College for Public Health and Social Justice, Saint Louis University.
Brookings Institution - USC Schaeffer Initiative on Health Policy.
Milbank Q. 2022 Mar;100(1):134-150. doi: 10.1111/1468-0009.12545. Epub 2021 Nov 23.
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.
Health care delivery is now inextricably linked to the use of electronic health records (EHRs), which exert considerable influence over providers, patients, and organizations.
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.
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.
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.
电子病历（EHRs）受到其设计者的隐性偏见的影响，这可能会随着时间的推移和用户的增加而持续放大这种偏见。如果不加控制，EHR 设计中的偏见以及随后 EHR 信息中的偏见将导致临床、组织和政策结果的差异。电子病历可以被设计用来挑战用户的隐性偏见，但除非通过创新政策得到激励，否则这种情况不太可能发生。
医疗服务的提供现在与电子病历（EHRs）的使用密不可分，EHRs 对提供者、患者和组织都有很大的影响。
本文提供了一个概念模型，展示了 EHR 的设计和随后的使用如何受到偏见的影响，它可以编码和延续系统性种族主义，也可以用来挑战它。使用结构化理论，该模型演示了一个社会结构，如 EHR，如何在环境和人之间建立一个循环关系，从而促进或破坏重要的社会价值观。
该模型说明了个人的隐性偏见，包括开发者和最终用户临床提供者，如何影响平台及其相关信息。有偏见的信息随后可能导致临床护理、组织决策和公共政策的不平等结果。有偏见的信息也会影响随后的用户，放大他们自己的隐性偏见，并可能使信息本身的偏见程度进一步增加。该概念模型用于解释这种担忧从根本上说是一个质量问题。它依赖于 Donabedian 模型，解释了 EHR 设计的元素（结构）、使用（过程）以及使用它的目的（结果）如何首先用于评估系统本身可能嵌入偏见的地方，但也可以确定抵制和积极挑战偏见的机会。
我们的概念模型可以通过修改 EHR 以支持更公平的数据来重新定义和提高技术对健康的价值，这些数据可用于更好的患者护理和公共政策。为了使 EHR 做到这一点，需要进一步努力开发评估结构、过程和结果中的偏见的措施，以及制定政策，说服供应商和卫生系统将系统性公平作为 EHR 的核心目标。
