compiled payment rules before the payment rules are stored in the Rule Repository 209. Automated precedence testing may also be performed when priority overrides are applied or when conflict resolution algorithms modify rule execution sequences. The automated precedence testing may provide verification that the hierarchical rule execution sequence produces accurate reimbursement calculations consistent with the healthcare payer contract document. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0201] Payment Calculation Engine

[0202] Referring to FIG. 3, following the Rule Execution 310, a payment calculator 123 may generate a Payment Result 320 comprising an expected reimbursement result for a patient encounter. The payment calculator 123 may apply applicable payment rules to patient encounter data according to execution priorities determined during the Rule Matching & Priority Determination 302. In some aspects, the payment calculator 123 may implement a calculation engine configured to perform reimbursement calculations based on the applied payment rules and generate expected reimbursement outputs together with supporting audit and explanation data.

[0203] With continued reference to FIG. 3, the payment calculator 123 may implement transactional rule application to ensure consistency during reimbursementDocket No.: BLUE-01 -PCTcalculation. Transactional rule application may group related rule evaluation and calculation operations into atomic transactions that either complete successfully or roll back to a prior state. In some cases, transactional rule application may prevent partial calculation results from persisting when errors occur during rule execution, thereby maintaining data integrity across the reimbursement calculation process.

[0204] The payment calculator 123 may implement staged rule execution with intermediate result validation. Staged rule execution may divide the reimbursement calculation process into discrete stages corresponding to the hierarchical rule sequence, including Can e-Out Rules 311, Base Payment Rules 312, Discount Rules 313, Multiple Procedure Rules 314, Stop Loss Rules 315, and Lesser-Of Rules 316. In some aspects, the payment calculator 123 may validate intermediate results generated at each stage before proceeding to subsequent stages. Intermediate result validation may include range checks, consistency verification, and comparison against expected value boundaries for the associated rule ty pe.

[0205] Referring to FIG. 1. the payment calculator 123 may implement condition evaluation optimized for healthcare data patterns. Condition evaluation may analyze applicability conditions defined in the machine-executable payment rules against patient encounter data to determine rule applicability. In some cases, the condition evaluation may employ indexing structures and evaluation sequences tailored to common healthcare data patterns, including diagnosis code hierarchies, procedure code groupings, and service type classifications. The optimized condition evaluation may reduce processing time when evaluating applicability conditions across large patient encounter populations.

[0206] With continued reference to FIG. 1, the payment calculator 123 may implement execution path tracking for payment explanation generation. Execution path tracking may record each payment rule applied to a patient encounter, the sequence in which the payment rules were applied, and the intermediate and final calculation results produced by each payment rule. In some aspects, the execution path tracking may generate execution path records that document the complete calculation sequence from patient encounter data input to expected reimbursement output. The execution path records may be stored in the results store 133 for subsequent retrieval and analysis.

[0207] The payment calculator 123 may generate audit trails with rule-by-rule impact analysis. The audit trails may link each applied payment rule to a corresponding source location in the healthcare payer contract document from which the payment rule was derived. In some cases, the audit trails may include rule-by-rule impact analysis thatDocket No.: BLUE-01 -PCTquantifies the contribution of each applied payment rule to the final expected reimbursement result. The rule-by-rule impact analysis may identify the payment amount attributable to each rule application, enabling detailed explanation of how the expected reimbursement result was calculated from the patient encounter data and the applicable payment rules.

[0208] As further shown in FIG. 1, the payment calculator 123 may implement deterministic calculation sequencing for reproducibility. Deterministic calculation sequencing may ensure that the same patient encounter data processed against the same set of machineexecutable payment rules produces identical expected reimbursement results across multiple calculation executions. In some aspects, deterministic calculation sequencing may control the order of rule evaluation, the handling of floating-point operations, and the resolution of tie-breaking conditions to eliminate sources of non-determinism in the calculation process. Deterministic calculation sequencing may support audit requirements and enable verification of calculation results through repeated execution.

[0209] Referring to FIG. 3, the payment calculator 123 may include a financial calculation subsystem configured to perform arithmetic operations for reimbursement calculation. The financial calculation subsystem may implement decimal-based arithmetic with configurable precision. Decimal-based arithmetic may use fixed-point or arbitrary-precision decimal representations rather than binary' floating-point representations to avoid rounding errors in financial calculations. In some cases, the configurable precision may allow specification of decimal places maintained during intermediate calculations and in final reimbursement results.

[0210] With continued reference to FIG. 3, the financial calculation subsystem may implement payer-specific rounding methodologies. Different payer organizations may specify different rounding conventions for reimbursement calculations, including rounding to the nearest cent, rounding down, rounding up, or banker's rounding. In some aspects, the financial calculation subsystem may' retrieve rounding methodology specifications from the machine-executable payment rules and apply the specified rounding methodology during calculation operations. The payer-specific rounding methodologies may be defined during rule compilation based on rounding provisions extracted from the healthcare payer contract document.

[0211] The financial calculation subsystem may implement multi-currency support with exchange rate handling. In some cases, healthcare payer contracts may specify payment amounts in different currencies or may’ require currency conversion for international patient encounters. The multi-currency support may maintain exchange rate tables and applyDocket No.: BLUE-01 -PCTcurrency conversion operations during reimbursement calculation. In some aspects, the exchange rate handling may support date-specific exchange rates to apply conversion rates corresponding to encounter dates or payment dates.

[0212] Referring to FIG. 1, the payment calculator 123 may implement complex payment formulas corresponding to different healthcare payment methodologies. The complex payment formulas may include outlier calculations with geometric mean length of stay. Outlier calculations may determine whether a patient encounter qualifies for outlier payment adjustments based on costs or length of stay exceeding defined thresholds. In some aspects, the outlier calculations may compute geometric mean length of stay values for diagnosis-related groups and compare actual length of stay against the geometric mean to identify statistical outliers qualifying for additional reimbursement.

[0213] With continued reference to FIG. 1, the payment calculator 123 may implement transfer payment adjustments. Transfer payment adjustments may modify reimbursement calculations for patient encounters involving transfers between healthcare facilities. In some cases, transfer payment adjustments may apply per diem payment methodologies or prorated diagnosis-related group payments based on the portion of the patient stay occurring at each facility. The transfer payment adjustments may evaluate discharge status codes and admission source codes in the patient encounter data to identify transfer scenarios and apply appropriate payment modifications.

[0214] The payment calculator 123 may implement bundling and packaging logic. Bundling logic may identify services, procedures, or supplies that are included in a bundled payment arrangement and should not receive separate reimbursement. Packaging logic may identify' ancillary services that are packaged into primary procedure payments under ambulatory payment classification based payment methodologies. In some aspects, the bundling and packaging logic may evaluate procedure codes, revenue codes, and service relationships to determine which line items within a patient encounter are subject to bundling or packaging provisions defined in the healthcare payer contract document.

[0215] As further shown in FIG. 1, the payment calculator 123 may implement multiple procedure discounting. Multiple procedure discounting may apply graduated discount percentages to secondary and subsequent procedures performed during a same patient encounter. In some cases, the multiple procedure discounting may rank procedures by payment amount and apply progressively higher discount percentages to lower-ranked procedures based on multiple procedure discount provisions defined in the healthcare payer contract document.Docket No.: BLUE-01 -PCT

[0216] The payment calculator 123 may implement validation against expected ranges with anomaly detection. Validation against expected ranges may compare calculated reimbursement amounts to expected value ranges based on historical data, industry benchmarks, or contract-specific parameters. In some aspects, the anomaly detection may identify calculated reimbursement amounts that fall outside expected ranges and flag the calculations for review. Anomaly detection may identify potential calculation errors, data quality issues, or unusual encounter characteristics that warrant additional verification before the expected reimbursement result is finalized.

[0217] Referring to FIG. 3, the Payment Result 320 generated by the payment calculator 123 may comprise a calculated payment amount, an identification of each payment rule applied, and a contribution of each applied payment rule to the calculated payment amount. In some cases, the Payment Result 320 may further comprise execution path records, audit trail data, and intermediate calculation values generated during the reimbursement calculation process. The Payment Result 320 may be stored in the results store 133 and may¬ be used for downstream applications including variance analysis, underpayment detection, and contract modeling operations. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0218] Expected Reimbursement Output Generation

[0219] Referring to FIG. 3. following the Rule Execution 310, the healthcare contract analysis system may generate a Payment Result 320 comprising an expected reimbursement result for a patient encounter. The expected reimbursement result may comprise a calculated payment amount, an identification of each payment rule applied, and a contribution of each applied payment rule to the calculated payment amount. In some aspects, the expected reimbursement result may further comprise execution path records, intermediate calculation values, and audit trail data generated during the reimbursement calculation process.

[0220] With continued reference to FIG. 3, an output module configured to generate outputs based on the expected reimbursement results may produce one or more output types for downstream applications. The output types may include expected payment amounts, patient financial responsibility estimates, reimbursement variance reports, and contract performance analyses. In some cases, the output module may format the expected reimbursement results according to output specifications corresponding to different downstream applications or user interface requirements.Docket No.: BLUE-01 -PCT

[0221] Referring to FIG. 1, the reports and analytics 114 in the application services layer 110 may generate outputs and analytical reports based on processing results from the core processing engine 120. The reports and analytics 114 may receive expected reimbursement results from the payment calculator 123 and generate formatted outputs for presentation through the user interface 101. In some aspects, the reports and analytics 114 may generate expected payment amount outputs that display the calculated reimbursement amount for a patient encounter together with supporting calculation details.

[0222] With continued reference to FIG. 1, the output module may generate patient financial responsibility estimates based on the expected reimbursement results. Patient financial responsibility estimates may calculate the portion of healthcare service costs that a patient may be responsible for paying after application of insurance reimbursement. In some cases, the patient financial responsibility’ estimates may account for deductibles, copayments, coinsurance percentages, and out-of-pocket maximums defined in the patient's insurance plan. The patient financial responsibility estimates may be used for point-of-service cost estimation and patient billing preparation.

[0223] The output module may generate reimbursement variance reports that compare expected reimbursement results to actual reimbursement amounts indicated in remittance data. Reimbursement variance reports may identity7patient encounters for which actual reimbursement differs from expected reimbursement by more than a defined threshold. In some aspects, reimbursement variance reports may include variance amounts, variance percentages, and variance classifications for each identified patient encounter. The reimbursement variance reports may support underpayment detection workflows and payment review operations.

[0224] As further shown in FIG. 1, the output module may generate contract performance analyses that evaluate reimbursement outcomes across patient encounter populations. Contract performance analyses may aggregate expected reimbursement results and actual reimbursement amounts across multiple patient encounters to assess overall contract performance. In some cases, contract performance analyses may include metrics such as total expected reimbursement, total actual reimbursement, aggregate variance, and variance distribution by service type, facility, or time period. The contract performance analyses may support contract evaluation and negotiation preparation activities.

[0225] Referring to FIG. 1. the healthcare contract analysis system may perform batch calculation of expected reimbursement results across a population of patient encounters. The calculation engine may receive a batch of patient encounter data comprisingDocket No.: BLUE-01 -PCTmultiple patient encounters and apply the set of machine-executable payment rules to each patient encounter in the batch. In some aspects, the batch calculation may employ parallel processing to calculate expected reimbursement results for multiple patient encounters concurrently. The batch calculation may support retrospective reimbursement analysis across historical encounter populations.

[0226] With continued reference to FIG. 1, the healthcare contract analysis system may generate population-level reimbursement analytics based on batch calculation results. Population-level reimbursement analytics may aggregate expected reimbursement results across the patient encounter population to generate summary statistics, trend analyses, and comparative metrics. In some cases, population-level reimbursement analytics may include total expected reimbursement for the population, average expected reimbursement per encounter, reimbursement distribution by sendee category, and reimbursement trends over time periods.

[0227] The population-level reimbursement analytics may further include opportunity’ analysis outputs that identify reimbursement improvement opportunities across the patient encounter population. In some aspects, opportunity analysis outputs may identify patient encounters with variance between expected and actual reimbursement, patient encounters that may benefit from alternative billing approaches, and service categories with suboptimal reimbursement performance. The opportunity analysis outputs may support revenue cycle management and reimbursement optimization activities.

[0228] As further shown in FIG. 1 , the results store 133 in the data storage layer 130 may store expected reimbursement results, variance analyses, audit trails, and other system outputs. The results store 133 may maintain associations between expected reimbursement results and corresponding patient encounter data, enabling retrieval of calculation results for specific patient encounters. In some cases, the results store 133 may store historical expected reimbursement results to support trend analysis and retrospective reporting operations.

[0229] The output module may generate audit-oriented explanation outputs that provide traceability’ between expected reimbursement results and originating contract provisions. Audit-oriented explanation outputs may include rule-by-rule impact analysis that quantifies the contribution of each applied payment rule to the calculated payment amount. In some aspects, audit-oriented explanation outputs may include source linkage information that associates each applied payment rule with a corresponding source location in the healthcare payer contract document. The audit-oriented explanation outputs may support payment explanation, audit compliance, and appeal preparation activities. Those of skill in the art willDocket No.: BLUE-01 -PCTrecognize other solutions which are intended to be within the scope of the present disclosure.

