Using AI to take the paperwork out of prior authorization

The group ran forty-two clinics across six specialties, and prior authorization had become one of its largest administrative costs. Every request meant finding the payer rule that applied, gathering the supporting clinical documentation, assembling it into the payer format, and submitting through whichever channel that payer used.

The rules changed constantly and were published in different formats by every payer. Staff maintained their own reference binders and shared documents, which meant a rule change reached different clinics at different times and sometimes never reached one at all.

Roughly a third of denials were administrative rather than clinical — a missing document, a wrong code, an outdated form. Each one triggered a rework cycle that cost more staff time than the original submission and delayed patient care by days.

Clinicians were being pulled into the process to locate documentation that already existed in the record, which is the least defensible use of clinical time in the organisation.

Two constraints framed the solution. Nothing could be built that made or appeared to make a clinical determination — the system could assemble and check, never decide. And nothing could sit outside the group’s existing HIPAA and business associate governance.

The objectives: maintain payer rules in one governed place, assemble supporting documentation automatically from the record, check submissions for completeness before sending, reduce administrative denials, and keep a clinician in the approval path for every submission.

The system was built as an administrative assistant to the authorization workflow, with a strict boundary: it retrieves, assembles, checks, and flags. It does not determine medical necessity and it does not submit anything a clinician has not approved.

Payer rules were moved out of binders into a structured, versioned rule repository. A language model assists in parsing newly published payer documents into candidate structured rules, but every rule is reviewed and activated by a human specialist before it takes effect. The model accelerates maintenance; it does not own it.

For each request, a retrieval layer locates relevant clinical documentation in the record — notes, imaging reports, prior treatment history, lab results — and assembles a draft submission package against the applicable payer rule, with every included item linked back to its source in the chart.

A completeness check runs before submission, comparing the assembled package against the payer’s current requirements and flagging missing documents, code mismatches, and expired forms. This check is where most of the reduction in administrative denials came from.

The clinician review interface presents the assembled package with the supporting evidence visible and its source cited, so approval is a review of real documents rather than a rubber stamp on a system recommendation. Any edit the clinician makes is captured and used to improve assembly.

Everything is logged: which rule version applied, which documents were retrieved and why, what the completeness check found, what the clinician changed, and who approved. That record exists for audit, for payer disputes, and for the group’s own governance committee.