HEOR Researcher

3+ years of experience conducting research with real-world healthcare data (for example, medical and pharmacy claims) at a population level.

Demonstrated experience researching medication adherence and outcomes across one or more of commercial, Medicare Advantage, or Medicaid Managed Care populations.

Hands-on proficiency with SQL, R, and Python for manipulating data, conducting analyses, and creating graphics.

Working knowledge of causal inference for observational data, with the ability to apply methods such as difference-in-differences and matching to real-world client analyses, and to reason about confounding and selection bias.

Familiarity with adherence and quality frameworks such as CMS Star Ratings and Part D adherence measures (diabetes, RAS antagonists, statins), SUPD and SPC, and HEDIS or Medicaid quality measures.

Understanding of medication therapy classes and clinically appropriate alternatives (for example, antihypertensive and diabetes regimens) and how clinical context affects adherence measurement.

Research drivers of medication adherence:

Design and independently conduct studies of what drives adherence and non-adherence using pharmacy and medical claims, enrollment, and supplemental data.

These studies span clinical comorbidities (including behavioral health), social determinants of health (such as social vulnerability), demographics, utilization (such as inpatient and skilled-nursing stays), pharmacy and medication factors, and operational or policy drivers such as pharmacy closures, formulary and copay changes, and CMS rule or measure changes.

Build and interpret explanatory models:

Fit and interpret multivariable statistical models to identify which factors are significant when considered together, distill them into focused lists of drive, and explain why effects differ across populations, contracts, and performance years.

You will collaborate with cross-functional teams made up of data scientists, clinicians, and others.