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

Unlabelled: 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.

Context: Health care delivery is now inextricably linked to the use of electronic health records (EHRs), which exert considerable influence over providers, patients, and organizations.

Methods: This 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.

Findings: The 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.

Conclusions: Our 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.