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Patient Identity Reconciliation Service (Managed Operations)

A dedicated team-as-a-service that ingests fragmented patient records from a health plan's claims, EHR, pharmacy, and enrollment systems, applies deterministic matching (SSN, DOB, name variants) plus probabilistic scoring, flags high-risk duplicates for manual review, and delivers a unified patient roster with reconciliation rules documented for compliance. The service owns the matching logic, quality gates, and ongoing maintenance—not the plan.

SERVICE

25 weeks • 70% confidence

Value Proposition

Eliminates manual reconciliation bottleneck, reduces duplicate-claim leakage by 40–60%, achieves compliance-ready audit trails, and requires zero IT integration work from the plan—service team connects directly to existing data exports. Faster ROI than building in-house; plans pay only for reconciliation work, not software licenses.

Target Audience

Mid-to-large health plans (50K–500K members) with 2–5 disparate core systems and in-house data teams too small to build/maintain identity logic

Key Features

  • Deterministic matching engine (SSN, DOB, name, address phonetic variants)
  • Probabilistic scoring for fuzzy matches (Levenshtein distance, date proximity)
  • Manual review queue with SLA-tracked adjudication by trained staff
  • And more, with full implementation detail...

Tech Stack

Python (pandas, scikit-learn for matching logic) PostgreSQL (record storage, match history) Simple web interface for manual review queue (React or Django template) Scheduled ETL (Airflow or cron) to ingest daily member feeds
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Original Problem

Health plans cannot accurately match patient records across fragmented data systems, causing billing errors, duplicate treatments, and compliance failures

Payers and health plans struggle with fragmented patient identity data spread across multiple systems, leading to incorrect claims processing, duplicate medical records, failed care coordination, and regulatory violations. Current identity management solutions fail to unify data across disparate sources, forcing manual reconciliation and creating operational bottlenecks that cost plans millions in waste and liability exposure.

Score: 54.8% • 2 demand signals

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