Job Title: Clinical Research Associate Department: Payment Integrity / Fraud, Waste, & Abuse (FWA) Analytics Reports to: Director of Healthcare Analytics / DRG Validation Manager.
Role Objective :
To bridge the gap between complex medical insurance policies and automated data mining. The Architect identifies "leakage" opportunities (overpayment, abuse, or fraud) by analyzing payer policies and translating clinical nuances into structured logic for high-scale data mining.
Core Responsibilities
- Policy Decoding: Analyze CMS (National/Local Coverage Determinations), Payer-specific medical policies, and Coding Clinics to identify "hard rules" for reimbursement.
- Concept Ideation: Proactively identify scenarios where providers may be over billing or misusing codes (e.g., unbundling, upcoding, or billing for services inconsistent with a diagnosis).
- Rule Drafting: Write "Clinical Concepts"—structured logic documents that define: o Inclusion Criteria: (e.g., "All claims with CPT X and Modifier Y"). o Exclusion Logic: (e.g., "Except when Diagnosis Z is present"). o Look-back Parameters: (e.g., "Check if the patient had a prior visit within 48 hours").
- FWA Detection: Design specific "Concept Scans" to detect intentional fraud (phantom billing) or unintentional abuse (pattern-based billing errors).
- Collaboration: Partner with Data Scientists to test the "hit rate" of the rules and refine the logic to reduce false positives.
Required Qualifications:
- Medical Background (Mandatory): MBBS, BDS, MD, or BHMS. A deep understanding of clinical disease progression is vital to spot when a claim "doesn't make clinical sense." Policy Expertise: High proficiency in reading and interpreting US Healthcare policy language (Medicare/Medicaid/Commercial).
- Coding Literacy: Deep knowledge of ICD-10-CM, CPT, HCPCS, and the Correct Coding Initiative (CCI) edits. Analytical Mindset: Ability to think in "Boolean Logic" (AND/OR/NOT) to help tech teams build the mining queries.
Pay: Up to ₹800,000.00 per year
Benefits:
Work Location: In person
