Automate Prior Authorization Workflows with LangGraph and FHIR Data Validation
Health care operations rely heavily on accurate medical coding (ICD-10, CPT) and rapid clinical decision verification for prior authorization requests. Modern healthcare automation platforms require parsing unstructured physician encounter notes, linking clinical entities to standardized FHIR R4 resources, and executing deterministic coverage rule graphs alongside probabilistic LLM evaluations. In this challenge, you will design and implement a stateful Prior Authorization Agent using LangChain and LangGraph, paired with automated code refactoring workflows powered by Aider. Your agent will process unstructured clinical encounter notes, construct compliant FHIR R4 bundles (Patient, Condition, Procedure, Coverage), evaluate medical necessity against insurance policy guidelines, and output structured prior authorization decisions with comprehensive audit trails. By completing this project, you will gain hands-on expertise in clinical NLP, healthcare data standards (FHIR R4), and constructing stateful, auditable multi-step agent graphs tailored for clinical operations and revenue cycle management.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an end-to-end clinical workflow automation agent using LangGraph, FHIR R4, and Aider to evaluate prior authorization requests from unstructured EHR notes.
How work is evaluated
Evaluation framework validating FHIR bundle creation correctness and LangGraph prior authorization execution accuracy.
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.
fhir_schema_validity
Validates that generated bundle strictly conforms to FHIR R4 specifications.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
auth_decision_correctness
Verifies prior authorization decision matches ground-truth policy evaluation logic.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
entity_extraction_f1
F1 score for extracted ICD-10 and CPT codes from clinical text. • target: 0.92 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
graph_execution_latency_ms
Total execution time for the full LangGraph decision workflow. • target: 2500 • range: 0-10000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Parse unstructured clinical notes into standardized FHIR R4 resource bundles (Patient, Condition, Procedure).
Build a stateful LangGraph execution graph incorporating medical necessity validation and clinical decision trees.
Integrate automated code generation and refactoring tools (Aider) to enforce schema compliance and clinical logic.
Generate auditable, deterministic prior authorization decision logs formatted for payer-provider operations.
Reference links and supporting material
Synthetic dataset of 250 clinical encounter notes spanning orthopedics, cardiology, and oncology paired with ground-truth FHIR R4 bundles and payer prior authorization rules.
Challenge setup
Evaluation framework validating FHIR bundle creation correctness and LangGraph prior authorization execution accuracy.
Challenge input
JSON containing transcript and patient meta
LangChain / LangGraph
Provides stateful graph modeling for deterministic and probabilistic medical authorization trees.
Aider
Automates code refactoring and test-driven modification of clinical validation scripts.
fhir.resources
Pydantic validation for FHIR R4 compliant healthcare objects.
Evaluated output
Valid FHIR R4 Bundle JSON object
- Validates that generated bundle strictly conforms to FHIR R4 specifications.
- Verifies prior authorization decision matches ground-truth policy evaluation logic.
- F1 score for extracted ICD-10 and CPT codes from clinical text. • target: 0.92 • range: 0-1
- Benchmark: ClinicalNLP-PriorAuth-Bench
- Entity Extraction F1 target: 0.92
- 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
- LangGraph · Required
- Aider · Optional
- LastMile AI · Optional
- Aider · Optional
- LangGraph · Required
Evaluation contract
- fhir_schema_validity · Weight 1
- auth_decision_correctness · Weight 1
- entity_extraction_f1 · Weight 1
- graph_execution_latency_ms · Weight 1
Metrics
- entity_extraction_f1 · Target 0.92
- graph_execution_latency_ms · Target 2500
Benchmarks
- ClinicalNLP-PriorAuth-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing transcript and patient meta
Output: Valid FHIR R4 Bundle JSON object
Execution harness: Python
- Task 2
Input: JSON with fhir_bundle and coverage_policy
Output: JSON with decision status, policy_met, and clinical_rationale
Execution harness: Python
Evaluation tools
- LangChain / LangGraph · Agentic Workflow Orchestration
- Aider · AI Code Refactoring & Pair Programming
- fhir.resources · Data Validation
Recipe freeze marker
No recipe freeze time is recorded.