Advancing health equity in patient safety: a reckoning, challenge and opportunity

• Published in: BMJ quality & safety Vol. 30; no. 5; pp. 356 - 361
• Main Author: Chin, Marshall H
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01.05.2021
• Subjects: Bias; Government programs; Health disparities; Hospitals; Legislation; Minority & ethnic groups; Patient safety; Police; Poverty; Professional associations; Public awareness; Quality of care; Racism; Safety; Socioeconomic factors; Socioeconomics; United States > US; health services research; health professions education; health policy
• ISSN: 2044-5415; 2044-5423
• DOI: 10.1136/bmjqs-2020-012599

COVID-19 and police brutality have simultaneously heightened public awareness of disparities in health outcomes by race/ethnicity, gender, and socioeconomic status, and the underlying structural drivers of systemic racism and social privilege in the USA.1 2 Increasingly major professional associations such as the American Medical Association, American Hospital Association, and Association of American Medical Colleges are decrying racism and inequities, and many individual healthcare organisations are committing to addressing health disparities. [...]persons living in zip code areas that have higher percentages of African Americans or persons living in poverty have less access to physicians practising in accountable care organisations.26 27 Moreover, inadequately designed incentive systems can penalise safety-net hospitals that care for marginalised populations, leading to a downward spiral in quality of care and outcomes. The initial iteration of Medicare’s Hospital Readmissions Reduction Program (HRRP) reduced Medicare payments to safety-net hospitals by 1%–3% and increased readmission rates for black patients in these hospitals.28 Directed by legislation passed by Congress, the Medicare programme intentionally addressed this equity problem in the HRRP in 2019 by stratifying hospitals by proportion of patients dually enrolled in Medicare and Medicaid, so that a given hospital’s clinical performance would be compared with that of hospitals with a similar prevalence of poverty when calculating financial rewards and penalties.29 (2) Focusing exclusively on cultural humility or implicit bias training and avoiding looking for systemic, structural drivers of inequities. [...]in a project designed to decrease hospital length of stay, the University of Chicago Medicine data analytics group discovered that the process the organisation had proposed for developing and using machine learning predictive algorithms to identify patients for intervention would have systematically shifted resources away from African Americans to more affluent white patients.30 31 This inequitable process was caught before implementation, and now the data analytics group is proactively building analytical processes to advance health equity.