Clinical evidence matching compares a payer requirement with information available for a specific patient. The useful output is not merely a probability. It is a transparent link between one criterion and one or more sources, with uncertainty and missing data clearly identified.
Start with atomic criteria
Complex policy sentences often contain several conditions joined by timing, alternatives, and exclusions. Break them into criteria that can each be evaluated while preserving the logic that connects them.
Attach the policy citation, version, plan scope, and effective date to every criterion. Without that context, even a correct evidence match may answer the wrong rule.
- Normalize the required clinical concept.
- Represent value, unit, status, and time constraints.
- Model AND, OR, and exclusion logic explicitly.
- Identify criteria that require clinical judgment.
Retrieve candidates, then evaluate fit
Search structured data and documents for candidate evidence, then evaluate whether each candidate satisfies semantic, temporal, and status constraints. Separate retrieval confidence from the final review state.
The interface should show the source passage or FHIR field so reviewers can confirm the match quickly and correct it when necessary.
Evaluate at the criterion level
Measure precision and recall for individual criteria, not only whether a case was ultimately submitted. Analyze errors by data source, policy type, and requirement category.
False matches can introduce unsupported claims, while missed matches create unnecessary work. Both belong in the quality program and should inform retraining or rule changes.