MedicalAI implementation case study

Medical – Mid-Sized Multi-Specialty Practice (50+ Staff): Revenue-Cycle AI Fabric

Full AI automation for eligibility, prior auth orchestration, claim scrubbing, and denial rework across a 50+ staff medical practice.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

PrimeAxiom automated the full revenue-cycle chain from schedule trigger to paid claim, using AI agents for auth and appeals—not single-point bots.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: multi-specialty outpatient. Size: 52 staff, 18 providers. Complexity: multi-payer, high prior-auth specialty mix. Systems: athena, clearinghouse, payer portals.

02 · Challenge

Core problem

Manual re-keying, unclear ownership of auth status, and denials discovered late. Bottlenecks at auth submission and COB verification. Scale broke when new providers joined with different payer mixes.

03 · Approach

Solution

PrimeAxiom connected EHR scheduling triggers to AI eligibility verification, prior-auth packet generation with payer-specific rules, autonomous claim scrubbing, and denial agents that draft appeals with chart excerpts.

04 · Impact

Results

Denials down 27%; prior auth turnaround improved 44%; FTE-equivalent 2.4 roles redeployed to patient-facing work; cash velocity improved 9% in 90 days.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Orchestrated pipeline: appointment → eligibility bot → auth workflow with document AI → claim generation checks → denial classification → appeal drafting with human sign-off.

Workflow

How the automation runs

1. Trigger: schedule create/modify. 2. AI eligibility: batch + real-time checks; patient SMS for gaps. 3. Structure: coverage object in DB. 4. Decision: auth required? which pathway? 5. Actions: fax/API submission; task nurses for clinical questions; scrub claims. 6. Humans: clinicians on clinical auth questions; finance on write-offs. 7. Loop: denial reasons refine rules and prompts.

Intelligence layer

AI agents and decision logic

Eligibility Agent, Prior Auth Agent, Claim Scrub Agent, Denial Triage Agent, Appeal Draft Agent.

Under the hood

Technical architecture

HIPAA-aligned VPC, encrypted PHI, audit logs, athena APIs, RPA for legacy portals, PostgreSQL, LLM with de-identified prompts where possible.

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