[0230] Variance Analysis and Underpayment Detection

[0231] Referring to FIG. 1, the healthcare contract analysis system may include an underpayment detection 126 configured to compare expected reimbursement results to actual reimbursement amounts indicated in remittance data. The underpayment detection 126 may receive remittance data associated with patient encounter data, determine actual reimbursement amounts from the remittance data, calculate variance between expected and actual reimbursement, and generate underpayment signals when variance exceeds a threshold. In some aspects, the underpayment detection 126 may implement a variance analysis module that performs automated payment comparison and variance detection operations.

[0232] With continued reference to FIG. 1, the healthcare contract analysis system may receive remittance data from external payer systems or healthcare information systems. Remittance data may include Electronic Remittance Advice (ERA) data, EDI X12835 transaction data, Explanation of Benefits (EOB) information, payment amounts, adjustment codes, denial codes, and related payment response information. In some cases, the healthcare contract analysis system may associate received remittance data with corresponding patient encounter data stored in the encounter database 132 based on claim identifiers, patient identifiers, service dates, or other matching criteria.

[0233] The underpayment detection 126 may implement contract-based expected payment calculation to generate expected reimbursement results for comparison against actual reimbursement amounts. Contract-based expected payment calculation may apply compiled machine-executable payment rules from the Rule Repository 209 to patient encounter data to generate expected reimbursement results using the same calculation engine and rule execution processes described with reference to FIG. 3. In some aspects, the contract-based expected payment calculation may generate expected reimbursement results that reflect the payment terms, methodologies, and provisions defined in the healthcare payer contract document.

[0234] As further shown in FIG. I, the underpayment detection 126 may implement automated payment comparison to compare expected reimbursement results to actual reimbursement amounts indicated in the remittance data. Automated payment comparison may determine an actual payment amount from the remittance data and calculate a variance between the expected reimbursement result and the actual payment amount. In some cases, the variance may be expressed as an absolute amount representing the difference betweenDocket No.: BLUE-01 -PCTexpected and actual reimbursement. In some aspects, the variance may be expressed as a percentage representing the proportional difference between expected and actual reimbursement.

[0235] The underpayment detection 126 may implement configurable variance threshold detection to identify patient encounters for which variance between expected and actual reimbursement exceeds a defined threshold. In some aspects, the variance threshold may be configured as an absolute amount threshold, a percentage threshold, or a combination of absolute and percentage thresholds. The configurable variance threshold detection may compare calculated variance values against the configured threshold and generate underpayment signals when the variance exceeds the threshold. In some cases, the healthcare contract analysis system may support multiple variance thresholds corresponding to different service types, payer organizations, or facility locations.

[0236] Referring to FIG. 1, the underpayment detection 126 may implement allowed-amount-based variance methodology for remittance analysis. Allowed-amount-based variance methodology may compare expected reimbursement results to allowed amounts indicated in the remittance data rather than to paid amounts alone. In some aspects, the allowed-amount-based variance methodology may account for patient responsibility amounts, contractual adjustments, and other payment components when calculating variance. The allowed-amount-based variance methodology may provide variance analysis that reflects the payer's determination of the allowable reimbursement amount under the healthcare payer contract.

[0237] With continued reference to FIG. 1, the underpayment detection 126 may classify underpayments according to root cause categories. Root cause categories may include categories corresponding to different types of payment discrepancies, such as incorrect payment methodology application, fee schedule rate errors, discount calculation errors, carve-out provision errors, multiple procedure adjustment errors, and other underpayment types. In some cases, the underpayment detection 126 may analyze the variance between expected and actual reimbursement to identify which payment rule or payment component contributed to the underpayment. The root cause classification may associate each identified underpayment with one or more root cause categories based on the analysis.

[0238] The underpayment detection 126 may generate appeal documentation comprising rule-based payment rationale and contract rule citations. Appeal documentation may include documentation supporting an appeal of an underpaid claim to the payerDocket No.: BLUE-01 -PCTorganization. In some aspects, the appeal documentation may include the expected reimbursement amount calculated by the healthcare contract analysis system, the actual reimbursement amount indicated in the remittance data, the calculated variance amount, and the root cause classification. The appeal documentation may further include contract rule citations that identify the specific provisions in the healthcare payer contract document that support the expected reimbursement amount.

[0239] As further shown in FIG. 1, the appeal documentation may include rulebased payment rationale that explains how the expected reimbursement result was calculated from the patient encounter data and the applicable payment rules. Rule-based payment rationale may include identification of each payment rule applied to the patient encounter, the contribution of each applied payment rule to the expected reimbursement result, and source linkage information associating each payment rule with a corresponding source location in the healthcare payer contract document. In some cases, the rule-based payment rationale may provide a step-by-step explanation of the reimbursement calculation that can be used to support the appeal.

[0240] Referring to FIG. 1 , the healthcare contract analysis system may implement automated appeal generation to generate appeal documentation without manual intervention. Automated appeal generation may retrieve expected reimbursement results, variance calculations, root cause classifications, and source linkage information from the results store 133 and format the retrieved information into appeal documentation according to defined appeal document templates. In some aspects, the automated appeal generation may generate appeal documentation in formats suitable for submission to payer organizations, including electronic formats and printable document formats.

[0241] The healthcare contract analysis system may implement recovery success tracking to monitor the outcomes of appeals submitted based on underpayment detection. Recovery success tracking may receive appeal outcome data indicating whether appeals w ere successful, partially successful, or unsuccessful. In some cases, recovery success tracking may associate appeal outcome data with the corresponding underpayment records, root cause classifications, and appeal documentation to maintain a record of appeal outcomes. Recovery success tracking may calculate recovery metrics including recovery rates, recovered amounts, and recovery7success rates by root cause category', payer organization, or service ty pe.

[0242] With continued reference to FIG. 1, the healthcare contract analysis system may implement strategy optimization based on recovery’ success tracking data. Strategy optimization may analyze appeal outcome data to identify’ patterns in successful andDocket No.: BLUE-01 -PCTunsuccessful appeals. In some aspects, strategy optimization may identify root cause categories, payer organizations, or variance characteristics associated with higher appeal success rates. Strategy optimization may generate recommendations for prioritizing underpayment appeals based on the likelihood of successful recover}7. In some cases, strategy optimization may update appeal documentation templates or appeal workflows based on patterns identified in the appeal outcome data.

[0243] The healthcare contract analysis system may implement outcome feedback loops that use recover}' success tracking data to improve underpayment detection and appeal generation operations. Outcome feedback loops may analyze appeal outcomes to identify cases in which expected reimbursement calculations differed from payer determinations. In some aspects, outcome feedback loops may identify patterns in calculation discrepancies and may generate recommendations for refining payment rule parameters, applicability conditions, or execution priorities. The outcome feedback loops may support continuous improvement of the healthcare contract analysis system based on observed appeal outcomes.

[0244] Referring to FIG. 1. the results store 133 may store underpayment detection results, variance analyses, root cause classifications, appeal documentation, and recovery tracking data. In some aspects, the results store 133 may maintain associations between underpayment records and corresponding patient encounter data, remittance data, and appeal outcome data. The reports and analytics 114 may generate underpayment reports, variance analysis reports, and recovery performance reports based on data stored in the results store 133. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0245] Contract Modeling and Financial Impact Analysis

[0246] Referring to FIG. 1, the healthcare contract analysis system may include a contract modeling module configured to generate financial impact analyses for proposed modifications to payment terms. The contract modeling module may receive proposed contract term modifications, generate modified sets of machine-executable payment rules incorporating the proposed modifications, apply the modified sets of machine-executable payment rules to historical encounter populations, and calculate aggregate financial impact of the proposed contract term modifications. In some aspects, the contract modeling module may be implemented within the optimization engine 125 in the core processing engine 120.

[0247] With continued reference to FIG. 1, the contract modeling module may receive proposed modifications to one or more structured payment terms. ProposedDocket No.: BLUE-01 -PCTmodifications may include modified base rates, modified discount percentages, modified carve-out provisions, modified stop-loss thresholds, modified multipliers, and related payment term changes. In some cases, the contract modeling module may receive proposed modifications through the user interface 101 via a term modification modeling interface. The term modification modeling interface may enable a user to specify proposed changes to individual payment terms, payment methodologies, or applicability conditions defined in the healthcare payer contract document.

[0248] The contract modeling module may generate a modified set of machineexecutable payment rules based on the proposed contract term modifications. In some aspects, the contract modeling module may invoke the rule generator 124 to compile the proposed modifications into modified payment rules using the same rule compilation processes applied to original structured payment terms. The modified set of machineexecutable payment rules may incorporate the proposed modifications while preserving unchanged payment terms from the original set of machine-executable payment rules. The modified set of machine-executable payment rules may maintain typed parameters, applicability conditions, and execution priorities consistent with the original rule compilation architecture.

[0249] As further shown in FIG. 1, the contract modeling module may apply the modified set of machine-executable payment rules to a historical encounter population stored in the encounter database 132. The historical encounter population may comprise patient encounter data from a defined time period, service category, facility, or payer relationship. In some cases, the contract modeling module may invoke the rules engine 122 and the payment calculator 123 to calculate expected reimbursement results for each patient encounter in the historical encounter population using the modified set of machine-executable payment rules.

[0250] Referring to FIG. 3, the contract modeling module may apply the modified set of machine-executable payment rules according to the hierarchical rule execution sequence defined in the Rule Execution 310. The contract modeling module may apply modified Carve-Out Rules 311, modified Base Payment Rules 312, modified Discount Rules 313, modified Multiple Procedure Rules 314, modified Stop Loss Rules 315, and modified Lesser-Of Rules 316 to generate modified expected reimbursement results for the historical encounter population.

[0251] With continued reference to FIG. 1, the contract modeling module may calculate aggregate financial impact of the proposed contract term modifications. Aggregate financial impact may comprise a comparison of reimbursement outcomes under the originalDocket No.: BLUE-01 -PCTset of machine-executable payment rules and the modified set of machine-executable payment rules. In some aspects, the aggregate financial impact may include total expected reimbursement under the original payment rules, total expected reimbursement under the modified payment rules, and a difference between the total expected reimbursement values. The aggregate financial impact may further include average reimbursement change per encounter, percentage change in total reimbursement, and reimbursement change distribution across the historical encounter population.

[0252] The contract modeling module may generate contract negotiation reports comprising the aggregate financial impact. Contract negotiation reports may present the financial effects of proposed contract term modifications in formats suitable for contract negotiation preparation. In some cases, contract negotiation reports may include summary metrics, detailed encounter-level impact data, and visualizations of reimbursement changes across the historical encounter population. The contract negotiation reports may be generated by the reports and analytics 114 and presented through the user interface 101.

[0253] As further shown in FIG. 1, the contract modeling module may generate contract simulation reports comprising financial impact analyses for proposed contract term modifications. Contract simulation reports may include projections of reimbursement outcomes under proposed contract terms applied to representative encounter populations. In some aspects, contract simulation reports may include sensitivity analyses that evaluate reimbursement outcomes under multiple proposed modification scenarios. The contract simulation reports may be stored in the results store 133 for subsequent retrieval and analysis.

[0254] Referring to FIG. 1, the contract modeling module may implement side-by-side comparison of proposed contract terms. Side-by-side comparison may present original payment terms and proposed modified payment terms in a comparative format that highlights differences between the original and proposed terms. In some cases, the side-by-side comparison may include original typed parameters, proposed typed parameters, and calculated reimbursement differences for each modified payment term. The side-by-side comparison may enable a user to evaluate the financial implications of individual term modifications within the context of the overall contract structure.

[0255] With continued reference to FIG. 1, the contract modeling module may implement service line impact analysis to evaluate the financial effects of proposed contract term modifications across different service categories. Service line impact analysis may calculate aggregate financial impact for each service line, department, or clinical program within the historical encounter population. In some aspects, service line impact analysis mayDocket No.: BLUE-01 -PCTidentify service lines with increased expected reimbursement under the proposed modifications and service lines with decreased expected reimbursement under the proposed modifications. The service line impact analysis may generate service line-specific impact reports that support targeted contract negotiation strategies.

[0256] The contract modeling module may implement favorable term identification from existing contracts. Favorable term identification may analyze payment terms across multiple healthcare payer contracts stored in the contract repository 131 to identify terms that produce higher expected reimbursement for comparable patient encounters. In some cases, favorable term identification may compare base rates, discount percentages, carve-out provisions, and other payment terms across contracts from different payer organizations. Favorable term identification may generate recommendations for payment terms to request during contract negotiation based on terms observed in existing contracts with other payers.

[0257] As further shown in FIG. 1, the contract modeling module may implement negotiation term optimization to identify' payment term modifications that produce favorable financial outcomes. Negotiation term optimization may evaluate multiple proposed modification scenarios and rank the scenarios according to aggregate financial impact. In some aspects, negotiation term optimization may identify combinations of payment term modifications that maximize expected reimbursement improvement across the historical encounter population. Negotiation term optimization may generate prioritized lists of payment term modifications for use in contract negotiation preparation.

