Prior authorization is where good care goes to wait. Before a procedure can happen, someone has to prove to a payer that it is necessary, gather the clinical evidence, submit it in the right form, and then often sit on hold for the answer. For this specialty group, that work landed on clinical and front-desk staff who spent hours a day on payer phone trees instead of with patients.

The delays compounded. Authorizations that stalled pushed procedures out by days or weeks and frustrated patients, and a meaningful share came back denied over something as small as a missing code or an outdated form.

The challenge

Could the entire authorization workflow run itself, from the moment an order is placed to the moment the patient is booked, without a person waiting on hold? The constraints were real: dozens of payers, each with its own rules, forms, and portals; clinical accuracy that leaves no room for a wrong code; and staff who needed to stay in control of the exceptions.

The approach

We built the platform around two kinds of intelligence working together. Machine-learning models read the order and the chart, assemble the right authorization packet, and match the procedure to each payer's specific rules. A conversational AI agent then does the part everyone dreads: it calls the payer, works through the phone tree, answers the questions, and captures the decision, in natural conversation, at any hour.

01
Automated packet assembly
ML pulls the order, diagnosis, and clinical criteria from the chart and builds a payer-specific authorization packet, with the right CPT and ICD codes attached.
02
A conversational AI that calls payers
A voice agent places and handles the call, works through the payer's prompts and questions, and records the authorization or the exact reason for a denial.
03
Rules tuned to every payer
The platform encodes the quirks of more than forty payers, so each request goes out in the precise form that payer expects, the first time.
04
Scheduling that closes the loop
The moment an authorization clears, the platform books the procedure and notifies the patient, turning an approval into an appointment automatically.

The staff didn't lose the work. They lost the hold music, and got their day back for patients.

Pipeline from intake to packet assembly to AI payer call to decision and scheduling
FIG.02From order to booked procedure: intake, ML packet assembly, the AI payer call, an auto-adjudicated decision, and scheduling, in one unbroken pipeline.

The outcome

Within the first year, most authorizations ran without a person touching them. Turnaround fell from days to hours, procedures stopped slipping, and denials tied to bad codes or missing forms dropped sharply because the packet was right before it ever left the building.

The fastest authorization is the one no one had to chase. Care stopped waiting on paperwork.

Staff still own the hard exceptions, where judgment matters. Everything routine now runs on its own, and the people who used to live on hold spend their time where it counts.