Prescription Claims Analytics for Prepayment Billing Accuracy
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Solution Overview
Problem
Pharmacy claims are often billed without thorough review, leading to errors that are difficult to detect due to the volume of claims and delayed reconciliation processes, resulting in significant financial losses for plan sponsors and additional costs from legal settlements.
Innovation Solution
A system and method for scanning claims data that includes parsing transactions, applying analytics to identify material errors, and withholding payments for claims exceeding tolerance levels, using a portal for real-time monitoring and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pharmacy claims are billed without review prior to payment, then processing speed is improved, but billing accuracy deteriorates and errors are not apparent
Solution Approach 1:
The system performs preliminary scanning and analysis of claims data before payment is finalized. The computing system receives claims data, scans it for errors using configured criteria, and identifies discrepancies prior to payment processing, allowing corrections to be made in advance rather than discovering errors months later during audits.
Solution Approach 2:
The system provides continuous feedback by scanning claims data and comparing it against contracted pricing terms, benefit design, and fee schedules. Error reports are generated and provided to plan sponsors in real-time or near-real-time, enabling them to review and correct errors before payment is completed, creating a closed-loop feedback system.
2Device complexity
If reconciliation is performed months after contract year close, then processing complexity is reduced, but time to identify errors deteriorates
Solution Approach 1:
The system performs error identification continuously throughout the contract year rather than waiting until months after year-end. Claims data is scanned and analyzed in real-time or near-real-time, allowing errors to be identified immediately when they occur rather than delaying detection until post-year-end reconciliation.
Solution Approach 2:
The scanning and error identification process operates continuously throughout the contract year, with the computing system constantly monitoring claims data against contracted terms. This continuous monitoring replaces the traditional batch processing approach that only occurs periodically or annually.
3Measurement precision
If representative sample audits are conducted by external firms, then measurement coverage is improved, but cost and time delays increase
Solution Approach 1:
The plan sponsor performs the auditing function themselves using the automated scanning system rather than hiring external audit firms. The computing system scans all claims data against contracted terms and generates error reports that the plan sponsor can review and act upon directly, eliminating the need for external consultants and significantly reducing both cost and time.
Solution Approach 2:
The system performs comprehensive error identification throughout the year rather than waiting for annual audits. By continuously scanning claims data and identifying errors in real-time, the system provides complete measurement coverage without the time delays and costs associated with post-year-end external audits.
4Productivity
If PBM reconciliation is delayed until after payment, then processing efficiency is improved, but fund recovery for errors deteriorates
Solution Approach 1:
The system identifies errors in claims data before payment is processed. By scanning claims against contracted pricing terms, benefit design, and fee schedules prior to payment, the system allows plan sponsors to correct errors and adjust payments in advance, ensuring fund recovery is possible rather than discovering errors months after payment has been made.
Solution Approach 2:
The system provides feedback about errors in claims data before payment is finalized, enabling plan sponsors to review the error reports and make necessary corrections. This pre-payment feedback loop ensures that erroneous payments can be prevented or recovered, maintaining both processing efficiency and fund recovery capability.
Data Source
AI summary
A method and system for scanning claims data, the method comprising receiving, by a computing system, contract details associated with claims, receiving, by the computer system, prescription transactions data associated with the claims, parsing, by the computer system, the transactions data into column values, and executing, by the computer system, analytics on the parsed transactions. The analytics include comparing the parsed transactions against the contract details for accurate pricing, fees, and accepted treatment protocols concerning dose and quantity, comparing the parsed transactions with independent pricing data that is based on third-party pricing data and drug information, identifying material errors in the parsed transactions, determining withholdings from payment of the claims for a portion above a tolerance level based on the identified material errors. The method further comprising presenting, by the computer system, results of the execution of the analytics to a portal.