[0258] Referring to FIG. 1 , the contract modeling module may implement risk assessment of proposed contract changes. Risk assessment may evaluate proposed contract term modifications to identify potential negative financial impacts, implementation challenges, or operational risks associated with the proposed changes. In some cases, risk assessment may identify service categories, patient populations, or encounter types for which proposed modifications may result in decreased expected reimbursement. Risk assessment may generate risk indicators and risk summaries that accompany financial impact analyses in contract negotiation reports.

[0259] With continued reference to FIG. 1, the healthcare contract analysis system may implement shadow calculation for testing proposed rule changes against historical encounter populations w ithout persisting results. Shadow' calculation may execute a proposed set of payment rules against a historical encounter population in a calculation mode that generates expected reimbursement results without storing the results in the results store 133 or modifying the original set of machine-executable payment rules stored in the RuleDocket No.: BLUE-01 -PCTRepository 209. In some aspects, shadow calculation may enable evaluation of proposed rule modifications in an isolated calculation environment that preserves the integrity of production payment rules and calculation results.

[0260] The shadow calculation may receive a proposed rule modification comprising one or more changes to typed parameters, applicability7conditions, or execution priorities in the machine-executable payment rules. The shadow calculation may generate a temporary modified rule set incorporating the proposed rule modification and apply the temporary modified rule set to the historical encounter population using the rules engine 122 and the payment calculator 123. In some cases, the shadow calculation may execute in parallel with production calculation operations without affecting production calculation results.

[0261] As further shown in FIG. 1, the healthcare contract analysis system may generate shadow calculation reports comparing outcomes under the proposed rule modification to outcomes under current payment rules. Shadow calculation reports may include encounter-level comparison data showing expected reimbursement under current rules and expected reimbursement under proposed rules for each patient encounter in the historical encounter population. In some aspects, shadow calculation reports may include aggregate comparison metrics, variance distributions, and identification of encounters with significant reimbursement changes under the proposed rule modification.

[0262] Referring to FIG. 1. the shadow calculation reports may be generated by the reports and analytics 1 14 and presented through the user interface 101. In some cases, shadow calculation reports may include recommendations for whether to implement the proposed rule modification based on the calculated financial impact. The shadow calculation reports may support iterative evaluation of multiple proposed rule modifications before committing changes to the production rule set stored in the Rule Repository 209. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0263] Multi-Payer Comparison and Optimization Decision Process

[0264] Referring to FIG. 4, an optimization decision process may begin with an Encounter 401 in which patient encounter data is received by the healthcare contract analysis system. The optimization decision process may then proceed to a Multi-payer Calculation 402 in which the healthcare contract analysis system performs parallel calculation of expected reimbursement using different payer rule sets. In some aspects, the Multi-payerDocket No.: BLUE-01 -PCTCalculation 402 may compile payment rules from multiple healthcare payer contracts and apply each set of payment rules to the patient encounter data to generate expected reimbursement results for each payer option.

[0265] With continued reference to FIG. 4, the healthcare contract analysis system may implement a simultaneous calculation engine for different payer rule sets. The simultaneous calculation engine may apply a first set of machine-executable payment rules compiled from a first healthcare payer contract and a second set of machine-executable payment rules compiled from a second healthcare payer contract to the same patient encounter data concurrently. In some cases, the simultaneous calculation engine may generate a Blue Cross Payment 403 representing expected reimbursement calculated using payment rules compiled from a Blue Cross entity contract and a Blue Shield Payment 404 representing expected reimbursement calculated using payment rules compiled from a Blue Shield entity contract.

[0266] Referring to FIG. 1, the optimization engine 125 in the core processing engine 120 may implement a distributed computing architecture for high- volume processing of multi-payer calculations. The distributed computing architecture may distribute calculation workloads across multiple computing nodes to enable parallel processing of expected reimbursement calculations for large patient encounter populations. In some aspects, the distributed computing architecture may allocate calculation tasks to available computing resources based on workload balancing algorithms and resource availability.

[0267] With continued reference to FIG. 1, the optimization engine 125 may implement asynchronous evaluation with priority queuing for time-sensitive claims.Asynchronous evaluation may enable the healthcare contract analysis system to process multi-payer calculations without blocking other system operations while awaiting calculation completion. In some cases, the priority queuing may assign higher processing priority to time-sensitive claims requiring rapid optimization decisions, such as claims approaching submission deadlines or claims associated w ith pre-service authorization requests. The priority queuing may order pending calculation requests according to assigned priority levels and process higher-priority requests before lower-priority requests.

[0268] As further shown in FIG. 1, the optimization engine 125 may implement batch optimization capabilities for retrospective analysis. Batch optimization may process historical patient encounter populations to identify optimization opportunities across previously submitted claims. In some aspects, batch optimization may apply multiple payer rule sets to a historical encounter population and generate aggregate optimization analysesDocket No.: BLUE-01 -PCTidentifying encounters that may have been submitted to suboptimal payers. The batch optimization capabilities may support lost revenue auditing and retrospective billing analysis workflows.

[0269] Referring to FIG. 1, the optimization engine 125 may implement real-time optimization for pre-claim submission decisions. Real-time optimization may calculate expected reimbursement across multiple payer options at the time of patient encounter registration or prior to claim submission. In some cases, real-time optimization may generate payer routing recommendations before claims are submitted, enabling healthcare providers to select optimal billing entities based on calculated expected reimbursement. The real-time optimization may support point-of-service decision making and pre-service financial estimation workflows.

[0270] With continued reference to FIG. 4, the optimization decision process may proceed from the Multi-payer Calculation 402 to a Comparative Analysis 405 in which the healthcare contract analysis system performs multi-dimensional comparison of expected reimbursement results from each payer option. The Comparative Analysis 405 may receive the Blue Cross Payment 403 and the Blue Shield Payment 404 from the Multi-payer Calculation 402 and evaluate the expected reimbursement results across multiple comparison dimensions.

[0271] The Comparative Analysis 405 may implement rule impact variance analysis to identify specific payment rules that contribute to differences between expected reimbursement results across payer options. Rule impact variance analysis may compare the payment rules applied to the patient encounter data under each payer rule set and identify rules that produce different reimbursement contributions. In some aspects, the rule impact variance analysis may quantify the reimbursement difference attributable to each divergent payment rule, enabling identification of contract provisions that drive reimbursement differences between payer options.

[0272] As further shown in FIG. 4, the Comparative Analysis 405 may implement component-level breakdown of payment differences. Component-level breakdown may decompose the total reimbursement difference between payer options into constituent components corresponding to different rule types, payment methodologies, or service categories. In some cases, the component-level breakdown may identify reimbursement differences attributable to base payment rules, discount rules, carve-out rules, multiple procedure rules, stop-loss rules, and lesser-of rules. The component-level breakdown may generate detailed comparison reports that explain the sources of reimbursement varianceDocket No.: BLUE-01 -PCTbetween payer options.

[0273] Referring to FIG. 4. the Comparative Analysis 405 may implement sensitivity analysis for variable factors that may affect expected reimbursement results.Sensitivity analysis may evaluate how changes in variable encounter characteristics, such as length of stay, procedure counts, or charge amounts, may affect the relative reimbursement outcomes across payer options. In some aspects, the sensitivity analysis may identify threshold values at which the optimal payer selection changes based on variable factor values. The sensitivity analysis may support decision making for encounters with uncertain or variable characteristics by indicating the robustness of payer routing recommendations across potential factor variations.

[0274] With continued reference to FIG. 4, the Comparative Analysis 405 may connect to Decision Factors 410 which encompasses evaluation criteria used to determine an optimal billing entity. The Decision Factors 410 may include a Payment Amount 411, a Processing Time 412, and a Denial Risk 413. The Payment Amount 411 may evaluate the expected reimbursement amount calculated for each payer option. The Processing Time 412 may evaluate estimated claim processing duration for each payer option based on historical processing time data or payer-specific processing characteristics. The Denial Risk 413 may evaluate the likelihood of claim denial for each payer option based on historical denial patterns, payer-specific denial rates, or claim characteristic analysis.

[0275] As further shown in FIG. 4, the outputs from the Decision Factors 410, including the Payment Amount 411, the Processing Time 412, and the Denial Risk 413, may feed into an Optimization Decision 420. The Optimization Decision 420 may generate a final billing entity recommendation based on the evaluated decision factors. In some cases, the Optimization Decision 420 may weight the decision factors according to configurable optimization targets, such as maximizing expected payment amount, minimizing processing time, or minimizing denial risk. The Optimization Decision 420 may generate payer routing recommendations that identify which payer entity to bill for the patient encounter based on the multi-dimensional evaluation.

[0276] Referring to FIG. I, the optimization engine 125 may implement specific BlueCard comparison workflows for healthcare facilities in states with separate Blue Cross and Blue Shield entities. The BlueCard comparison workflows may evaluate out-of-state patient encounters to determine whether to bill a Blue Cross entity or a Blue Shield entity based on expected reimbursement analysis. In some aspects, the BlueCard comparison w orkflows may compile payment rules from both Blue Cross and Blue Shield contractsDocketNo.: BLUE-01 -PCTapplicable to the healthcare facility and apply the respective rule sets to out-of-state Blue plan encounters to generate comparative reimbursement analyses.

[0277] With continued reference to FIG. 1, the reports and analytics module 114 may generate optimization reports based on the Optimization Decision 420. Optimization reports may include encounter-level billing entity recommendations, aggregate optimization opportunity analyses across patient encounter populations, and trend analyses of payer performance by claim characteristics. In some cases, the optimization reports may include facility-specific optimization recommendations based on the healthcare facility's existing contract terms with different payer entities. The optimization reports may be stored in the results store 403 and presented through the user interface 101. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0278] BlueCard Optimization and Billing Entity Recommendations

[0279] Referring to FIG. 4, the healthcare contract analysis system may implement BlueCard optimization workflows for healthcare facilities in states with separate Blue Cross and Blue Shield entities. In some aspects, healthcare providers in states such as California, Idaho, New York, Pennsylvania, and Washington may have the option to bill either a Blue Cross entity7or a Blue Shield entity for out-of-state patients with Blue Cross Blue Shield plans. The healthcare contract analysis system may generate billing entity recommendations that identify which Blue Cross Blue Shield entity to bill for out-of-state patient encounters based on comparative reimbursement analysis.

[0280] With continued reference to FIG. 4, the Multi-payer Calculation 402 may generate parallel calculation paths for Blue Cross and Blue Shield payment options. A first calculation path may generate a Blue Cross Payment 403 representing expected reimbursement calculated using machine-executable payment rules compiled from a Blue Cross entity contract. A second calculation path may generate a Blue Shield Payment 404 representing expected reimbursement calculated using machine-executable payment rules compiled from a Blue Shield entity contract. In some cases, the healthcare contract analysis system may apply the respective rule sets to the same patient encounter data concurrently to generate expected reimbursement results for each billing entity option.

[0281] The healthcare contract analysis system may implement facility-specific BlueCard optimization based on existing contract terms. In some aspects, the healthcare contract analysis system may retrieve machine-executable payment rules compiled from Blue Cross and Blue Shield contracts applicable to a specific healthcare facility. The facility -Docket No.: BLUE-01 -PCTspecific BlueCard optimization may evaluate the payment terms, fee schedules, discount provisions, and other reimbursement parameters defined in each contract to determine expected reimbursement for patient encounters at the specific healthcare facility. In some cases, the facility-specific BlueCard optimization may account for facility-specific rate variations, geographic adjustments, and contract-specific payment methodologies when calculating expected reimbursement under each billing entity option.

[0282] Referring to FIG. I. the optimization engine 125 may implement state-by-state analysis of Blue entity payment differences. State-by-state analysis may evaluate payment methodology variations, fee schedule differences, and reimbursement rate disparities between Blue Cross and Blue Shield contracts across different states. In some aspects, the state-by-state analysis may identify states in which Blue Cross contracts provide higher expected reimbursement for certain service types and states in which Blue Shield contracts provide higher expected reimbursement for certain service types. The state-by-state analysis may generate state-specific payment difference profiles that inform billing entity' recommendations for out-of-state patient encounters originating from different states.

[0283] With continued reference to FIG. 1, the healthcare contract analysis system may implement BlueCard program compliance monitoring. BlueCard program compliance monitoring may verify that billing entity7recommendations comply with BlueCard program requirements administered by the Blue Cross Blue Shield Association. In some cases, the BlueCard program compliance monitoring may evaluate inter-plan arrangement provisions, multi-plan reimbursement routing parameters, and Blue Cross Blue Shield Association program terms extracted from healthcare payer contract documents. The BlueCard program compliance monitoring may generate compliance indicators that accompany billing entity7recommendations to indicate whether the recommended billing entity selection complies with applicable BlueCard program requirements.

[0284] As further shown in FIG. 4, the Comparative Analysis 405 may receive the Blue Cross Payment 403 and the Blue Shield Payment 404 from the Multi-payer Calculation 402 and perform multi-dimensional comparison of expected reimbursement results. The Comparative Analysis 405 may evaluate the expected reimbursement amounts, identify specific payment rules that contribute to reimbursement differences, and generate component-level breakdowns of payment differences between the Blue Cross and Blue Shield options. In some aspects, the Comparative Analysis 405 may identify whether base payment rules, discount rules, can e-out rules, or other rule types contribute to reimbursement variance between the billing entity options.Docket No.: BLUE-01 -PCT

[0285] The healthcare contract analysis system may implement automated routing recommendations for optimal submission of out-of-state patient encounters. Automated routing recommendations may identify which Blue Cross Blue Shield entity to bill based on the comparative analysis of expected reimbursement results. In some cases, the automated routing recommendations may be generated at the time of patient encounter registration or prior to claim submission, enabling healthcare providers to select billing entities based on calculated expected reimbursement. The automated routing recommendations may include the recommended billing entity, the expected reimbursement amount under the recommended billing entity, and the reimbursement difference compared to the alternative billing entity option.

