美国医院如何采用人工智能?2022 年的早期证据。

Health affairs scholar Pub Date : 2024-09-26 eCollection Date: 2024-10-01 DOI:10.1093/haschl/qxae123
Redwan Bin Abdul Baten
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引用次数: 0

摘要

美国医院正在迅速采用人工智能(AI),但人们对采用人工智能的医院的特点、趋势和分布却缺乏了解。本研究旨在通过分析 2022 年美国医院协会(AHA)的数据来填补这一空白。我们开发了新颖的医院人工智能应用模型(HAIAM),根据医院在以下领域的人工智能应用特点对其进行分类:(1)预测患者需求;(2)优化工作流程;(3)自动化常规任务;(4)员工调度;(5)预测人员需求。到 2022 年,将近五分之一的美国医院(1107 家或 18.70%)采用了某种形式的人工智能。HAIAM 显示,只有 3.82% 的医院是高度采用者,其次是 6.22% 的中度采用者和 8.67% 的低度采用者。在优化工作流程方面,人工智能采用率最高(12.91%),而员工调度(9.53%)的增长率最低。拥有大型病床和门诊手术部、非营利性私营医院、教学医院和医疗系统的医院更有可能采用不同形式的人工智能。新泽西州(48.94%)在医院采用人工智能方面处于领先地位,而新墨西哥州(0%)则最为落后。这些数据可以帮助政策制定者更好地了解医院在采用人工智能方面的差异,并为潜在的政策应对措施提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How are US hospitals adopting artificial intelligence? Early evidence from 2022.

US hospitals are rapidly adopting artificial intelligence (AI), but there is a lack of knowledge about AI-adopting hospitals' characteristics, trends, and spread. This study aims to fill this gap by analyzing the 2022 American Hospital Association (AHA) data. The novel Hospital AI Adoption Model (HAIAM) is developed to categorize hospitals based on their AI adoption characteristics in the fields of (1) predicting patient demand, (2) optimizing workflow, (3) automating routine tasks, (4) staff scheduling, and (5) predicting staffing needs. Nearly one-fifth of US hospitals (1107 or 18.70%) have adopted some form of AI by 2022. The HAIAM shows that only 3.82% of hospitals are high adopters, followed by 6.22% moderate and 8.67% low adopters. Artificial intelligence adoption rates are highest in optimizing workflow (12.91%), while staff scheduling (9.53%) has the lowest growth rate. Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI. New Jersey (48.94%) is the leading hospital AI-adopting state, whereas New Mexico (0%) is the most lagging. These data can help policymakers better understand variations in AI adoption by hospitals and inform potential policy responses.

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