Prior-Authorization Oncology Review Agent using LangChain and Llama 3.3 70B
Processing oncology prior-authorization requests demands aligning patient gene mutant dosage against metastatic tropism evidence. Build a stateful LangChain and LangGraph pipeline powered by Llama 3.3 70B to evaluate unstructured clinical records and yield structured approval determinations with 90% precision.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an enterprise oncology prior-authorization agent using LangChain, LangGraph, and Llama 3.3 70B.
How work is evaluated
Evaluates precision and evidence extraction accuracy of prior-authorization determinations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
decision_validity
Checks if decision is strictly APPROVED or DENIED.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
decision_precision
Precision of approval determinations against expert oncologist consensus. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design stateful multi-step graphs using LangChain and LangGraph.
Integrate Llama 3.3 70B for clinical record processing and reasoning.
Parse unstructured medical records and cross-reference gene mutant dosage data.
Output standardized JSON prior-authorization decisions with evidence citations.
Reference links and supporting material
De-identified synthetic prior-authorization requests combining clinical oncology progress notes with genomic sequencing reports.
Challenge setup
Evaluates precision and evidence extraction accuracy of prior-authorization determinations.
Challenge input
JSON containing patient clinical text, targeted drug, and genomic mutation profile.
LangChain
Industry standard framework for building retrieval and tool-augmented chains.
Llama 3.3 70B
High-capacity open weights model for complex clinical text reasoning.
Evaluated output
JSON with decision (APPROVED/DENIED), rationale, and cited_evidence.
- Checks if decision is strictly APPROVED or DENIED.
- Precision of approval determinations against expert oncologist consensus. • target: 0.9 • range: 0-1
- Benchmark: Clinical Prior-Authorization Benchmark
- Decision Precision target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Llama 3.3 70B · Required
- Langchain · Optional
- Stainless · Optional
- Langchain · Optional
Evaluation contract
- decision_validity · Weight 1
- decision_precision · Weight 1
Metrics
- decision_precision · Target 0.9
Benchmarks
- Clinical Prior-Authorization Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing patient clinical text, targeted drug, and genomic mutation profile.
Output: JSON with decision (APPROVED/DENIED), rationale, and cited_evidence.
Execution harness: Python
Evaluation tools
- LangChain · Agent Framework
- Llama 3.3 70B · LLM Engine
Recipe freeze marker
No recipe freeze time is recorded.