Pharmacy Transaction Rules Engine for Structured Data Validation
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Solution Overview
Problem
The pharmacy billing process is prone to errors and inefficiencies due to manual data entry and inconsistencies in pharmacy information systems, leading to claim rejections and suboptimal processing of prescription transactions.
Innovation Solution
A rules-based processing system that includes a rules engine and translation components to normalize and modify transaction data, adding supplementary data fields and instructions, ensuring accurate and efficient processing of pharmacy transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry is used in pharmacy information systems, then flexibility in data input is maintained, but errors and inconsistencies increase leading to claim rejections
Solution Approach 1:
The system performs preliminary validation and standardization of prescription data at the point of entry, checking for completeness and format compliance before the claim is submitted. This preliminary action prevents errors from propagating through the billing cycle and reduces claim rejections due to formatting issues.
Solution Approach 2:
A translation component acts as an intermediary between the pharmacy information system and the billing system, normalizing data formats and mediating between different data standards. This intermediary layer ensures data consistency without requiring changes to the underlying pharmacy systems.
2Productivity
If data is entered manually into the pharmacy information system, then adaptability to various input methods is maintained, but processing time and efficiency decrease
Solution Approach 1:
The system automatically performs validation, format standardization, and completeness checks without requiring manual intervention. The automated rules engine evaluates each prescription against predefined criteria and either approves or rejects claims systematically, eliminating time-consuming manual review processes.
Solution Approach 2:
The system changes the state of data from unvalidated to validated through automated rule evaluation. By transforming raw input data into standardized, validated data structures automatically, the system accelerates processing while maintaining data quality standards.
3Reliability
If comprehensive validation rules are applied to all prescription fields, then data quality and reimbursement accuracy improve, but system complexity and processing overhead increase
Solution Approach 1:
The validation process is segmented into distinct rule categories (completeness rules, format rules, business logic rules) that are applied in sequence. Each rule set focuses on specific aspects of data quality, making the overall complex validation process more manageable and maintainable while ensuring comprehensive coverage.
Solution Approach 2:
The system applies validation rules selectively based on the prescription type, payer requirements, and risk factors. Not all prescriptions undergo the full suite of validation checks - the system adjusts the level of validation applied to each claim, optimizing the balance between thoroughness and efficiency.
4Measurement precision
If claim transactions are rejected by the switch vendor or payor, then data quality issues are identified, but processing time is lost and additional manual intervention is required
Solution Approach 1:
The system incorporates feedback loops where rejection reasons from switch vendors and payors are captured and fed back into the validation rules. This feedback mechanism continuously improves the validation logic by learning from actual rejection patterns, preventing similar rejections in future claims and reducing repeat errors.
Solution Approach 2:
By applying validation rules preliminarily before submission, the system identifies and corrects data quality issues that would otherwise cause rejections. This preliminary validation prevents the claim from entering the rejection cycle, saving time that would be spent on resubmission and manual correction.
Data Source
AI summary
A rules processing system includes one or more processors and one or more memory devices storing non-transitory, computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include receiving, by an API gateway, a transaction from a pharmacy, the transaction having a first data format; reformatting, by a translation component, the transaction received by the API gateway into a structured data format used by a rules engine included in the rules processing system; evaluating, by the rules engine, one or more of fields of the transaction according to a configurable set of logic rules; inserting, into a wrapper of the transaction, supplementary data based on the evaluation of the one or more fields; reformatting, by the translation component, the transaction into the first data format; and transmitting, by the API gateway, the transaction to the pharmacy.