[0286] Referring to FIG. 4. the Decision Factors 410 may include a Payment Amount 411, a Processing Time 412, and a Denial Risk 413 for evaluation of billing entity options. The Payment Amount 411 may evaluate the expected reimbursement amount calculated for each Blue Cross Blue Shield entity option. The Processing Time 412 may evaluate estimated claim processing duration for each billing entity based on historical processing time data or payer-specific processing characteristics. The Denial Risk 413 may evaluate the likelihood of claim denial for each billing entity option based on historical denial patterns, payer-specific denial rates, or claim characteristic analysis.

[0287] With continued reference to FIG. 4, the Optimization Decision 420 may generate a billing entity recommendation based on the evaluated decision factors. In some aspects, the billing entity recommendation may identify which Blue Cross Blue Shield entity to bill for an out-of-state patient encounter based on multi-dimensional evaluation of payment amount, processing time, and denial risk. The billing entity recommendation may weight the decision factors according to configurable optimization targets, such as maximizing expected payment amount, minimizing processing time, or minimizing denial risk. In some cases, the billing entity recommendation may include confidence indicators reflecting the degree of certainty associated with the recommendation based on the magnitude of reimbursement differences and the reliability of the underlying payment rule calculations.

[0288] The healthcare contract analysis system may implement expanded BlueCard detection and routing mechanics. In some aspects, the expanded BlueCard detection may identify out-of-state Blue plan encounters based on patient insurance information, plan identifiers, and geographic indicators in the patient encounter data. The expanded BlueCard detection may determine whether a patient encounter qualifies for BlueCard program processing and whether multiple billing entity options are available for the encounter. InDocket No.: BLUE-01 -PCTsome cases, the expanded BlueCard detection may evaluate plan-specific routing requirements and inter-plan arrangement provisions to identify applicable billing entity options.

[0289] Referring to FIG. 1, the reports and analytics module 114 may generate BlueCard optimization reports based on the billing entity recommendations. BlueCard optimization reports may include encounter-level billing entity recommendations, aggregate optimization opportunity analyses across out-of-state patient encounter populations, and trend analyses of Blue entity payment differences by claim characteristics. In some cases, the BlueCard optimization reports may include facility-specific optimization recommendations based on the healthcare facility 's existing contract terms with Blue Cross and Blue Shield entities. The BlueCard optimization reports may be stored in the results store 133 and presented through the user interface 101.

[0290] As further shown in FIG. 1, the healthcare contract analysis system may implement batch BlueCard optimization for retrospective analysis of historical out-of-state patient encounters. Batch BlueCard optimization may apply Blue Cross and Blue Shield rule sets to a historical encounter population and generate aggregate optimization analyses identifying encounters that may have been submitted to suboptimal billing entities. In some aspects, the batch BlueCard optimization may calculate aggregate reimbursement differences across the historical encounter population to quantify optimization opportunities. The batch BlueCard optimization may support lost revenue auditing and retrospective billing analysis workflows for BlueCard program encounters. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0291] Decision Factors and Optimization Algorithms

[0292] Referring to FIG. 4, the Comparative Analysis 405 may connect to Decision Factors 410 which encompasses evaluation criteria used to determine an optimal billing entity for a patient encounter. The Decision Factors 410 may include a Payment Amount 411, a Processing Time 412, and a Denial Risk 413. The healthcare contract analysis system may evaluate the Decision Factors 410 to generate an Optimization Decision 420 comprising a billing entity recommendation or payer routing recommendation based on multi-dimensional analysis of the evaluated decision factors.

[0293] With continued reference to FIG. 4, the Payment Amount 411 may evaluate the expected reimbursement amount calculated for each payer option. The Payment Amount 411 may receive expected reimbursement results from the Multi -pay er Calculation 402,Docket No.: BLUE-01 -PCTincluding the Blue Cross Payment 403 and the Blue Shield Payment 404, and compare the expected reimbursement amounts across the payer options. In some aspects, the Payment Amount 411 may identify the payer option associated with the highest expected reimbursement amount and may quantify the reimbursement difference between payer options.

[0294] The Processing Time 412 may evaluate estimated claim processing duration for each payer option. In some cases, the Processing Time 412 may retrieve historical processing time data associated with each payer organization and calculate estimated processing duration based on claim characteristics, payer-specific processing patterns, and historical claim adjudication timelines. The Processing Time 412 may identify payer options associated with shorter expected processing durations and may factor processing time considerations into the optimization decision.

[0295] As further shown in FIG. 4, the Denial Risk 413 may evaluate the likelihood of claim denial for each payer option. The Denial Risk 413 may analyze historical denial patterns, payer-specific denial rates, and claim characteristic analysis to estimate denial probability for each payer option. In some aspects, the Denial Risk 413 may evaluate claim attributes including diagnosis codes, procedure codes, service types, and billing form characteristics against historical denial data to generate denial risk estimates. The Denial Risk 413 may identify payer options associated ith lower estimated denial risk.

[0296] Referring to FIG. 1. the optimization engine 125 may implement decision optimization algorithms to generate the Optimization Decision 420 based on the evaluated Decision Factors 410. The decision optimization algorithms may include rule-based decision trees for clear-cut cases in which one payer option is superior across all evaluated decision factors. Rule-based decision trees may evaluate the Payment Amount 411, the Processing Time 412, and the Denial Risk 413 according to defined decision logic and generate billing entity recommendations when the decision factors indicate a clear preference for one payer option.

[0297] With continued reference to FIG. 1, the optimization engine 125 may implement probabilistic modeling for cases with incomplete information. Probabilistic modeling may generate optimization decisions when one or more decision factors cannot be evaluated with certainty due to missing data, limited historical information, or variable encounter characteristics. In some cases, the probabilistic modeling may estimate probability distributions for uncertain decision factors based on available data and generate optimization decisions that account for uncertainty in the underlying factor evaluations.Docket No.: BLUE-01 -PCT

[0298] The optimization engine 125 may implement machine learning classifiers trained on historical optimization outcomes. The machine learning classifiers may receive training data comprising historical patient encounters, billing entity selections, and observed reimbursement outcomes. In some aspects, the machine learning classifiers may learn patterns associating encounter characteristics, payer attributes, and decision factor values with successful optimization outcomes. The machine learning classifiers may generate optimization decisions for new patient encounters based on learned patterns from historical optimization data.

[0299] As further shown in FIG. 1, the optimization engine 125 may implement profit maximization algorithms with confidence scoring. Profit maximization algorithms may evaluate expected reimbursement amounts, processing costs, denial-related costs, and other financial factors to identify payer options that maximize net financial benefit. In some cases, the profit maximization algorithms may generate confidence scores indicating the degree of certainty associated with the optimization decision based on the magnitude of reimbursement differences, the reliability of decision factor evaluations, and the consistency of historical outcome data.

[0300] Referring to FIG. 4, the optimization engine 125 may implement configurable optimization targets that govern how the Decision Factors 410 are weighted in generating the Optimization Decision 420. Configurable optimization targets may include a maximize payment target that prioritizes the Payment Amount 411 in optimization decisions, a minimize denials target that prioritizes the Denial Risk 413 in optimization decisions, and a minimize processing time target that prioritizes the Processing Time 412 in optimization decisions. In some aspects, the configurable optimization targets may allow a user to specify custom weighting of decision factors according to facility-specific priorities or operational objectives.

[0301] With continued reference to FIG. 4, the Optimization Decision 420 may be generated based on the evaluated Decision Factors 410 and the configured optimization targets. The Optimization Decision 420 may comprise a billing entity recommendation identifying which payer entity to bill for the patient encounter, an expected reimbursement amount under the recommended billing entity, and a confidence indicator reflecting the reliability of the recommendation. In some cases, the Optimization Decision 420 may include comparative data showing expected outcomes under alternative payer options.

[0302] Referring to FIG. 1. the healthcare contract analysis system may implement facility-wide aggregation for optimization impact analysis. Facility-wide aggregation mayDocket No.: BLUE-01 -PCTaggregate optimization decisions and expected reimbursement results across patient encounter populations to calculate aggregate optimization impact at the facility level. In some aspects, facility-wide aggregation may calculate total expected reimbursement improvement, average reimbursement improvement per encounter, and optimization opportunity distribution across service categories, payer relationships, and time periods.

[0303] The healthcare contract analysis system may implement predictive modeling of future optimization opportunities. Predictive modeling may analyze historical encounter patterns, payer mix trends, and contract term changes to forecast future optimization opportunities. In some cases, predictive modeling may generate projections of expected optimization impact for future time periods based on anticipated encounter volumes, payer distributions, and contract configurations. The predictive modeling may support revenue forecasting and resource planning activities.

[0304] As further shown in FIG. 1, the healthcare contract analysis system may implement population trend detection across patient populations and service lines. Population trend detection may analyze optimization outcomes across patient encounter populations to identify trends in payer performance, reimbursement patterns, and optimization opportunity distribution. In some aspects, population trend detection may identify' service lines with increasing optimization opportunities, patient populations with changing payer mix characteristics, and temporal trends in payer reimbursement performance.

[0305] The healthcare contract analysis system may implement payer performance statistics by claim characteristics. Payer performance statistics may calculate statistical measures of payer reimbursement performance segmented by claim characteristics including service type, diagnosis category, procedure category, facility location, and patient type. In some cases, payer performance statistics may identify claim characteristic combinations associated with higher reimbursement variance between payer options, enabling targeted optimization strategies for specific claim populations.

[0306] Referring to FIG. 1, the healthcare contract analysis sy stem may implement contract time-series analysis to evaluate payer performance changes over time. Contract timeseries analysis may track expected reimbursement results, optimization outcomes, and decision factor values across sequential time periods to identify trends in contract performance. In some aspects, contract time-series analysis may detect changes in payer reimbursement rates, processing time patterns, and denial rates over the duration of a healthcare payer contract. The contract time-series analysis may generate trend reports and performance change indicators that support contract evaluation and negotiation preparationDocket No.: BLUE-01 -PCTactivities. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0307] Pattern Recognition and Learning Systems

[0308] Referring to FIG. 1, the optimization engine 125 may implement a pattern recognition and learning system configured to improve billing optimization outcomes through automated analysis of billing patterns, outcome data, and contract term effectiveness. The pattern recognition and learning system may analyze historical billing data, optimization outcomes, and reimbursement results to identify patterns that inform future billing decisions and contract negotiation strategies. In some aspects, the pattern recognition and learning system may operate in conjunction with the rules engine 122 and the payment calculator 123 to generate data-driven recommendations for billing optimization.

[0309] With continued reference to FIG. 1, the pattern recognition and learning system may implement automated detection of systematic billing patterns across patient encounter populations. Systematic billing pattern detection may analyze historical billing data to identify recurring patterns in payer selection, service type distribution, reimbursement outcomes, and claim processing characteristics. In some cases, the systematic billing pattern detection may identify7billing patterns associated with higher reimbursement outcomes, shorter processing times, or lower denial rates. The systematic billing pattern detection may generate pattern profiles that characterize billing behaviors across different service categories, facility locations, and payer relationships.

[0310] Referring to FIG. 4, the pattern recognition and learning system may implement identification of contract term exploitation opportunities. Contract term exploitation opportunity identification may analyze machine-executable payment rules stored in the Rule Repository 209 to identify contract provisions that may be underutilized or that may provide reimbursement advantages for specific encounter types. In some aspects, the contract term exploitation opportunity identification may compare expected reimbursement results across different rule application scenarios to identify contract terms that produce favorable reimbursement outcomes when applicable. The contract term exploitation opportunity identification may generate opportunity reports that highlight contract provisions with potential for increased reimbursement capture.

[0311] With continued reference to FIG. 4, the pattern recognition and learning system may implement machine learning models for predicting optimal billing pathways. The machine learning models may receive training data comprising historical patient encounters,Docket No.: BLUE-01 -PCTbilling entity selections, payer routing decisions, and observed reimbursement outcomes. In some cases, the machine learning models may learn associations between encounter characteristics, payer attributes, contract terms, and successful billing outcomes. The machine learning models may generate predictions for optimal billing pathways for new patient encounters based on learned patterns from historical billing data.

[0312] Referring to FIG. 1. the machine learning models may include classification models trained to predict which payer option will produce the highest expected reimbursement for a given patient encounter. In some aspects, the classification models may evaluate encounter attributes including service type, diagnosis codes, procedure codes, facility identifiers, and patient insurance information to generate payer selection predictions. The machine learning models may further include regression models trained to predict expected reimbursement amounts for different billing pathway options. The regression models may generate reimbursement predictions that inform the Optimization Decision 420 shown in FIG. 4.

