Healthcare Provider Automates Referrals and Prior Authorization

The Client

A regional healthcare provider under pressure to do more with the same team

The client is a mid-sized regional healthcare provider in the United States, operating a network of clinics across multiple specialties. Details are anonymized at the client's request, as is standard for engagements in regulated, privacy-sensitive environments.

Their referral and intake teams sit at the center of patient flow: every inbound referral and every prior-authorization request passes through a small group of coordinators. As patient volume grew, that team did not. The work that kept the practice running was the same work quietly capping how many patients it could see.

Leadership did not want another advisory deck describing the problem they already lived with every day. They wanted the problem gone: built out, in production, inside their own systems, without compromising patient privacy. That is precisely the shape of work Forward Deployed Engineering is built for.

The Challenge

30 to 40 hours a week lost to manual referrals and prior auth

Referral and prior-authorization processing was almost entirely manual. Coordinators re-keyed patient and clinical data between the EMR, payer portals, and a CRM; chased missing documentation by phone and fax; and tracked the status of each case in spreadsheets and inboxes. Taken together, this consumed 30 to 40 hours of staff time every week.

The slow, error-prone process had three compounding consequences the team felt constantly:

• No audit trail. When a referral stalled or a prior auth was denied, no one could reconstruct what happened, when, or why, a real liability in a regulated environment where defensibility matters.
• Frequent errors. Manual re-keying across systems meant transposed codes, missing attachments, and incomplete submissions that bounced back from payers and restarted the clock.
• Slow turnaround. A typical prior authorization took around four days to clear, delaying care, frustrating patients and referring physicians, and pushing avoidable rework onto an already stretched team.

Our Approach

A 12-week embedded FDE squad, with privacy as a hard boundary

We did not advise and leave. A ConvertEdge Tech Forward Deployed Engineering squad (an AI engineer, an integration architect, and a Salesforce specialist) embedded directly with the provider's operations, clinical-informatics, and IT teams for a focused 12-week engagement. We joined their standups, worked from their backlog, and built inside their systems.

Healthcare changes how an engagement is run. Privacy-first digital transformation that still moves fast was the operating principle from day one. Before a single line of automation shipped, we agreed the boundaries with the client's privacy and compliance leads, and held them as non-negotiable:

• The AI agent operates within strict, explicitly scoped privacy boundaries: it touches only the minimum data each step requires, and only inside systems the provider already controls.
• Protected health information never leaves the provider's trust boundary for model training, and every automated action is constrained by guardrails that fail closed rather than guess.
• A human coordinator stays in the loop for every clinical-judgment decision; the agent handles the mechanical, repetitive work and escalates anything outside its mandate.

As with every FDE engagement, we started from a clear, mutually agreed Definition of Done: a production agent automating the referral and prior-authorization workflow end to end, measurably reducing manual processing time, and producing a complete, queryable audit trail. We measured success in those KPIs, not in milestones or documents.

What We Built

A privacy-aware AI agent that runs the workflow end to end

The deliverable was a production AI agent integrated with the provider's EMR and CRM, automating the full referral-to-authorization lifecycle. It ingests an inbound referral, assembles the clinical and demographic context each payer requires, drafts and submits the prior-authorization request, tracks its status, and routes exceptions to a human, with every step logged.

The result is a system the team trusts: the agent does the repetitive heavy lifting, the guardrails keep it honest, and the audit trail means there is finally an answer to "what happened with this referral?", in seconds, not a phone tree.

The Outcome

Faster authorizations, hours back, and a record you can defend

Inside the 12-week window, the agent moved from pilot to handling live referral and prior-authorization volume in production. The numbers tell the story, but the adoption story underneath them matters just as much.

Adoption: trust earned, not mandated

Because the squad was embedded, adoption did not depend on a training rollout after handover. Coordinators watched the agent take over the parts of their day they hated (the re-keying, the status chasing, the fax follow-ups) while keeping them firmly in control of every clinical decision. Trust built case by case, and the agent went from "the new tool" to "how we do referrals" well before the engagement ended.

The recovered 30 to 40 hours a week were not cut from the budget; they were given back to the same team, who redeployed them to patient care and the complex, judgment-heavy cases where human attention actually moves outcomes. Faster authorizations meant patients started treatment sooner, and referring physicians stopped chasing the practice for updates.

Timeline

How the 12 weeks unfolded