Digital Quality Measures Lead (Part-Time)

🏢 Americaninstitutesforresearch · 📍 US-Remote · 💼 remote

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Position Summary & Responsibilities

AIR's Quality Measurement & Improvement (QM&I) portfolio is seeking a part-time Fast Healthcare Interoperability Resources (FHIR) / Digital Quality Measures Lead to serve as a senior technical authority on our federally funded eCQM development contracts. This role sets technical direction for FHIR-based digital quality measure (dQM) specification work, serves as the primary technical point of contact with CMS and other clients for topics related to digital quality measurement, including FHIR, and is responsible for mentoring a small team of measure developers and analysts. The ideal candidate has hands-on experience translating clinical quality measures into CQL and FHIR-based specifications and is comfortable operating across the full dQM lifecycle, from measure concept through annual maintenance updates.

This position sits within AIR's Quality Measurement & Improvement portfolio, which supports CMS accountability and quality reporting programs across clinician, hospital, and post-acute/nursing home settings. You will work closely with QM&I leadership to grow the organization's technical capability in FHIR-based digital quality measurement.

Engagements are typically project-based, part-time, and may vary in duration and hours depending on project needs.

Candidates hired for the position may work remotely within the United States (U.S.) or from one of our U.S. office locations. This does not include U.S. territories. 

About AIR:

Founded in 1946 and headquartered in Arlington, Virginia, the American Institutes for Research (AIR) is a nonpartisan, not-for-profit organization that conducts behavioral and social science research and delivers technical assistance to address some of the most pressing challenges in the United States

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Key Role Competencies & Evaluation Highlights

Key Role Focus:

Assesses rigorous methodological precision, objective research integrity, and the ability to translate complex data science into actionable public policy.

📋 Preparation Priorities (Key Areas to Clarify):

1. Advancing a partisan agenda or showing bias in research interpretation.
2. Prioritizing speed over empirical rigor and peer-reviewed validity.
3. Inability to explain complex quantitative models to non-technical policymakers.

✨ Recommended Role Alignment Hook:

I combine rigorous statistical methodologies with clear policy communication to ensure research directly drives equitable societal outcomes.

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