[0313] With continued reference to FIG. 1, the pattern recognition and learning system may implement continuous improvement through outcome feedback loops. Outcome feedback loops may receive outcome data indicating actual reimbursement amounts, claim processing durations, denial occurrences, and appeal results associated with billing decisions. In some cases, the outcome feedback loops may compare predicted outcomes generated by the pattern recognition and learning system to actual observed outcomes to identify prediction errors and model improvement opportunities. The outcome feedback loops may update machine learning model parameters, pattern profiles, and optimization algorithms based on observed outcome data.

[0314] The outcome feedback loops may implement model retraining processes that incorporate new outcome data into the machine learning models. In some aspects, the model retraining processes may execute periodically or upon accumulation of a threshold quantity of new outcome data. The model retraining processes may update model weights, feature importance rankings, and prediction thresholds based on the new outcome data. The outcome feedback loops may generate model performance metrics indicating prediction accuracy, reimbursement optimization effectiveness, and improvement trends over time.

[0315] Referring to FIG. 4, the pattern recognition and learning system may implement rule effectiveness scoring to guide future contract negotiations. Rule effectiveness scoring may evaluate the contribution of individual machine-executable payment rules to reimbursement outcomes across patient encounter populations. In some cases, ruleDocket No.: BLUE-01 -PCTeffectiveness scoring may calculate effectiveness metrics for each payment rule based on frequency of application, reimbursement contribution, variance from expected outcomes, and comparison to alternative rule options. Rule effectiveness scoring may generate rule effectiveness reports that rank payment rules according to calculated effectiveness metrics.

[0316] With continued reference to FIG. 1, the rule effectiveness scoring may identify payment rules associated with favorable reimbursement outcomes and payment rules associated with suboptimal reimbursement outcomes. In some aspects, the rule effectiveness scoring may compare rule effectiveness across different healthcare payer contracts to identify contract terms that produce higher reimbursement for comparable patient encounters. The rule effectiveness scoring may generate contract negotiation recommendations that identify payment terms to prioritize, modify, or replace during contract renegotiation based on observed rule effectiveness data.

[0317] Referring to FIG. 1, the pattern recognition and learning system may implement retrospective analysis of past billing to identify missed optimization opportunities. Retrospective billing analysis may apply current machine-executable payment rules and optimization algorithms to historical patient encounter populations to identify encounters that may have been billed to suboptimal payers. In some cases, the retrospective billing analysis may calculate expected reimbursement under alternative billing pathways for historical encounters and compare the calculated expected reimbursement to actual reimbursement received. The retrospective billing analysis may identify historical encounters for which alternative billing pathways would have produced higher reimbursement.

[0318] With continued reference to FIG. 1, the pattern recognition and learning system may implement automated detection of claims billed to suboptimal payers.Suboptimal payer detection may analyze historical billing records to identify claims that were submitted to payers other than the payer predicted to produce the highest expected reimbursement. In some aspects, the suboptimal payer detection may calculate reimbursement differences between actual billing pathways and optimal billing pathways for identified claims. The suboptimal payer detection may generate suboptimal billing reports that quantify aggregate reimbursement differences across historical encounter populations.

[0319] As further shown in FIG. 1, the pattern recognition and learning system may implement recovery opportunity prioritization based on reimbursement potential. Recovery' opportunity prioritization may rank identified missed optimization opportunities according to estimated recovery potential, recovery likelihood, and recovery effort requirements. In some cases, the recovery opportunity prioritization may evaluate factors including reimbursementDocket No.: BLUE-01 -PCTdifference magnitude, time since original billing, payer rebilling policies, and historical recovery success rates for similar claims. The recovery opportunity prioritization may generate prioritized recovery opportunity lists that guide resource allocation for recovery efforts.

[0320] Referring to FIG. 4, the recovery opportunity prioritization may integrate with the Decision Factors 410 to evaluate recovery opportunities according to multiple criteria. The recovery opportunity prioritization may consider Payment Amount 411 factors including the magnitude of potential reimbursement recovery. The recovery opportunity prioritization may consider Processing Time 412 factors including estimated time required to pursue recovery'. In some aspects, the recovery' opportunity prioritization may consider Denial Risk 413 factors including the likelihood that recovery attempts will be successful based on historical recovery outcome data.

[0321] With continued reference to FIG. 1, the results store 133 may store pattern recognition data, machine learning model parameters, rule effectiveness scores, retrospective analysis results, and recovery opportunity prioritization data generated by the pattern recognition and learning system. The reports and analytics module 114 may generate pattern analysis reports, optimization opportunity reports, and recovery' prioritization reports based on data stored in the results store 133. In some cases, the reports and analytics module 114 may present pattern recognition and learning system outputs through the user interface 101 to support billing optimization decision making and contract negotiation preparation. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0322] Real-Time Reimbursement Applications

[0323] Referring to FIG. 1, the healthcare contract analysis system may support real-time reimbursement calculation applications that generate expected reimbursement results at or near the time of patient encounter registration or service delivery. The real-time reimbursement calculation applications may leverage the machine-executable payment rules stored in the Rule Repository 209 and the calculation engine implemented by the payment calculator 123 to generate expected reimbursement outputs for prospective patient encounters. In some aspects, the real-time reimbursement calculation applications may support point-of-service decision making, patient financial counseling, and pre-service authorization workflows.

[0324] With continued reference to FIG. 1, the healthcare contract analysis systemDocket No.: BLUE-01 -PCTmay implement pre-service estimation of expected payments from different payers. Preservice estimation may calculate expected reimbursement amounts for anticipated healthcare services before the services are rendered. In some cases, the pre-service estimation may receive preliminary patient encounter data comprising anticipated diagnosis codes, planned procedure codes, estimated service dates, and patient insurance information. The pre-service estimation may apply machine-executable payment rules compiled from healthcare payer contracts associated with the patient's insurance coverage to generate expected reimbursement estimates for the anticipated services.

[0325] Referring to FIG. 4, the pre-sen ice estimation may implement multi-payer calculation when a patient encounter may be billed to multiple payer options. The Multipayer Calculation 402 may generate expected reimbursement estimates under different payer rule sets, including a Blue Cross Payment 403 and a Blue Shield Payment 404 for out-of-state Blue plan patients. In some aspects, the pre-service estimation may generate comparative expected reimbursement estimates that identify reimbursement differences across payer options prior to service delivery.

[0326] With continued reference to FIG. 1, the healthcare contract analysis system may implement point-of-service patient financial responsibility determination. Point-of-service patient financial responsibility determination may calculate the portion of healthcare service costs that a patient may be responsible for paying based on expected reimbursement from the patient's insurance coverage. In some cases, the point-of-service patient financial responsibility determination may account for deductibles, copayments, coinsurance percentages, and out-of-pocket maximums defined in the patient's insurance plan. The point-of-service patient financial responsibility determination may generate patient financial responsibility estimates at the time of patient registration or service scheduling.

[0327] The healthcare contract analysis system may implement registration-time insurance verification and optimization. Registration-time insurance verification may verify patient insurance coverage and identify applicable healthcare payer contracts at the time of patient registration. In some aspects, the registration-time insurance verification may retrieve machine-executable payment rules associated with the verified insurance coverage from the Rule Repository 209. Registration-time optimization may evaluate expected reimbursement across available billing options and generate billing entity recommendations prior to service delivery. In some cases, the registration-time optimization may identify out-of-state Blue plan patients eligible for BlueCard program processing and generate billing entity recommendations based on comparative reimbursement analysis.Docket No.: BLUE-01 -PCT

[0328] As further shown in FIG. 1, the healthcare contract analysis system may implement dynamic price transparency tools for patient cost estimation. Dynamic price transparency tools may generate patient-facing cost estimates based on expected reimbursement calculations and patient financial responsibility determinations. In some cases, the dynamic price transparency tools may receive sendee selection inputs from patients or healthcare staff and generate cost estimates reflecting the patient's specific insurance coverage and applicable contract terms. The dynamic price transparency tools may update cost estimates dynamically as service selections change or as additional patient insurance information becomes available.

[0329] Referring to FIG. 3. the healthcare contract analysis system may implement prior authorization financial impact assessment. Prior authorization financial impact assessment may calculate expected reimbursement for services requiring prior authorization from payer organizations. In some aspects, the prior authorization financial impact assessment may apply machine-executable payment rules to anticipated service data to generate expected reimbursement estimates that inform prior authorization decision making. The prior authorization financial impact assessment may identify services for which expected reimbursement may vary based on authorization status and may quantify the financial impact of authorization outcomes.

[0330] With continued reference to FIG. 1, the healthcare contract analysis system may implement predictive analytics for future reimbursement scenarios. Predictive analytics may analyze historical encounter data, contract terms, and reimbursement patterns to generate projections of future reimbursement outcomes. In some cases, the predictive analytics may employ statistical models, trend analysis, and machine learning techniques to forecast expected reimbursement across future time periods.

[0331] The healthcare contract analysis system may implement revenue forecasting based on contract terms and anticipated encounter volumes. Revenue forecasting may apply machine-executable payment rules to projected encounter populations to generate aggregate expected reimbursement projections. In some aspects, the revenue forecasting may incorporate anticipated changes in payer mix, service volume, and contract terms to generate revenue projections that reflect expected operational conditions. The revenue forecasting may generate revenue projections segmented by payer, sendee line, facility, or time period.

[0332] As further shown in FIG. 1, the healthcare contract analysis system may implement impact analysis of payer mix changes. Payer mix impact analysis may evaluate how changes in the distribution of patients across different payer categories may affectDocket No.: BLUE-01 -PCTaggregate expected reimbursement. In some cases, the payer mix impact analysis may model scenarios in which the proportion of patients covered by different payer organizations changes and may calculate the resulting effect on total expected reimbursement. The payer mix impact analysis may identify payer categories associated with higher or lower expected reimbursement per encounter and may quantify7the financial sensitivity of the healthcare facility to payer mix variations.

[0333] Referring to FIG. 4. the healthcare contract analysis system may implement cash flow projection based on contract terms. Cash flow projection may combine expected reimbursement calculations with estimated claim processing durations to generate projections of when reimbursement payments may be received. In some aspects, the cash flow projection may incorporate the Processing Time 412 factor from the Decision Factors 410 to estimate payment timing for different payer options. The cash flow projection may generate cash flow forecasts that account for expected reimbursement amounts, estimated processing durations, and anticipated denial rates across the patient encounter population.

[0334] With continued reference to FIG. 1, the healthcare contract analysis system may implement service line profitability analysis. Service line profitability analysis may calculate expected reimbursement aggregated by service line, clinical program, or department. In some cases, the service line profitability analysis may compare expected reimbursement to service delivery costs to generate profitability metrics for different service categories. The service line profitability analysis may identify service lines with higher or lower expected reimbursement relative to costs and may support resource allocation and strategic planning decisions.

[0335] The healthcare contract analysis system may implement what-if analysis for business planning. What-if analysis may evaluate the financial impact of hypothetical changes in operational parameters, contract terms, or market conditions. In some aspects, the what-if analysis may receive scenario parameters specifying proposed changes and may apply modified assumptions to historical or projected encounter populations to calculate expected reimbursement under the hypothetical scenarios. The what-if analysis may compare expected reimbursement outcomes across multiple scenarios to support business planning and strategic decision making.

[0336] As further shown in FIG. 1, the reports and analytics module 114 may generate predictive analytics reports based on revenue forecasting, payer mix impact analysis, cash flow projection, service line profitability analysis, and what-if analysis results. The predictive analytics reports may be stored in the results store 133 and presented through theDocket No.: BLUE-01 -PCTuser interface 101. In some cases, the predictive analytics reports may include visualizations, summary metrics, and detailed projection data that support financial planning and operational decision making. Those of skill in the art will recognize other solutions which are intended to be within the scope of the present disclosure.

[0337] Those skilled in the art will recognize that various modifications and variations can be made without departing from the spirit and scope of the invention.

[0338] EXAMPLES

[0339] Aspects of the present teachings may be further understood in light of the following examples, which should not be construed as limiting the scope of the present teachings in any way.

[0340] Example 1: Automated Contract Rule Extraction and Compilation

[0341] In this example, a healthcare contract analysis system receives and processes a healthcare payer contract document to generate machine-executable payment rules. A healthcare facility provides a payer contract document in Portable Document Format (PDF) comprising 127 pages of contract terms, including base payment provisions, fee schedule exhibits, discount structures, and carve-out specifications.

[0342] The system receives the contract document through a contract ingestion module and performs document preprocessing operations. The document preprocessing includes LLM-enhanced optical character recognition (OCR) for scanned sections with healthcare-specific term recognition, LLM-powered structural analysis to identify 23 distinct sections, 14 tables, and 4 appendices, and neural table extraction for 3 complex fee schedule tables containing procedure code-to-rate mappings. The system performs document segmentation based on semantic boundaries, generating 47 semantically coherent segments for subsequent analysis.

[0343] Following preprocessing, the system performs semantic analysis using a large language model configured with healthcare payment domain context. The healthcare payment domain context includes a healthcare payment terminology dictionary, contract structure templates, payment methodology patterns, and payer-specific term definitions. The system employs multi-stage prompting with progressive refinement comprising an initial pass for section classification, targeted extraction for identified payment sections, and a verification pass to resolve ambiguities.

