人工智能患者调度在现实世界的医疗保健设置:元叙述回顾

IF 3.4 3区 医学 Q1 HEALTH POLICY & SERVICES
Dacre R.T. Knight , Christopher A. Aakre , Christopher V. Anstine , Bala Munipalli , Parisa Biazar , Ghada Mitri , Jose Raul Valery , Tara Brigham , Shehzad K. Niazi , Adam I. Perlman , John D. Halamka , Abd Moain Abu Dabrh
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引用次数: 0

摘要

目的人工智能(AI)和机器学习(ML)在医疗实践调度中的应用对大多数专业都有相当大的影响。然而,人工智能和机器学习在调度优化中的应用前景尚不明朗。我们的目的是系统地总结有关人工智能和机器学习模型在临床设置调度优化中的应用的最新证据。方法我们系统地检索了从成立到2020年8月的多个数据库,以确定描述AI和ML在医疗保健计划和报告结果中的实际应用的研究。符合条件的研究包括在任何医疗保健环境中使用ML或通过人工智能进行预测建模,以优化实时、现实环境中的患者调度流程的研究。感兴趣的结果包括评估对利益相关者(即提供者、患者、卫生系统)的影响,包括对工作量、负担、倦怠、成本、利用率、患者和提供者满意度、减少浪费和质量的影响。两名审稿人独立、盲目地重复提取和审查数据。使用元叙事方法对结果进行综合和总结。结果最初的搜索策略产生了3415条引用,其中包括11项符合条件的研究。错过预约研究的结果指标包括患者重复预约量、错过预约、服务使用和错过预约风险。资源分配结果评估了等待时间、疾病类型匹配性能、计划效率收入和新患者数量等待时间。其他结果包括访问请求、检查时间预测和手术病例时间。结论现有证据表明,人工智能和机器学习发展阶段在患者调度方面存在异质性。人工智能和机器学习应用程序可用于减少提供者的时间负担,提高患者满意度,并最终为医疗实践提供更多以患者为导向的医疗保健和效率。这些发现有助于确定开发人工智能平台以优化患者日程安排的其他机会。人工智能(AI)和机器学习(ML)可以在医疗保健的许多方面提供帮助。患者日程安排对改进技术的成本效益具有重要意义。技术在医学中的长期应用为未来的人工智能应用奠定了坚实的基础。在这里,我们介绍了目前在卫生保健诊所设置中使用人工智能和机器学习进行时间表优化的最新综述。目前的证据表明,人工智能和机器学习在患者调度中的发展、功能和应用处于各种各样的阶段。鉴于目前的知识差距,未来的研究应解决人工智能在患者安排中的可行性、有效性、普遍性和风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Objectives

The application of artificial intelligence (AI) and machine learning (ML) to scheduling in medical practices has considerable implications for most specialties. However, the landscape of AI and ML use in scheduling optimization is unclear. We aimed to systematically summarize up-to-date evidence about application of AI and ML models for scheduling optimization in clinical settings.

Methods

We systematically searched multiple databases from inception through August 2020 to identify studies that described real-world application of AI and ML in health care scheduling and reported outcomes. Eligible studies included those conducted in any health care setting using ML or predictive modeling through AI to optimize patient scheduling processes in real-time, real-world settings. Outcomes of interest included assessing impact on stakeholders (i.e., providers, patients, health systems), including impact on workload, burden, burnout, cost, utilization, patient and provider satisfaction, waste reduction, and quality. Data were extracted and reviewed in duplicates, independently and blindly by two reviewers. The results were synthesized and summarized using a metanarrative approach.

Results

The initial search strategy yielded 3,415 citations, of which 11 eligible studies were included. Outcome measures for studies on missed appointments covered patient double-booking volume, missed appointments, service use, and missed appointment risk. Resource allocation outcomes assessed wait time, disease-type matching performance, schedule efficiency revenue, and new patient volume wait time. Other outcomes included visit requests, examination length prediction, and surgical case time.

Conclusions

Available evidence shows heterogeneity in the stages of AI and ML development as they apply to patient scheduling. AI and ML applications can be used to decrease the burden on provider time, increase patient satisfaction, and ultimately provide more patient-directed health care and efficiency for medical practices. These findings help identify additional opportunities in which AI platforms can be developed to optimize patient scheduling.

Public Interest Summary

Artificial Intelligence (AI) and machine learning (ML) can help many aspects of health care. Patient scheduling has significant implications for the cost benefits of improved technology. The longstanding use of technology in medicine serves as a strong foundation for future AI applications. Here, we present an up-to-date review of the current use of AI and ML for schedule optimization in the health care clinic setting. Current evidence shows a wide variety of stages in the development, function, and application of AI and ML in patient scheduling. Given the current gaps of knowledge, future studies should address feasibility, effectiveness, generalizability, and risk of AI bias in patient scheduling.

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来源期刊
Health Policy and Technology
Health Policy and Technology Medicine-Health Policy
CiteScore
9.20
自引率
3.30%
发文量
78
审稿时长
88 days
期刊介绍: Health Policy and Technology (HPT), is the official journal of the Fellowship of Postgraduate Medicine (FPM), a cross-disciplinary journal, which focuses on past, present and future health policy and the role of technology in clinical and non-clinical national and international health environments. HPT provides a further excellent way for the FPM to continue to make important national and international contributions to development of policy and practice within medicine and related disciplines. The aim of HPT is to publish relevant, timely and accessible articles and commentaries to support policy-makers, health professionals, health technology providers, patient groups and academia interested in health policy and technology. Topics covered by HPT will include: - Health technology, including drug discovery, diagnostics, medicines, devices, therapeutic delivery and eHealth systems - Cross-national comparisons on health policy using evidence-based approaches - National studies on health policy to determine the outcomes of technology-driven initiatives - Cross-border eHealth including health tourism - The digital divide in mobility, access and affordability of healthcare - Health technology assessment (HTA) methods and tools for evaluating the effectiveness of clinical and non-clinical health technologies - Health and eHealth indicators and benchmarks (measure/metrics) for understanding the adoption and diffusion of health technologies - Health and eHealth models and frameworks to support policy-makers and other stakeholders in decision-making - Stakeholder engagement with health technologies (clinical and patient/citizen buy-in) - Regulation and health economics
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