The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

Yearbook of medical informatics Pub Date : 2022-06-01 DOI:10.1055/s-0042-1742504

Charlene Esteban Ronquillo First Co-Author, James Mitchell First Co-Author, Dari Alhuwail, Laura-Maria Peltonen, M. Topaz, L. Block

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引用次数: 0

Abstract

Summary Objectives : 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. Methods : 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. Results : 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. Conclusions : 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.

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护理和相关健康数据的未开发潜力，以改善人工智能应用中健康社会决定因素和交叉性的表现:快速回顾

摘要目的:本文的目的是提请注意护理和相关卫生专业人员目前未充分利用的临床文件潜力，以改善电子健康记录(EHRs)中健康社会决定因素(SDoH)和交叉性数据的代表性，从而促进公平的人工智能(AI)技术的发展。方法:快速回顾有关在开发和/或使用人工智能方法中纳入护理和相关健康数据以及健康公平信息表示性质的文献，以及来自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。结果:在医疗人工智能、护理和相关卫生人工智能文献中，对健康的社会决定因素和交叉性数据的考虑都是有限的。作为人工智能背景下新讨论的概念，文献中缺乏对交叉性的讨论并不令人惊讶。然而，考虑到对健康的社会决定因素的认识相对较长，以及作为公平人工智能的关键要求，代表不同人口特征的重要性，对健康的社会决定因素的有限考虑令人惊讶。结论:利用护理和相关卫生专业人员收集的丰富的背景数据，有可能改善对健康和交叉性的社会决定因素的捕捉和表示。这将需要解决与评估人工智能目标相关的问题(例如，诊断与支持医疗服务)，并改进电子病历基础设施，以促进医学以外的数据记录。利用护理和相关卫生数据来支持公平的人工智能发展是当前有待进一步探索和研究的一个开放性问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Yearbook of medical informatics Medicine-Medicine (all)

CiteScore

4.10

自引率

0.00%

发文量

期刊介绍： Published by the International Medical Informatics Association, this annual publication includes the best papers in medical informatics from around the world.