Bending the patient safety curve: how much can AI help?

被引：20

作者：

Classen, David C. ^{[1
]}

Longhurst, Christopher ^{[2
]}

Thomas, Eric J. ^{[3
,4
]}

机构：

[1] Univ Utah, Sch Med, Div Clin Epidemiol, Salt Lake City, UT 84112 USA

[2] UC San Diego Hlth, Dept Med & Pediat, San Diego, CA USA

[3] Univ Texas Hlth Sci Ctr Houston, McGovern Med Sch, Houston, TX USA

[4] UT Houston, Mem Hermann Ctr Healthcare Quality & Safety, Houston, TX USA

来源：

NPJ DIGITAL MEDICINE | 2023年 / 6卷 / 01期

关键词：

ARTIFICIAL-INTELLIGENCE; REAL-TIME; IDENTIFICATION; RISK;

D O I：

10.1038/s41746-022-00731-5

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

This paper reviews the current state of patient safety and the application of artificial intelligence (AI) techniques to patient safety. This paper defines patient safety broadly, not just inpatient care but across the continuum of care, including diagnostic errors, misdiagnosis, adverse events, injuries, and measurement issues. It outlines the major current uses of AI in patient safety and the relative adoption of these techniques in hospitals and health systems. It also outlines some of the limitations of these AI systems and the challenges with evaluation of these systems. Finally, it outlines the importance of developing a proactive agenda for AI in healthcare that includes marked increased funding of research and evaluation in this area.

引用

页数：3

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