Payer medical policies contain the rules that shape many prior authorization decisions, but those rules often live in long documents written for human interpretation. A payer policy graph represents the important concepts and relationships in a form that software can query without losing the source context reviewers need.
From policy document to connected criteria
A policy graph is more than a searchable document library. It breaks a policy into entities such as payer, plan, service, drug, procedure code, diagnosis, prerequisite, documentation requirement, exclusion, and effective date. Edges describe how those entities relate: a procedure requires a diagnosis, a medication follows a step-therapy sequence, or a coverage rule applies only to a specific plan and date range.
Every extracted criterion should retain a link to the policy version and source passage. That provenance lets a reviewer verify the interpretation and gives policy operations teams a concrete target when a document changes.
Why keyword search is not enough
Keyword search can find a document that mentions a code or drug, but it cannot reliably tell whether the mention is a covered indication, an exclusion, a prerequisite, or an example. It also struggles when one requirement depends on several conditions that appear in different sections of the policy.
A graph models those relationships directly. A workflow can ask which criteria apply to this plan, service, diagnosis, and date, then return a bounded set of requirements with their provenance.
- Plan and product scope, including effective and termination dates.
- CPT, HCPCS, ICD-10, drug, and other coding relationships.
- Clinical indications, exclusions, contraindications, and prerequisites.
- Required tests, notes, imaging, pathology, and treatment history.
- Step therapy, duration, dose, site-of-care, and renewal rules.
How evidence matching should work
Once applicable criteria are identified, the workflow can look for candidate evidence in structured FHIR resources and unstructured clinical documents. The result should not be a single opaque score. It should show each criterion, the evidence that may satisfy it, the source of that evidence, and the confidence or review status.
This design makes gaps operational. A missing lab becomes a lab-specific task; an uncertain treatment date becomes a confirmation request; and a policy exclusion becomes a visible escalation instead of a late denial surprise.
Governance matters as much as extraction
Policies change, and an old rule can be worse than no automation at all. A production policy graph needs versioning, effective-date logic, review states, source hashes, change detection, and ownership. Automated extraction can accelerate ingestion, but clinically and financially consequential rules need a defined validation process.
The safest pattern separates the original source, the machine-extracted representation, and the reviewed computable rule. That separation preserves the audit trail and makes disagreements resolvable.
What to measure in a policy graph pilot
Start with policies that create meaningful volume or complexity. Compare the graph output with expert review and measure criterion-level precision, missing-rule recall, time to update a changed policy, evidence-match accuracy, and first-pass authorization completeness.
The goal is not to claim that a graph replaces policy expertise. The goal is to make that expertise reusable, traceable, and available at the moment a care team needs it.