Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
Department of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA.
J Am Med Inform Assoc. 2023 Jul 19;30(8):1456-1462. doi: 10.1093/jamia/ocad043.
Identifying patients' social needs is a first critical step to address social determinants of health (SDoH)-the conditions in which people live, learn, work, and play that affect health. Addressing SDoH can improve health outcomes, population health, and health equity. Emerging SDoH reporting requirements call for health systems to implement efficient ways to identify and act on patients' social needs. Automatic extraction of SDoH from clinical notes within the electronic health record through natural language processing offers a promising approach. However, such automated SDoH systems could have unintended consequences for patients, related to stigma, privacy, confidentiality, and mistrust. Using Floridi et al's "AI4People" framework, we describe ethical considerations for system design and implementation that call attention to patient autonomy, beneficence, nonmaleficence, justice, and explicability. Based on our engagement of clinical and community champions in health equity work at University of Washington Medicine, we offer recommendations for integrating patient voices and needs into automated SDoH systems.
识别患者的社会需求是解决健康的社会决定因素（SDoH）的首要关键步骤-人们生活，学习，工作和娱乐的条件会影响健康。解决 SDoH 可以改善健康结果，人口健康和健康公平。新兴的 SDoH 报告要求要求卫生系统实施有效的方法来识别和满足患者的社会需求。通过自然语言处理从电子健康记录中的临床记录中自动提取 SDoH 提供了一种很有前途的方法。但是，此类自动化的 SDoH 系统可能会对患者产生意想不到的后果，例如耻辱感，隐私，机密性和不信任。我们使用佛罗里迪等人的“ AI4People”框架，描述了系统设计和实施的道德考虑因素，这些因素引起了人们对患者自主性，善行，不伤害，正义和可解释性的关注。基于我们在华盛顿大学医学中心参与健康公平工作的临床和社区拥护者，我们为将患者的声音和需求纳入自动化的 SDoH 系统提供了建议。
