Autonomous PA agent cuts cycle time 71%

Overview

Prior authorization is the #1 reason patients abandon treatment; AMA's 2023 report shows 94% of physicians say PA delays care.

An autonomous PA agent that can query an EHR, check formulary, call the payer API, and draft an approval letter end-to-end needs training data that teaches it which tool to use, in what order, under which clinical context.

Firstsource enables that training data: tool-selection labels for clinical scenarios, multi-step agent chain traces rated for sequencing and error recovery, an adversarial API-error corpus, and output QA grading PA letters for clinical accuracy and compliance.

This was Intelligence That Operates: PA agent training data produced under one quality discipline, tied to a 71% cycle-time reduction outcome.

Challenges

• Agent tool selection is a clinical judgment, not a software routing decision. The agent has to know that a Tier 3 drug needs step-therapy validation before submission — and the training data has to encode that clinical reasoning.
• Multi-step agent chains break on real-world API behavior. Payer timeouts, malformed EHR responses, and formulary mismatches are routine. An agent trained only on happy-path scenarios fails in production.
• PA letters and authorization decisions are compliance artifacts. A PA agent's output goes into the patient's medical record. Output QA has to grade for clinical accuracy, completeness, and regulatory compliance — not just for natural-sounding text.

How We Made It Happen

We produce the labeled training data, chain-trace evaluation, and output QA an autonomous PA agent needs to reach the cycle-time reduction healthcare requires.

• Tool-selection labels for clinical scenarios. Annotators tag which tools are correct for each clinical scenario, training the agent's selection model with clinical context, not just API metadata.
• Multi-step chain traces rated for sequencing and error recovery. Full agent runs evaluated for tool ordering accuracy, latency, and recovery from API errors and conflicting data.
• PA letter and decision output QA against compliance standards. Output graded for clinical accuracy, completeness, and regulatory compliance — production-grade evaluation, not benchmark-grade.

Conclusion

Autonomous PA only ships when the agent's reasoning has been taught by people who do PA for a living. Firstsource produces the training data and evaluation discipline that makes PA agents trustworthy — turning agentic PA into Intelligence that Operates.