Use Claim Data Analysis for Effective Testing
Claims data is a rich source of patient information. It helps you gauge a holistic view of the patient’s interactions with the healthcare system. Analysis of claims data is a powerful asset across the payer and provider industry for activities such as retrospective and predictive analysis, identification of potential operational problems, operational realignment, identification of potential revenue losses, and overpayment and underpayment analysis. Clearing houses can also leverage this data for cross-provider benchmarking. This data and its associated analysis are valuable to product test data design (both regulatory and upgrade testing). De-identified claim data looks at the holistic picture of the usage of diagnosis and procedure codes spread across the provider billing area. This analysis helps in the design of test cases and to generate test data. Moreover, the analysis also helps understand diagnosis codes and procedure codes, generating highest revenue across locations. Key analysis leveraged during test data design and management includes:
- Highest used Diagnosis (Dx) and Procedure (Px) codes for Hospital and Professional Billing
- Dx and Px codes generating the highest revenue
- Patient volume distribution for encounters across various age groups
- Patient type and associated cost trending
The approach illustrated in the flow diagram here can help identify realistic test conditions and data for realistic and maximum test coverage. However, there are certain limitations. The claim only reflects the diagnosis observed and services documented. It does not convey information about medical history such as surgical history and family history. While this can be a major deterrent for some analytics activities, it does not impact test data identification to a vast extent. Almost all the attributes that influence 100% test coverage such as, patient diagnosis, Px, age group and gender location can be fetched from the data. In case the product requires association of additional codes to ensure complete testing (e.g. certain products support 15 or more Dx codes), it is recommended to fill the gaps by leveraging domain expertise to ensure optimal clinical and business coverage.