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Achieved 100% Precision and 83% Recall in Prior Authorization Question Generation

Avalon managed over 60 complex, frequently updated policy documents used to determine patient eligibility for diagnostic tests. The prior authorization process was manual and time-consuming, requiring staff and providers to sift through extensive documentation. This created operational inefficiencies and increased the risk of delays or errors in patient care. Avalon partnered with Tribe to build a customized proof of concept using large language models to extract key information from policy documents and generate medically accurate prior authorization questions. The team used Claude 3 Opus to parse policy language, identify relevant coverage sections, and produce structured clinical questions for human reviewers. The PoC also supported PDF uploads, model selection, auto-generated test lists with manual adjustment, and a feedback loop to refine outputs with human-in-the-loop oversight. The PoC pilot achieved 100% precision in producing clinically accurate follow-up questions for human reviewers and delivered 83% recall across the policy documents tested, exceeding internal benchmarks. These results supported fewer eligibility assessment errors and faster review times. Avalon also shifted its policy documentation review cadence from annual to monthly cycles and planned to expand beyond the initial four test policies.

Delivered by

Tribe AI

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Achieved 100% Precision on Prior Authorization Automation

Avalon Healthcare Solutions needed to automate prior authorization while maintaining strict accuracy in decisions. The process required extracting medical necessity criteria and cross-referencing it against clinical data. They also needed a way to handle complex cases that could not be cleanly automated. A GenAI solution was implemented to automate key prior authorization steps. The system extracted medical necessity criteria, cross-referenced clinical data, and used intelligent routing to send complex cases to the appropriate path. The delivery used a RAG architecture with fine-tuned LLMs. A team of 3 AI engineers built and delivered the solution. The implementation achieved production-ready prior authorization automation. It delivered 100% precision on automated approvals and reached an 83% recall rate. The automation improved efficiency by streamlining how criteria were extracted, data were checked, and edge cases were routed.

Delivered by

Tribe AI

AvalonThe customer this was delivered for.Action CenterSave this case, request the same outcome from Tribe AI, share it, or get in touch.

Achieved 100% Precision and 83% Recall in Prior Authorization Automation

Avalon Healthcare Solutions needed to automate prior authorization while maintaining high clinical accuracy. The existing process required extracting medical necessity criteria and cross-referencing it against clinical data. Complex cases also needed to be routed correctly rather than auto-approved. They sought a GenAI approach that could be production-ready. A GenAI solution was implemented to extract medical necessity criteria and cross-reference relevant clinical data. The implementation used a RAG architecture with fine-tuned LLMs to support accurate decisioning. An intelligent routing system was built to escalate complex cases appropriately. The work was delivered by a focused engineering team. The solution achieved production-ready automation for prior authorization workflows. It delivered 100% precision on automated approvals. It also reached an 83% recall rate. The automation improved efficiency by reducing manual effort in routine approvals while routing exceptions for review.

Delivered by

Tribe AI