[0344] The system extracts structured payment terms from the contract documentDocket No.: BLUE-01 -PCTusing methodology-specific extraction templates. The extraction identifies the following payment terms:Table 1: Extracted Payment Terms from Healthcare Payer Contract Payment Term Type Extracted Value Applicability Condition Inpatient sendees, effective DRG Base Rate $7,842.5001 / 01 / 2025DRG Multiplier 1.0 (base weight) All MS-DRG assignments APC Payment Rate 85% of Medicare APC Outpatient services Professional services, CPT Fee Schedule Reference Exhibit B-299201-99499Skilled nursing facility' Per Diem Rate $1,250.00senicesDiscount Percentage 15% Charges exceeding $50,000Implantable devices, Carve-Out Provision Separate paymentrevenue code 0278Stop-Loss Threshold $250,000 Per admissionSame-day surgical Multiple Procedure Discount 50% secondary, 75% tertiaryprocedures

[0345] The system performs validation operations on the extracted structured payment terms including historical pattern validation against prior contract data, industry range validation comparing extracted values to expected ranges, and internal consistency checks across related terms. The system determines a confidence score for each extracted term based on LLM certainty' indicators, pattern matching confidence, presence of ambiguous language, and consistency with expected values. Terms with confidence scores below a threshold of 0.85 are routed to a human review workflow for verification.

[0346] Following validation, the system compiles the validated and normalized structured payment terms into machine-executable payment rules. Each machine-executable payment rule comprises typed parameters, one or more applicability conditions, and an execution priority. The rule compilation module generates a rule dependency graph and determines execution priorities based on the dependency relationships. The system generates 156 machine-executable payment rules from the contract document, organized into ruleDocket No.: BLUE-01 -PCTcategories including DRG-based reimbursement (12 rules), APC-based reimbursement (8 rules), fee schedule reimbursement (89 rules), carve-out rules (14 rules), discount rules (7 rules), stop-loss rules (3 rules), multiple procedure rules (4 rules), and lesser-of rules (19 rules).

[0347] The generated machine-executable payment rules are stored in a rule repository with source linkage metadata associating each payment rule with a corresponding source location in the healthcare payer contract document. The contract processing workflow completes in approximately 4.2 minutes, compared to an estimated 8-12 hours for manual contract abstraction workflows.

[0348] Example 2: Priority-Ordered Reimbursement Calculation Across Multiple Payment Methodologies

[0349] In this example, the healthcare contract analysis system applies compiled machine-executable payment rules to patient encounter data to generate expected reimbursement results. The system receives patient encounter data for an inpatient surgical admission with the following charactenstics:Table 2: Patient Encounter Characteristics for Inpatient Surgical Admission Encounter Attribute ValueAdmission Date 03 / 15 / 2025Discharge Date 03 / 19 / 2025Length of Stay 4 daysICD-10-CM: K80.00 (Calculus of Principal Diagnosisgallbladder with acute cholecystitis) ICD-10-PCS: 0FT44ZZ (Resection of Principal Proceduregallbladder, percutaneous endoscopic) 418 (Laparoscopic cholecystectomy with MS-DRG Assignmentcomplications)DRG Relative Weight 1.4523Total Charges $78,450.00Implantable Device Charge $4,200.00 (Revenue Code 0278) Facility Identifier NPI: 1234567890Billing Form Type UB-04 (CMS-1450)Docket No.: BLUE-01 -PCT

[0350] The rules engine identifies applicable payment rules from the rule repository based on the patient encounter data and the applicability conditions defined in the machineexecutable payment rules. The rules engine identifies 8 applicable payment rules for this encounter and determines execution priorities according to the hierarchical rule sequence.

[0351] The system applies the applicable payment rules in the following hierarchical sequence:

[0352] Step 1 - Carve-Out Rules (Highest Priority): The system applies carve-out rules to identify services subject to separate payment terms. The implantable device charge of $4,200.00 (revenue code 0278) is identified as a carved-out service. The carve-out rule specifies payment at 85% of billed charges for implantable devices. Carve-out payment calculation: $4,200.00 x 0.85 = $3,570.00.

[0353] Step 2 - Base Payment Rules: The system applies DRG-based payment rules to establish the foundational reimbursement calculation. The base payment calculation applies the contracted DRG base rate multiplied by the DRG relative weight:$7,842.50 x 1.4523 = $11,391.29.

[0354] Step 3 - Discount Rules: The system evaluates discount rules. The total charges of $78,450.00 exceed the $50,000 threshold specified in the discount provision. However, the discount rule applicability condition specifies application to percentage-of-charge payment methodologies only, not DRG-based payments. No discount is applied.

[0355] Step 4 - Multiple Procedure Rules: The system evaluates multiple procedure rules. The encounter includes a single surgical procedure, so multiple procedure discount rules do not apply.

[0356] Step 5 - Stop-Loss Rules: The system evaluates stop-loss rules. The calculated base payment of $1 1,391.29 does not exceed the stop-loss threshold of $250,000. No stop-loss adjustment is applied.

[0357] Step 6 - Lesser-Of Rules (Lowest Priority): The system applies lesser-of rules to select the appropriate payment amount. The lesser-of rule compares the calculated contract payment to billed charges less the carved-out amount: $78,450.00 — $4,200.00 = $74,250.00. The calculated DRG payment of $11,391.29 is less than the adjusted billed charges of $74,250.00, so the DRG payment amount is selected.

[0358] The payment calculator generates the expected reimbursement result comprising:Docket No.: BLUE-01 -PCTTable 3: Expected Reimbursement Result Components Payment Component AmountDRG Base Payment $11,391.29Carve-Out Payment (Implantable Device) $3,570.00Total Expected Reimbursement $14,961.29

[0359] The system generates an audit trail with rule-by-rule impact analysis linking each applied payment rule to the corresponding source location in the healthcare payer contract document. The execution path record documents the complete calculation sequence from patient encounter data input to expected reimbursement output.

[0360] In this example, the healthcare contract analysis system compares expected reimbursement results to actual reimbursement amounts indicated in remittance data to identify payment variances and generate underpayment signals. The system processes a population of 2,847 patient encounters associated with a healthcare payer contract over a 90-day period.

[0361] For each patient encounter, the system applies compiled contract rules to calculate expected reimbursement and associates the encounter record with corresponding remittance data received via EDI XI 2835 transaction. The system determines encounterlevel variance between expected reimbursement and actual reimbursement amounts.

[0362] The variance analysis identifies the following results across the encounter population:Table 4: Variance Analysis Results Across Encounter Population Variance Category Encounter Count Aggregate Variance Within Tolerance (±$5.00) 2,412 $0.00Overpayment (>$5.00) 89 ±$12,847.50 Underpayment (<-$5.00) 346 -$287,432.18

[0363] The system applies configurable variance threshold detection to identify encounters for which variance exceeds a defined threshold. Using a threshold of $100.00 or 5% of expected reimbursement (whichever is greater), the system identifies 127 encounters with significant underpayment indicators.Docket No.: BLUE-01 -PCT

[0364] For a representative underpaid encounter, the system generates the following variance analysis:Table 5: Variance Analysis for Representative Underpaid Encounter Analysis Element ValueEncounter ID ENC-2025 -0847291Service Date 02 / 28 / 2025Expected Reimbursement $8,742.50Actual Payment $6,218.00Variance Amount -$2,524.50Variance Percentage -28.9%

[0365] The underpayment detection module classifies the underpayment according to root cause categories. The system analyzes the variance to identify which payment rule or payment component contributed to the underpayment. For this encounter, the root cause classification identifies "Multiple Procedure Discount Error" as the primary cause — the payer applied a 50% discount to the secondary procedure when the contract specifies a 25% discount for the applicable procedure code category.

[0366] The system generates appeal documentation comprising rule-based payment rationale and contract rule citations. The appeal documentation includes:

[0367] - Expected reimbursement calculation showing step-by-step application of payment rules

[0368] - Contract citation identifying Section 4.3.2, paragraph (b) specifying the 25% multiple procedure discount for the applicable procedure category

[0369] - Variance calculation demonstrating the $2,524.50 underpayment

[0370] - Supporting encounter data including procedure codes, service dates, and billing form information

[0371] The system generates population-level underpayment analytics identifying aggregate recovery opportunity of $287,432.18 across 346 underpaid encounters. The analytics include root cause distribution showing 42% of underpayments attributed to multiple procedure discount errors, 28% to fee schedule rate discrepancies. 18% to carve-out provision misapplication, and 12% to other causes.Docket No.: BLUE-01 -PCT

[0372] Example 3: Contract Negotiation Simulation and Financial Impact Analysis

[0373] In this example, the healthcare contract analysis system models the financial effects of proposed contract term modifications to support contract negotiation preparation. A healthcare facility prepares to renegotiate a payer agreement and uses the system to evaluate the financial impact of proposed changes.

[0374] The system receives proposed modifications to structured payment terms comprising:Table 6: Proposed Contract Term ModificationsProposed Modification Current Value Proposed Value DRG Base Rate $7,842.50 $8,150.00 (3.9% increase) APC Payment Rate 85% of Medicare APC 90% of Medicare APC Per Diem Rate (SNF) $1,250.00 $1,375.00 (10% increase) Stop-Loss Threshold $250,000 $200,000Implant Carve-Out Rate 85% of charges 90% of charges

[0375] The contract modeling module generates a modified set of machineexecutable payment rules incorporating the proposed modifications. The system applies the modified set of machine-executable payment rules to a historical encounter population comprising 12,847 patient encounters from the prior 12-month period.

[0376] The system calculates aggregate financial impact by comparing reimbursement outcomes under the current payment rules and the modified payment rules:Table 7: Aggregate Financial Impact of Proposed Modifications Impact Metric Current Rules Modified Rules Difference Total Expected$47,842,156.00 $49,687,423.00 +$1,845,267.00 ReimbursementAverageReimbursement $3,724.18 $3,867.82 +$143.64 per EncounterPercentage Change — — +3.86%Docket No.: BLUE-01 -PCT

[0377] The system generates sen-ice line impact analysis evaluating the financial effects across different service categories:Table 8: Service Line Impact AnalysisCurrent ModifiedService Line Impact Reimbursement Reimbursement+$710,625.00 Inpatient Surgical $18,247,500.00 $18,958,125.00(+3.9%) +$474,036.00 Inpatient Medical $12,156,000.00 $12,630,036.00(+3.9%) +$524,520.00 Outpatient Surgery $8,742,000.00 $9,266,520.00(+6.0%) +$412,500.00 Skilled Nursing $4,125,000.00 $4,537,500.00(+10.0%) -$276,414.00 (- Other Services $4,571,656.00 $4,295,242.006.0%)

[0378] The system identifies that the proposed stop-loss threshold reduction from $250,000 to $200,000 results in decreased reimbursement for 23 high-cost cases, contributing to the negative impact in the "Other Services" category. The system generates a contract negotiation report comprising the aggregate financial impact, service line breakdowns, and identification of specific term modifications contributing to positive and negative reimbursement changes.

[0379] Example 4: BlueCard Multi-Payer Comparison and Billing Entity Optimization

[0380] In this example, the healthcare contract analysis system evaluates out-of-state Blue plan patient encounters to determine optimal billing entity selection. A healthcare facility located in California, where separate Blue Cross and Blue Shield entities operate, receives patients with Blue Cross Blue Shield coverage from other states.

[0381] The system receives contract materials corresponding to both the Blue Cross of California contract and the Blue Shield of California contract. The system derives executable payment logic for each contract, generating a first set of machine-executableDocket No.: BLUE-01 -PCTpayment rules from the Blue Cross contract and a second set of machine-executable payment rules from the Blue Shield contract.

[0382] For an out-of-state patient encounter with the following characteristics, the system performs multi-payer calculation:Table 9: Out-of-State Patient Encounter Characteristics Encounter Attribute ValuePatient Home Plan Blue Cross Blue Shield of Texas Service Type Outpatient SurgeryPrincipal Procedure CPT 27447 (Total knee arthroplasty) Total Charges $45,750.00Implant Charges $12,400.00Service Date 04 / 10 / 2025

[0383] The system applies the Blue Cross rule set and the Blue Shield rule set to the patient encounter data concurrently:Table 10: Blue Cross and Blue Shield Payment Comparison Calculation Component Blue Cross Payment Blue Shield Payment Base Procedure Payment $8,425.00 $9,150.00$10,540.00 (85% ofImplant Carve-Out $9,920.00 (80% of charges) charges)Facility Fee $2,100.00 $2,450.00Total Expected$21,065.00 $21,520.00 ReimbursementReimbursement Difference — +$455.00

[0384] The comparative analysis module performs multi-dimensional comparison of expected reimbursement results. The system evaluates decision factors including:

[0385] - Payment Amount: Blue Shield payment exceeds Blue Cross payment by $455.00 (2.2% higher)

[0386] - Processing Time: Historical data indicates average processing time of 18 days for Blue Cross and 22 days for Blue Shield

[0387] - Denial Risk: Historical denial rate of 3.2% for Blue Cross and 2.8% forDocket No.: BLUE-01 -PCTBlue Shield for comparable procedures

[0388] The optimization decision module generates a billing entity recommendation based on the evaluated decision factors. Using a configured optimization target of maximizing payment amount, the system recommends billing Blue Shield of California for this encounter, with an expected reimbursement improvement of $455.00.

