PBM Claims QA Automation Framework
Applying 6+ years of pharmacy benefit management domain expertise to automated test design.
What & Why
After six years of manually testing PBM claims systems, running hundreds of claim adjudications, validating eligibility flows, checking reject codes against NCPDP standards, I know exactly what breaks and why. This project takes that domain knowledge and encodes it into automated tests.
This isn't a generic testing framework. It's built by someone who has lived inside PBM workflows and knows the edge cases that no requirements document fully captures.
Architecture
The framework is built with Python and Pytest, structured for maintainability and clarity:
pbm-claims-qa/ ├── tests/ │ ├── test_eligibility.py # Member eligibility validation │ ├── test_reject_codes.py # NCPDP reject code handling │ ├── test_formulary.py # Formulary and copay logic │ └── test_claims.py # End-to-end claim adjudication ├── fixtures/ │ ├── members.py # Test member data generators │ ├── claims.py # Claim payload builders │ └── plans.py # Benefit plan configurations ├── utils/ │ ├── ncpdp.py # NCPDP code lookups and validation │ └── validators.py # Common assertion helpers ├── .github/ │ └── workflows/ │ └── qa-pipeline.yml # GitHub Actions CI/CD ├── pytest.ini └── README.md
Coverage
Test Coverage Areas
Eligibility Validation
Verifies member eligibility across active, terminated, and future-effective dates. Tests coverage gaps, dependent age-outs, and coordination of benefits scenarios that are common failure points in PBM claims processing.
NCPDP Reject Code Handling
Validates correct reject codes are returned for specific claim failure scenarios: wrong BIN/PCN, invalid NDC, refill too soon, quantity limits exceeded. Maps real-world rejection patterns I encountered across thousands of test claims.
Formulary & Copay Logic
Tests tier assignment, step therapy requirements, prior authorization triggers, and copay calculation across different benefit plan structures. Covers the accumulator logic that tracks deductibles and out-of-pocket maximums.
CI/CD Pipeline
The test suite runs automatically on every push via GitHub Actions:
- ■Automated test execution on push and pull request
- ■Test result reporting with pass/fail summaries
- ■Coverage tracking to identify untested paths
- ■Lint and code quality checks with ruff
View the source code
Repository Coming SoonWhy This Matters
Anyone can learn Pytest. The difference is knowing what to test.
When I write a test for NCPDP reject code 75 ("Prior Authorization Required"), I'm not copying a spec. I'm recalling the dozens of times I debugged that exact scenario in production claims. When I build fixtures for coordination of benefits, I'm encoding the edge cases I discovered running thousands of claim adjudications through a raw claim generator.
This framework is proof that domain expertise and automation aren't competing skills. They're complementary. The best automated tests come from people who understand the system deeply enough to know where it will fail.