Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms.

Autor:	Jain A; Evidence-based Practice Center Division, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland., Brooks JR; Department of Psychology, University of Houston, Houston, Texas., Alford CC; Evidence-based Practice Center Division, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland., Chang CS; Evidence-based Practice Center Division, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland., Mueller NM; Evidence-based Practice Center Division, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland.; Division of Practice Improvement, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland., Umscheid CA; Evidence-based Practice Center Division, Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland., Bierman AS; Office of the Director, Agency for Healthcare Research and Quality, Rockville, Maryland.; Center for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland.
Jazyk:	angličtina
Zdroj:	JAMA health forum [JAMA Health Forum] 2023 Jun 02; Vol. 4 (6), pp. e231197. Date of Electronic Publication: 2023 Jun 02.
DOI:	10.1001/jamahealthforum.2023.1197
Abstrakt:	Importance: Algorithms are commonly incorporated into health care decision tools used by health systems and payers and thus affect quality of care, access, and health outcomes. Some algorithms include a patient's race or ethnicity among their inputs and can lead clinicians and decision-makers to make choices that vary by race and potentially affect inequities. Objective: To inform an evidence review on the use of race- and ethnicity-based algorithms in health care by gathering public and stakeholder perspectives about the repercussions of and efforts to address algorithm-related bias. Design, Setting, and Participants: Qualitative methods were used to analyze responses. Responses were initially open coded and then consolidated to create a codebook, with themes and subthemes identified and finalized by consensus. This qualitative study was conducted from May 4, 2021, through December 7, 2022. Forty-two organization representatives (eg, clinical professional societies, universities, government agencies, payers, and health technology organizations) and individuals responded to the request for information. Main Outcomes and Measures: Identification of algorithms with the potential for race- and ethnicity-based biases and qualitative themes. Results: Forty-two respondents identified 18 algorithms currently in use with the potential for bias, including, for example, the Simple Calculated Osteoporosis Risk Estimation risk prediction tool and the risk calculator for vaginal birth after cesarean section. The 7 qualitative themes, with 31 subthemes, included the following: (1) algorithms are in widespread use and have significant repercussions, (2) bias can result from algorithms whether or not they explicitly include race, (3) clinicians and patients are often unaware of the use of algorithms and potential for bias, (4) race is a social construct used as a proxy for clinical variables, (5) there is a lack of standardization in how race and social determinants of health are collected and defined, (6) bias can be introduced at all stages of algorithm development, and (7) algorithms should be discussed as part of shared decision-making between the patient and clinician. Conclusions and Relevance: This qualitative study found that participants perceived widespread and increasing use of algorithms in health care and lack of oversight, potentially exacerbating racial and ethnic inequities. Increasing awareness for clinicians and patients and standardized, transparent approaches for algorithm development and implementation may be needed to address racial and ethnic biases related to algorithms.
Databáze:	MEDLINE
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