[0389] The system performs batch BlueCard optimization across a population of 847 out-of-state Blue plan encounters from the prior quarter. The aggregate optimization analysis identifies:Table 11: Batch BlueCard Optimization Analysis Results Optimization Metric ValueEncounters with Blue Cross Advantage 312 (36.8%)Encounters with Blue Shield Advantage 489 (57.7%)Encounters with No Significant Difference 46 (5.4%)Aggregate Optimization Opportunity $127,845.00Average Optimization per Encounter $150.94

[0390] The system generates BlueCard optimization reports including encounterlevel billing entity recommendations, aggregate optimization opportunity analyses, and trend analyses of Blue entity payment differences by service type and patient home plan state.

[0391] Example 5: Shadow Calculation for Proposed Rule Modifications

[0392] In this example, the healthcare contract analysis system performs shadow calculation to evaluate proposed rule modifications against a historical encounter population without persisting results. A healthcare faci 1 i ty considers implementing a modified fee schedule interpretation and uses shadow calculation to assess the financial impact before committing changes to the production rule set.

[0393] The system receives a proposed rule modification comprising an updated interpretation of the multiple procedure discount provision. The current rule applies a 50% discount to the second procedure and 75% discount to subsequent procedures. The proposed modification applies a 50% discount to the second procedure. 50% discount to the third procedure, and 75% discount to the fourth and subsequent procedures, based on updated contract language interpretation.Docket No.: BLUE-01 -PCT

[0394] The system generates a temporary modified rule set incorporating the proposed rule modification. The shadow calculation module applies the temporary modified rule set to a historical encounter population comprising 4,218 patient encounters with multiple procedures from the prior 6-month period. The shadow calculation executes in an isolated calculation environment without storing results in the production results store or modifying the original payment rules in the rule repository.

[0395] The shadow calculation generates comparison results:Table 12: Shadow Calculation Comparison Results Comparison Metric Current Rules Proposed Rules Difference Total Expected$8,742,156.00 $8,891,423.00 +$149,267.00 ReimbursementAffected Encounters — 1,247 —Average Impact per— +$119.70 —Affected Encounter

[0396] The system generates a shadow calculation report identifying encounters with significant reimbursement changes under the proposed rule modification. The report identifies 89 encounters with reimbursement increases exceeding $500.00 and 12 encounters with reimbursement decreases (due to edge cases in the rule interaction). The shadow calculation report includes recommendations for whether to implement the proposed rule modification based on the calculated financial impact and identifies specific encounter patterns that may require additional review before implementation.

[0397] Other Embodiments

[0398] The detailed description set forth above is provided to aid those skilled in the art in practicing the present invention. However, the invention described and claimed herein is not to be limited in scope by the specific embodiments herein disclosed because these embodiments are intended as illustration of several aspects of the invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description which do not depart from the spirit or scope of the present inventive discover}'. Such modifications are also intended toDocket No.: BLUE-01 -PCTfall within the scope of the appended claims.

[0399] References Cited

[0400] All publications, patents, patent applications and other references cited in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication, patent, patent application or other reference was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. Citation of a reference herein shall not be construed as an admission that such is prior art to the present invention.

Claims

Docket No.: BLUE-01 -PCTCLAIMSWhat is claimed is:

1. A healthcare contract analysis system, comprising:one or more processors; andone or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to:receive a healthcare payer contract document;extract structured payment terms from the healthcare payer contract document; compile the structured payment terms into a set of machine-executable payment rules, wherein each payment rule comprises typed parameters, one or more applicability conditions, and an execution priority;receive patient encounter data;apply the set of machine-executable payment rules to the patient encounter data according to the execution priorities to generate an expected reimbursement result; andgenerate an output based on the expected reimbursement result.

2. The system according to claim 1, wherein the instructions further cause the system to:validate the structured payment terms against one or more validation criteria; and normalize the structured payment terms to a standardized format prior to compiling the structured payment terms into the set of machine-executable payment rules.

3. The system according to any one of claims 1 to 2, wherein extracting the structured payment terms comprises:preprocessing the healthcare payer contract document to identify document structure including sections, tables, and appendices;performing semantic analysis on the preprocessed document to identify paymentrelevant content; andextracting payment term data from the identified payment-relevant content.

4. The system according to any one of claims 1 to 3, wherein extracting the structured payment terms comprises processing the healthcare payer contract document using a large language model configured with healthcare payment domain context.

5. The system according to any one of claims 1 to 4, wherein the healthcare payment domain context comprises one or more of:Docket No.: BLUE-01 -PCTa healthcare payment terminology dictionary';contract structure templates;payment methodology patterns: andpayer-specific term definitions.

6. The system according to any one of claims 1 to 5, wherein the instructions further cause the system to:determine a confidence score for each extracted structured payment term; and route structured payment terms having confidence scores below a threshold to a human review workflow.

7. The system according to any one of claims 1 to 6, wherein the ty ped parameters comprise one or more of:base rates;multipliers;discount percentages;carve-out provisions;stop-loss thresholds; andoutlier provisions.

8. The system according to any one of claims 1 to 7, wherein the one or more applicability conditions comprise one or more of:service type constraints;diagnosis code requirements;procedure code applicability;date range limitations;patient type restrictions; andfacility-specific terms.

9. The system according to any one of claims 1 to 8, wherein the set of machineexecutable payment rules comprises one or more rule types selected from:base payment rules;discount rules;carve-out rules;stop-loss rules;outlier adjustment rules;multiple procedure discount rules; andlesser-of rules.Docket No.: BLUE-01 -PCT10. The system according to any one of claims 1 to 9, wherein applying the set of machine-executable payment rules according to the execution priorities comprises applying rules in a hierarchical sequence comprising:applying carve-out rules;apply ing base payment rules;applying discount rules;applying multiple procedure rules;applying stop-loss rules; andapplying lesser-of rules.

11. The system according to any one of claims 1 to 10, wherein the base payment rules implement one or more payment methodologies selected from:diagnosis-related group based payment;ambulatory payment classification based payment;fee schedule based payment;per diem payment; andpercentage-of-charge payment.

12. The system according to any one of claims 1 to 11, wherein the instructions further cause the system to:receive remittance data associated with the patient encounter data;compare the expected reimbursement result to an actual reimbursement amount indicated in the remittance data;determine a variance between the expected reimbursement result and the actual reimbursement amount; andgenerate an underpayment indication when the variance exceeds a threshold.

13. The system according to any one of claims 1 to 12, wherein the instructions further cause the system to:classify the variance according to one or more underpayment categories; and generate appeal documentation based on the classification.

14. The system according to any one of claims 1 to 13, wherein the instructions further cause the system to:receive a proposed modification to one or more of the structured payment terms; compile the proposed modification into a modified set of machine-executable payment rules;apply the modified set of machine-executable payment rules to a historical encounterDocket No.: BLUE-01 -PCTpopulation; andgenerate a financial impact analysis comparing reimbursement outcomes under the set of machine-executable payment rules and the modified set of machine-executable payment rules.

15. The system according to any one of claims 1 to 14, wherein the instructions further cause the system to:compile a first set of machine-executable payment rules from a first healthcare payer contract;compile a second set of machine-executable payment rules from a second healthcare payer contract;apply the first set of machine-executable payment rules to the patient encounter data to generate a first expected reimbursement result;apply the second set of machine-executable payment rules to the patient encounter data to generate a second expected reimbursement result; andgenerate a comparative analysis identifying a reimbursement difference between the first expected reimbursement result and the second expected reimbursement result.

16. The system according to any one of claims 1 to 15, wherein the first healthcare payer contract is associated with a first Blue Cross Blue Shield entity' and the second healthcare payer contract is associated with a second Blue Cross Blue Shield entity, and wherein the comparative analysis comprises a billing entity recommendation.

17. The system according to any one of claims 1 to 16, wherein the comparative analysis further comprises evaluation of one or more decision factors selected from:payment amount;processing time; anddenial risk.

18. The system according to any one of claims 1 to 17, wherein the instructions further cause the system to:generate an audit trail comprising rule-by-rule impact analysis linking each applied payment rule to a corresponding portion of the healthcare payer contract document.

19. The system according to any one of claims 1 to 18, wherein compiling the structured payment terms into the set of machine-executable payment rules comprises:generating a rule dependency graph; anddetermining the execution priority for each payment rule based on the rule dependency graph.Docket No.: BLUE-01 -PCT20. The system according to any one of claims 1 to 19, wherein the instructions further cause the system to:detect an unresolved dependency in the structured payment terms, wherein the unresolved dependency comprises a reference to an unresolved fee schedule, exhibit, or attachment; andblock compilation of a payment rule associated with the unresolved dependency until the unresolved dependency is resolved.

21. The system according to any one of claims 1 to 20, wherein the instructions further cause the system to:maintain a plurality of fee schedule versions associated with the healthcare payer contract;determine an encounter date from the patient encounter data; andselect a fee schedule version from the plurality of fee schedule versions based on the encounter date.

22. The system according to any one of claims 1 to 21, wherein the instructions further cause the system to:receive an updated extraction of structured payment terms from the healthcare payer contract document;compare the updated extraction to previously compiled payment rules; and reconcile the updated extraction with the previously compiled payment rules while preserving manually adjusted rule parameters.

23. The system according to any one of claims 1 to 22, wherein the instructions further cause the system to:receive a proposed set of payment rules;apply the proposed set of payment rules to a historical encounter population in a shadow calculation mode without persisting results; andgenerate a comparison between outcomes of the proposed set of payment rules and outcomes of the set of machine-executable payment rules.

24. The system according to any one of claims 1 to 23, wherein the patient encounter data comprises one or more of:patient demographic information;diagnosis codes;procedure codes;service dates;Docket No.: BLUE-01 -PCTfacility identifiers;billing form type; andcharge amounts.

25. The system according to any one of claims 1 to 24, wherein the output comprises one or more of:an expected payment amount;a patient financial responsibility estimate;a reimbursement variance report; anda contract performance analysis.

26. A method for converting healthcare contract language into executable reimbursement logic, comprising:receiving, by one or more processors, a healthcare payer contract document; extracting, by the one or more processors, structured payment terms from the healthcare payer contract document;validating and normalizing, by the one or more processors, the structured payment terms;compiling, by the one or more processors, the validated and normalized structured payment terms into a set of machine-executable payment rules, wherein each payment rule comprises typed parameters, one or more applicability conditions, and an execution priority;receiving, by the one or more processors, patient encounter data;identifying, by the one or more processors, applicable payment rules from the set of machine-executable payment rules based on the patient encounter data and the one or more applicability conditions;applying, by the one or more processors, the applicable payment rules to the patient encounter data according to the execution priorities to generate an expected reimbursement result; andoutputting, by the one or more processors, the expected reimbursement result.

27. The method according to claim 26, wherein extracting the structured payment terms comprises:performing optical character recognition on the healthcare payer contract document; identifying structural elements including sections, tables, and fee schedules; segmenting the healthcare payer contract document based on semantic boundaries; andDocket No.: BLUE-01 -PCTextracting payment term data from identified payment sections.

28. The method according to any one of claims 26 to 27, wherein extracting the structured payment terms comprises:providing the healthcare payer contract document to a large language model; providing healthcare payment domain context to the large language model; and receiving extracted payment terms from the large language model.

29. The method according to any one of claims 26 to 28, wherein validating and normalizing the structured payment terms comprises:standardizing terminology to an internal schema;converting textual amounts to decimal values;standardizing percentage representations; andperforming consistency checks across extracted terms.

30. The method according to any one of claims 26 to 29, wherein compiling the validated and normalized structured payment terms comprises:assigning each payment rule to a rule category;defining typed parameters for each payment rule;defining one or more applicability conditions for each payment rule; and assigning an execution priority' to each payment rule.

31. The method according to any one of claims 26 to 30, wherein the rule category is selected from:diagnosis-related group based reimbursement;ambulatory' payment classification based reimbursement;fee schedule reimbursement;per diem reimbursement;percentage-of-charge reimbursement;carve-out rules;outlier adjustments;stop-loss rules;lesser-of rules; andmultiple procedure adjustments.

32. The method according to any one of claims 26 to 31, wherein applying the applicable payment rules according to the execution priorities comprises:applying carve-out rules to identify services subject to separate payment terms; applying base payment rules to establish a foundational reimbursement calculation;Docket No.: BLUE-01 -PCTapplying discount rules to modify the foundational reimbursement calculation; applying multiple procedure rules to adjust reimbursement for multiple procedures performed during a same encounter;applying stop-loss rules to limit payment adjustments; andapplying lesser-of rules to select an appropriate payment amount based on contract terms.

33. The method according to any one of claims 26 to 32, further comprising: receiving remittance data corresponding to the patient encounter data; determining an actual reimbursement amount from the remittance data; calculating a variance between the expected reimbursement result and the actual reimbursement amount; andgenerating an underpayment signal when the variance indicates the actual reimbursement amount is less than the expected reimbursement result by more than a threshold amount.

34. The method according to claim 33. further comprising:classifying a root cause of the underpayment; andgenerating appeal evidence documentation based on the root cause classification and the applicable payment rules.

