School of Nursing, University of British Columbia Okanagan, kelowna, Canada.
School of Computing and Mathematics, Keele University, UK.
Yearb Med Inform. 2022 Aug;31(1):94-99. doi: 10.1055/s-0042-1742504. Epub 2022 Jun 2.
The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs), towards the development of equitable artificial intelligence (AI) technologies.
A rapid review of the literature on the inclusion of nursing and allied health data and the nature of health equity information representation in the development and/or use of artificial intelligence approaches alongside expert perspectives from the International Medical Informatics Association (IMIA) Student and Emerging Professionals Working Group.
Consideration of social determinants of health and intersectionality data are limited in both the medical AI and nursing and allied health AI literature. As a concept being newly discussed in the context of AI, the lack of discussion of intersectionality in the literature was unsurprising. However, the limited consideration of social determinants of health was surprising, given its relatively longstanding recognition and the importance of representation of the features of diverse populations as a key requirement for equitable AI.
Leveraging the rich contextual data collected by nursing and allied health professions has the potential to improve the capture and representation of social determinants of health and intersectionality. This will require addressing issues related to valuing AI goals (e.g., diagnostics versus supporting care delivery) and improved EHR infrastructure to facilitate documentation of data beyond medicine. Leveraging nursing and allied health data to support equitable AI development represents a current open question for further exploration and research.
本文旨在提请人们注意护理和相关健康专业人员目前对临床文档的利用不足的潜力，以改善电子健康记录（EHR）中社会决定因素（SDoH）和交叉性数据的表示，从而开发公平的人工智能（AI）技术。
对护理和相关健康数据的纳入以及在人工智能方法的开发和/或使用中健康公平信息表示的性质的文献进行快速回顾，同时参考国际医学信息学协会（IMIA）学生和新兴专业人员工作组的专家观点。
在医学人工智能和护理及相关健康人工智能文献中，对社会决定因素和交叉性数据的考虑都很有限。由于交叉性作为一个新概念在人工智能的背景下被新讨论，因此文献中缺乏对交叉性的讨论并不令人惊讶。然而，对社会决定因素的考虑有限令人惊讶，因为它已经得到了相对长期的认可，并且代表性的多样化人群的特征是公平人工智能的关键要求。
利用护理和相关健康专业人员收集的丰富的上下文数据，有可能改善对社会决定因素和交叉性的捕捉和表示。这将需要解决与重视 AI 目标（例如，诊断与支持护理提供）相关的问题，以及改善 EHR 基础设施，以促进除医学以外的数据的记录。利用护理和相关健康数据来支持公平的 AI 发展，这是一个当前需要进一步探讨和研究的开放性问题。
