Data Analyst, Clinical Data Effectiveness

Counterpart Health is an AI‑powered physician enablement platform that delivers clinical insights to providers at the point of care. Our flagship product, Counterpart Assistant, is embedded into clinicians’ workflows and integrates with EHR systems, helping care teams drive value-based outcomes, close care gaps, and proactively manage chronic disease. As we grow our data ecosystem, expanding across legacy interoperability networks, QHIN/TEFCA connectivity, and direct HIE integrations, we need rigorous analytical ownership to ensure the clinical data powering our platform is complete, reliable, and actionable. You will serve as the Subject Matter Expert (SME) for clinical data completeness and effectiveness at Counterpart Health. Sitting within the Data Products pillar, this role owns the end-to-end understanding of our clinical data lifecycle: where data comes from, where it breaks down, and how each vendor or integration pathway performs across our customer markets. Your analysis will directly shape vendor strategy, facility targeting, and data pipeline investments, thus ensuring that the clinical evidence our care teams depend on is consistently available and trustworthy. As a Data Analyst, you will: Proactively identify and monitor which healthcare facilities consistently generate high-quality, usable clinical documents (e.g., EHRs, discharge summaries, procedure notes) to precisely locate and quantify data gaps. Conduct deep-dive analyses by mapping and correlating facility-specific data across disparate sources like structured claims, clinical care summaries (CCDAs), and ADT messages to uncover systematic patterns of missingness, latency, Experience with TEFCA/QHIN frameworks and evolving national interoperability standards. Familiarity with vendor-specific data exchange platforms (e.g., Bamboo Health, HSX, Particle Health, Kno2). Background in value-based care, population health, or clinical data operations. Experience with claims-clinical data reconciliation (e.g., matching EOBs to clinical encounter records). Comfort working with Python or similar tools for data manipulation and automation. or incongruence in clinical records. Establish and maintain clear, transparent reporting on ADT coverage across key geographic, organizational, and technological dimensions (e.g., by county, facility, and underlying EMR system). Based on rigorous analysis of data flow and quality, recommend and guide the implementation of optimal technical configurations for existing data vendors (e.g., Bamboo Health, PCC, Experian, HSX). Conduct comprehensive evaluations and proof-of-concepts for potential new data integration technologies (e.g., Kno2 vs. Particle Health) to assess viability, quality improvements, and strategic fit. Systematically determine the existence and completeness of documentation associated with specific patient visits or encounters. Develop a data-driven framework for prioritizing facility targets for direct data acquisition, assessing impact