35. The method according to any one of claims 26 to 34, further comprising: receiving a proposed contract term modification;generating a modified set of machine-executable payment rules incorporating the proposed contract term modification;applying the modified set of machine-executable payment rules to a historical encounter population;calculating aggregate financial impact of the proposed contract term modification; and generating a contract negotiation report comprising the aggregate financial impact.

36. The method according to any one of claims 26 to 35, further comprising: compiling a first set of machine-executable payment rules from a first healthcare payer contract;compiling a second set of machine-executable payment rules from a second healthcare payer contract;applying the first set of machine-executable payment rules to an encounter to generate a first expected reimbursement;applying the second set of machine-executable payment rules to the encounter toDocket No.: BLUE-01 -PCTgenerate a second expected reimbursement;comparing the first expected reimbursement and the second expected reimbursement; andgenerating a payer routing recommendation based on the comparison.

37. The method according to claim 36, wherein the first healthcare payer contract is associated with a Blue Cross entity and the second healthcare payer contract is associated with a Blue Shield entity, and wherein the payer routing recommendation identifies which entity to bill for an out-of-state patient.

38. The method according to any one of claims 26 to 37, further comprising: generating an execution path record tracking each payment rule applied to the patient encounter data; andstoring the execution path record for audit purposes.

39. The method according to any one of claims 26 to 38, further comprising: detecting an unresolved reference to a fee schedule, exhibit, or attachment in the structured payment terms; andblocking compilation of an associated payment rule until the unresolved reference is resolved.

40. The method according to any one of claims 26 to 39, further comprising: maintaining a plurality of versioned fee schedules;determining an encounter date from the patient encounter data; andselecting an applicable fee schedule version based on the encounter date.

41. The method according to any one of claims 26 to 40, further comprising: receiving an updated extraction of structured payment terms;identifying differences between the updated extraction and previously compiled payment rules;preserving manually adjusted parameters in the previously compiled payment rules; andupdating the previously compiled payment rules with non-conflicting terms from the updated extraction.

42. The method according to any one of claims 26 to 41, further comprising: performing batch calculation of expected reimbursement results across a population of patient encounters; andgenerating population-level reimbursement analytics.

43. The method according to any one of claims 26 to 42, wherein the expectedDocket No.: BLUE-01 -PCTreimbursement result comprises:a calculated payment amount;an identification of each payment rule applied; anda contribution of each applied payment rule to the calculated payment amount.

44. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:receiving a healthcare payer contract document;extracting structured payment terms from the healthcare payer contract document using a term extraction module;compiling the structured payment terms into a set of machine-executable payment rules using a rule compilation module, wherein each payment rule comprises:typed parameters defining payment amounts, percentages, or thresholds; one or more applicability conditions defining when the payment rule applies; andan execution priority defining an order of application relative to other payment rules;storing the set of machine-executable payment rules in a rule repository'; receiving patient encounter data;applying the set of machine-executable payment rules to the patient encounter data using a calculation engine, wherein the calculation engine applies the payment rules according to the execution priorities; andgenerating an expected reimbursement output.

45. The non-transitory computer-readable medium according to claim 44. wherein the term extraction module comprises a large language model configured with healthcare reimbursement domain context.

46. The non-transitory' computer-readable medium according to any one of claims 44 to 45, wherein the operations further comprise:validating the structured payment terms using a validation module; and normalizing the structured payment terms to a standardized representation using a normalization module.

47. The non-transitory computer-readable medium according to any one of claims 44 to 46, wherein the operations further comprise:determining a confidence score for each extracted structured payment term;Docket No.: BLUE-01 -PCTcomparing the confidence score to a confidence threshold; androuting structured payment terms having confidence scores below the confidence threshold to a human review interface.

48. The non-transitory computer-readable medium according to any one of claims 44 to 47, wherein the set of machine-executable payment rules comprises:carve-out rules having a first execution priority;base payment rules having a second execution pnority lower than the first execution priority;discount rules having a third execution priority lower than the second execution priority;multiple procedure rules having a fourth execution priority lower than the third execution priority;stop-loss rules having a fifth execution priority lower than the fourth execution priority; andlesser-of rules having a sixth execution priority lower than the fifth execution priority.

49. The non-transitory computer-readable medium according to any one of claims 44 to 48, wherein the base payment rules implement one or more healthcare payment methodologies selected from:diagnosis-related group based payment methodology;ambulatory payment classification based payment methodology;fee schedule based payment methodology;per diem payment methodology; andpercentage-of-charge payment methodology.

50. The non-transitory computer-readable medium according to any one of claims 44 to 49, wherein the operations further comprise:receiving remittance data associated with the patient encounter data; determining an actual payment amount from the remittance data;calculating a variance between the expected reimbursement output and the actual payment amount;determining whether the variance exceeds a variance threshold; andgenerating an underpayment alert when the variance exceeds the variance threshold.

51. The non-transitory computer-readable medium according to claim 50, wherein the operations further comprise:classifying the underpayment according to a root cause category; andDocket No.: BLUE-01 -PCTgenerating appeal documentation comprising contract rule citations supporting the expected reimbursement output.

52. The non-transitory computer-readable medium according to any one of claims 44 to 51, wherein the operations further comprise:receiving a contract modification proposal;generating a modified set of machine-executable payment rules based on the contract modification proposal;applying the modified set of machine-executable payment rules to a historical encounter dataset;calculating a financial impact of the contract modification proposal; and generating a contract simulation report comprising the financial impact.

53. The non-transitory computer-readable medium according to any one of claims 44 to 52, wherein the operations further comprise:compiling a first set of machine-executable payment rules from a first payer contract; compiling a second set of machine-executable payment rules from a second payer contract;applying the first set of machine-executable payment rules to the patient encounter data to generate a first expected reimbursement;applying the second set of machine-executable payment rules to the patient encounter data to generate a second expected reimbursement;performing a comparative analysis of the first expected reimbursement and the second expected reimbursement; andgenerating a billing optimization recommendation based on the comparative analysis.

54. The non-transitory computer-readable medium according to claim 53. wherein the first payer contract is associated with a first Blue Cross Blue Shield entity and the second payer contract is associated with a second Blue Cross Blue Shield entity, and wherein the billing optimization recommendation identifies an optimal billing entity for an out-of-state patient encounter.

55. The non-transitory computer-readable medium according to any one of claims 53 to 54, wherein the comparative analysis evaluates one or more factors selected from: expected payment amount;estimated processing time; andestimated denial risk.

56. The non-transitory computer-readable medium according to any one of claimsDocket No.: BLUE-01 -PCT44 to 55, wherein the operations further comprise:generating an audit trail linking each applied payment rule to a source location in the healthcare payer contract document.

57. The non-transitory computer-readable medium according to any one of claims 44 to 56, wherein the operations further comprise:detecting an unresolved dependency in the structured payment terms;blocking compilation of a payment rule dependent on the unresolved dependency; and generating a notification indicating the unresolved dependency.

58. The non-transitory computer-readable medium according to any one of claims 44 to 57, wherein the operations further comprise:storing a lurality of fee schedule versions with associated effective date ranges; determining an encounter date from the patient encounter data; andselecting an applicable fee schedule version based on the encounter date falling within an associated effective date range.

59. The non-transitory computer-readable medium according to any one of claims 44 to 58, wherein the operations further comprise:receiving updated structured payment terms extracted from the healthcare payer contract document;comparing the updated structured payment terms to previously stored payment rules; identifying manually adjusted parameters in the previously stored payment rules; and reconciling the updated structured payment terms with the previously stored payment rules while preserving the manually adjusted parameters.

60. The non-transitory computer-readable medium according to any one of claims 44 to 59, wherein the operations further comprise:receiving a proposed rule modification;executing a shadow calculation applying the proposed rule modification to a historical encounter population without persisting results; andgenerating a shadow calculation report comparing outcomes under the proposed rule modification to outcomes under current payment rules.

61. A healthcare reimbursement optimization system, comprising:a contract ingestion module configured to receive healthcare payer contract documents in one or more document formats;a term extraction module configured to extract structured payment terms from the healthcare payer contract documents;Docket No.: BLUE-01 -PCTa validation module configured to validate the structured payment terms against validation criteria and normalize the structured payment terms;a rule compilation module configured to compile the validated and normalized structured payment terms into machine-executable payment rules, wherein each machine-executable payment rule comprises ty ped parameters, applicability conditions, and an execution priority;a rule repository configured to store the machine-executable payment rules;a calculation engine configured to:receive patient encounter data;identify applicable payment rules from the rule repository based on the patient encounter data and the applicability conditions;apply the applicable payment rules to the patient encounter data according to the execution priorities; andgenerate expected reimbursement results; andan output module configured to generate outputs based on the expected reimbursement results.

62. The system according to claim 61, wherein the term extraction module comprises a large language model processor configured with healthcare payment domain context including one or more of:healthcare payment terminology;contract structure templates;payment methodology patterns; andpayer-specific definitions.

63. The system according to any one of claims 61 to 62. further comprising: a confidence scoring module configured to determine confidence scores for extracted structured payment terms; anda review interface configured to present structured payment terms having confidence scores below a threshold for human review.

64. The system according to any one of claims 61 to 63, wherein the machineexecutable payment rules comprise rule types including:carve-out rules configured to identify services subject to separate payment terms; base payment rules configured to calculate foundational reimbursement amounts using one or more payment methodologies;discount rules configured to apply contractual discounts to calculated amounts;Docket No.: BLUE-01 -PCTmultiple procedure rules configured to adjust reimbursement for multiple procedures; stop-loss rules configured to limit payment adjustments; andlesser-of rules configured to select payment amounts based on comparative calculations.

65. The system according to any one of claims 61 to 64, wherein the calculation engine is configured to apply the applicable payment rules in a hierarchical sequence comprising:applying can e-out rules at a highest priority:applying base payment rules;applying discount rules;applying multiple procedure rules;applying stop-loss rules; andapplying lesser-of rules at a lowest priority.

66. The system according to any one of claims 61 to 65, wherein the base payment rules implement payment methodologies including one or more of:diagnosis-related group based payment;ambulatory' payment classification based payment;fee schedule based payment;per diem payment; andpercentage-of-charge payment.

67. The system according to any one of claims 61 to 66, further comprising: a variance analysis module configured to:receive remittance data associated with the patient encounter data; compare the expected reimbursement results to actual reimbursement amounts from the remittance data;calculate variance between expected and actual reimbursement; and generate underpayment signals when variance exceeds a threshold.

68. The system according to claim 67, wherein the variance analysis module is further configured to:classify underpayments according to root cause categories; andgenerate appeal documentation comprising rule-based payment rationale.

69. The system according to any one of claims 61 to 68, further comprising: a contract modeling module configured to:receive proposed modifications to payment terms;Docket No.: BLUE-01 -PCTgenerate modified payment rules incorporating the proposed modifications; apply the modified payment rules to historical encounter data; and generate financial impact analyses comparing outcomes under current and modified payment rules.

70. The system according to any one of claims 61 to 69, further comprising: a multi-payer comparison module configured to:compile payment rules from a plurality of payer contracts;apply each set of payment rules to common encounter data;compare expected reimbursement results across the plurality of payer contracts; andgenerate payer routing recommendations.

71. The system according to claim 70, wherein the plurality of payer contracts comprise contracts from different Blue Cross Blue Shield entities, and wherein the payer routing recommendations identify an optimal billing entity for out-of-state patient encounters.

72. The system according to any one of claims 61 to 71, further comprising: an audit module configured to generate audit trails linking applied payment rules to source contract provisions.

73. The system according to any one of claims 61 to 72, wherein the rule compilation module is configured to:detect unresolved dependencies in the structured payment terms; andblock compilation of payment rules having unresolved dependencies until the dependencies are resolved.

74. The system according to any one of claims 61 to 73, wherein the rule repository is configured to:store a plurality- of fee schedule versions with associated effective date ranges; and wherein the calculation engine is configured to select an applicable fee schedule version based on an encounter date.

75. The system according to any one of claims 61 to 74, wherein the rule compilation module is configured to:receive updated extracted payment terms;compare the updated extracted payment terms to previously compiled payment rules; preserve manually adjusted parameters in the previously compiled payment rules; and update the previously compiled payment rules with non-conflicting terms from the updated extracted payment terms.Docket No.: BLUE-01 -PCT76. A healthcare contract reimbursement analysis system, comprising:a contract ingestion module configured to receive healthcare payer contract documents;a term extraction module configured to extract structured payment terms from the healthcare payer contract documents using a language model processor;a validation and normalization module configured to validate and normalize the structured payment terms;a rule compilation module configured to compile the validated and normalized structured payment terms into machine-executable payment rules, wherein each machine-executable payment rule comprises typed parameters, applicability conditions, and an execution priority;a rule repository configured to store the machine-executable payment rules;a calculation engine configured to:receive patient encounter data;identify applicable payment rules from the rule repository based on the patient encounter data;apply the applicable payment rules to the patient encounter data according to the execution priorities; andgenerate expected reimbursement results;a variance analysis module configured to compare the expected reimbursement results to actual reimbursement amounts and generate underpayment signals;a contract modeling module configured to generate financial impact analyses for proposed modifications to payment terms; andan output module configured to generate outputs based on the expected reimbursement results.

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