释放价值：量化医院人工智能的投资回报率。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology Pub Date : 2024-10-01 DOI:10.1016/j.jacr.2024.02.034

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引用次数: 0

摘要

目的：我们开发了一个综合投资回报率（ROI）计算器，用于评估人工智能（AI）驱动的放射诊断成像平台的货币和非货币收益，为有意采用人工智能的决策者提供信息：构建了一个计算器，用于计算比较成本、估计收入，并量化在美国一家医院的放射学工作流程中使用人工智能平台与不使用人工智能平台在五年时间跨度内的临床价值。参数是根据专家访谈和文献综述确定的。进行了情景和确定性敏感性分析，以评估计算器的驱动因素：在我们的计算器中，在医院放射科工作流程中引入人工智能平台可减少劳动时间，并在五年内实现 451% 的投资回报率。如果考虑到放射科医生节省的时间，投资回报率则增加到 791%。放射科医生节省的时间包括超过 15 个 8 小时工作日的等待时间、78 天的分诊时间、10 天的读片时间和 41 天的报告时间。使用该平台还能为医院带来收入效益，因为它能让患者接受对临床有益的随访扫描、住院治疗和治疗程序。结果对时间跨度、医疗中心设置和扫描次数都很敏感。其中，影响最大的结果是因人工智能识别患者而额外进行的必要治疗次数：结论：我们证明了在一家通过卒中管理认证的医院实施人工智能平台可获得可观的五年投资回报率。投资回报率计算器可能会对评估人工智能驱动的放射学平台的决策者有所帮助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence

Purpose

A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)–powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI.

Methods

A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers.

Results

In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients.

Conclusions

The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management–accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.

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来源期刊

Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

6.30

自引率

8.90%

发文量

312

审稿时长

34 days

期刊介绍： The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.