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临床研究信息学中的健康公平性。

Health Equity in Clinical Research Informatics.

机构信息

University of Oslo, Oslo, Norway.

出版信息

Yearb Med Inform. 2023 Aug;32(1):138-145. doi: 10.1055/s-0043-1768720. Epub 2023 Jul 6.

Abstract

OBJECTIVES

Through a scoping review, we examine in this survey what ways health equity has been promoted in clinical research informatics with patient implications and especially published in the year of 2021 (and some in 2022).

METHOD

A scoping review was conducted guided by using methods described in the Joanna Briggs Institute Manual. The review process consisted of five stages: 1) development of aim and research question, 2) literature search, 3) literature screening and selection, 4) data extraction, and 5) accumulate and report results.

RESULTS

From the 478 identified papers in 2021 on the topic of clinical research informatics with focus on health equity as a patient implication, 8 papers met our inclusion criteria. All included papers focused on artificial intelligence (AI) technology. The papers addressed health equity in clinical research informatics either through the exposure of inequity in AI-based solutions or using AI as a tool for promoting health equity in the delivery of healthcare services. While algorithmic bias poses a risk to health equity within AI-based solutions, AI has also uncovered inequity in traditional treatment and demonstrated effective complements and alternatives that promotes health equity.

CONCLUSIONS

Clinical research informatics with implications for patients still face challenges of ethical nature and clinical value. However, used prudently-for the right purpose in the right context-clinical research informatics could bring powerful tools in advancing health equity in patient care.

摘要

目的

通过范围综述,我们在本次调查中研究了临床研究信息学在哪些方面促进了健康公平,并特别关注了 2021 年(以及 2022 年的一些)发表的与患者相关的研究。

方法

本综述采用了乔安娜·布里格斯研究所手册中描述的方法进行。综述过程包括五个阶段:1)制定目的和研究问题,2)文献检索,3)文献筛选和选择,4)数据提取,5)汇总和报告结果。

结果

在 2021 年关于临床研究信息学主题的 478 篇论文中,有 8 篇符合我们的纳入标准。所有纳入的论文都集中在人工智能(AI)技术上。这些论文通过暴露 AI 解决方案中的不平等,或者使用 AI 作为工具,在医疗服务提供中促进健康公平,来解决临床研究信息学中的健康公平问题。虽然算法偏差给基于 AI 的解决方案中的健康公平带来了风险,但 AI 也揭示了传统治疗中的不平等,并展示了有效的补充和替代方案,促进了健康公平。

结论

对患者有影响的临床研究信息学仍然面临着伦理性质和临床价值的挑战。然而,如果谨慎使用——在正确的目的和正确的背景下——临床研究信息学可以为在患者护理中推进健康公平带来强大的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/880b/10751137/4e4a6045b513/10-1055-s-0043-1768720-imaurud-1.jpg

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