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
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.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
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.
Learning goals
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.
Resources
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)
Shows the first task, up to three tools, and up to three evidence 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.