Increasing Healthcare Equity in Collaboration with Medstar Health

Wednesday, October 4, 2023

I.D.E.A.S. for Diagnostic Equity

The Elaine Marieb Center is pleased to announce that Dr. Muge Capan (Elaine Marieb Center Affiliated Faculty Member ) was recently awarded a four-year grant by the Agency for Healthcare Research and Quality to fund her team’s project.  The study, titled Improving Diagnostic Equity in Ambulatory Care Settings (I.D.E.A.S):  Research to Practice, is led by the Capan team (comprised of Dr. Capan of UMass Amherst as well as Dr. Kristen Miller and Dr. William Gallagher of Medstar Health).  I.D.E.A.S will examine the impact of diagnostic errors on healthcare equity.  Effective healthcare delivery depends on accurate diagnoses, and diagnostic errors remain a leading cause of mortality and suffering in the United States. Diagnostic errors can disproportionately affect medically underserved and vulnerable patient populations, due to the volume and variety of patients as well as expensive, scarce, and costly referral and testing resources.  Disparities result when preventable diagnostic errors occur disproportionately among demographic subgroups; research has shown disparities in diagnostic errors in areas such as race and ethnicity, sex, gender, geographic location, and socioeconomic status in part due to implicit bias, discrimination, and stigma. 


Diagnostic uncertainty is a natural part of medicine and more common in primary care than any other specialty (the complexity, time constraints, and uncertainty of the diagnostic process can make it a challenge).  I.D.E.A.S. is novel in its interdisciplinary approach, bringing together data science, operations research, and human factors engineering along with clinical diagnosticians to create methodological advances in conceptualizing and measuring diagnostic errors.  It is expected that the outcomes of the Capan team’s study will lay the foundation for a comprehensive framework that can be used to inform interventions that will aim to reduce diagnostic inequity and improve the management of diagnostic uncertainty.