Electronic Health Records as Biased Tools or Tools Against Bias: A Conceptual Model

Abstract

Policy Points Electronic health records (EHRs) are subject to the implicit bias of their designers, which risks perpetuating and amplifying that bias over time and across users. If left unchecked, the bias in the design of EHRs and the subsequent bias in EHR information will lead to disparities in clinical, organizational, and policy outcomes. Electronic health records can instead be designed to challenge the implicit bias of their users, but that is unlikely to happen unless incentivized through innovative policy. ContextHealth care delivery is now inextricably linked to the use of electronic health records (EHRs), which exert considerable influence over providers, patients, and organizations.MethodsThis article offers a conceptual model showing how the design and subsequent use of EHRs can be subject to bias and can either encode and perpetuate systemic racism or be used to challenge it. Using structuration theory, the model demonstrates how a social structure, like an EHR, creates a cyclical relationship between the environment and people, either advancing or undermining important social values.FindingsThe model illustrates how the implicit bias of individuals, both developers and end‐user clinical providers, influence the platform and its associated information. Biased information can then lead to inequitable outcomes in clinical care, organizational decisions, and public policy. The biased information also influences subsequent users, amplifying their own implicit biases and potentially compounding the level of bias in the information itself. The conceptual model is used to explain how this concern is fundamentally a matter of quality. Relying on the Donabedian model, it explains how elements of the EHR design (structure), use (process), and the ends for which it is used (outcome) can first be used to evaluate where bias may become embedded in the system itself, but then also identify opportunities to resist and actively challenge bias.ConclusionsOur conceptual model may be able to redefine and improve the value of technology to health by modifying EHRs to support more equitable data that can be used for better patient care and public policy. For EHRs to do this, further work is needed to develop measures that assess bias in structure, process, and outcome, as well as policies to persuade vendors and health systems to prioritize systemic equity as a core goal of EHRs.

Multi-Sector Assessment and Client-Perception of Social Need at Long-Term Follow-Up of a Group-Randomized Trial of Community-Engaged Collaborative Care for Adults with Depression

“…Organizational responsiveness may be prompted by external factors including but not limited to value-based transitions in financing [ 44 ] and may also require systems redesign—including trainings to increase provider preparedness, systematic data collection on clients’ social needs, a more robust IT infrastructure to track outcomes while simultaneously ensuring equity, and increased systems capacity to sustain viable solutions for social needs within the healthcare organization or through strategic community partnerships [ 6 , 45 , 46 , 47 ].…”

Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records

“…We show how when medical machine learning researchers create or use datasets that lean on EHR data with limited information about sex/gender, they embed any assumptions about sex/gender that are present in those data and potentially introduce their own. 85 …”

“…22 , 77 , 107 (6) Consider bias from sources other than sex and gender-identity fields. 46 , 59 , 85 , 100 , 101 (7) Audit model performance for subgroups without presuming or essentializing differences. 46 , 57 , 108 , 109 , 110 , 111 …”

“…We show how when medical machine learning researchers create or use datasets that lean on EHR data with limited information about sex/gender, they embed any assumptions about sex/gender that are present in those data and potentially introduce their own. 85 Then, in the next section, we present a case study that takes a closer look at HIV and prescription of pre-exposure prophylaxis (PrEP). We chose this area because one of the inaugural predictive studies included transgender people explicitly, but subsequent studies still demonstrate sex obsession, despite the fact that HIV prevention is an area where one might expect a much more nuanced understanding of sex/gender.…”

“…22,77,107 (6) Consider bias from sources other than sex and genderidentity fields. 46,59,85,100,101 (7) Audit model performance for subgroups without presuming or essentializing differences. 46,57,[108][109][110][111] Avoid sex obsession by increasing data richness We recommend focusing on data richness whenever possible instead of relying primarily on binary sex assigned at birth as a proxy for other variables.…”