João Lopes, Tiago Guimarães, Júlio Duarte, Manuel Santos
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引用次数: 0
Abstract
Background: Health care is facing many challenges. The recent pandemic has caused a global reflection on how clinical and organizational processes should be organized, which requires the optimization of decision-making among managers and health care professionals to deliver care that is increasingly patient-centered. The efficiency of surgical scheduling is particularly critical, as it affects waiting list management and is susceptible to suboptimal decisions due to its complexity and constraints.
Objective: In this study, in collaboration with one of the leading hospitals in Portugal, Centro Hospitalar e Universitário de Santo António (CHUdSA), a heuristic approach is proposed to optimize the management of the surgical center.
Methods: CHUdSA's surgical scheduling process was analyzed over a specific period. By testing an optimization approach, the research team was able to prove the potential of artificial intelligence (AI)-based heuristic models in minimizing scheduling penalties-the financial costs incurred by procedures that were not scheduled on time.
Results: The application of this approach demonstrated potential for significant improvements in scheduling efficiency. Notably, the implementation of the hill climbing (HC) and simulated annealing (SA) algorithms stood out in this implementation and minimized the scheduling penalty, scheduling 96.7% (415/429) and 84.4% (362/429) of surgeries, respectively. For the HC algorithm, the penalty score was 0 in the urology, obesity, and pediatric plastic surgery medical specialties. For the SA algorithm, the penalty score was 5100 in urology, 1240 in obesity, and 30 in pediatric plastic surgery. Together, this highlighted the ability of AI-heuristics to optimize the efficiency of this process and allowed for the scheduling of surgeries at closer dates compared to the manual method used by hospital professionals.
Conclusions: Integrating these solutions into the surgical scheduling process increases efficiency and reduces costs. The practical implications are significant. By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.
背景：卫生保健面临着许多挑战。最近的大流行引起了全球对如何组织临床和组织过程的反思，这需要优化管理人员和卫生保健专业人员之间的决策，以提供越来越以患者为中心的护理。手术调度的效率尤其重要，因为它影响到等待名单的管理，并且由于其复杂性和局限性，容易产生次优决策。目的：本研究与葡萄牙一家领先的医院Centro Hospitalar e Universitário de Santo António （CHUdSA）合作，提出了一种启发式方法来优化外科中心的管理。方法：对CHUdSA某一特定时期的手术安排过程进行分析。通过测试一种优化方法，研究团队能够证明基于人工智能（AI）的启发式模型在最大限度地减少调度损失方面的潜力，即未按时安排的程序所产生的财务成本。结果：该方法的应用显示了在调度效率显著提高的潜力。值得注意的是，爬山算法（HC）和模拟退火算法（SA）的实现在该实现中表现突出，调度惩罚最小，分别调度96.7%（415/429）和84.4%（362/429）的手术。在HC算法中，泌尿外科、肥胖症、小儿整形外科等医学专业的罚分为0分。对于SA算法，泌尿科的罚分为5100分，肥胖为1240分，儿科整形为30分。总之，这突出了人工智能启发式优化这一过程效率的能力，与医院专业人员使用的手动方法相比，它允许在更近的日期安排手术。结论：将这些解决方案整合到手术调度过程中可以提高效率并降低成本。其实际意义是重大的。通过实施这些人工智能驱动的策略，医院可以最大限度地减少患者等待时间，最大限度地利用资源，并通过改进计划提高手术效果。这一发展凸显了人工智能算法如何能够有效地适应不断变化的医疗保健环境，并产生变革性影响。
期刊介绍：
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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