A novel approach to forecast surgery durations using machine learning techniques.

IF 2.3 3区医学 Q2 HEALTH POLICY & SERVICES

Health Care Management Science Pub Date : 2024-09-01 Epub Date: 2024-07-10 DOI:10.1007/s10729-024-09681-8

Marco Caserta, Antonio García Romero

引用次数: 0

Abstract

This study presents a methodology for predicting the duration of surgical procedures using Machine Learning (ML). The methodology incorporates a new set of predictors emphasizing the significance of surgical team dynamics and composition, including experience, familiarity, social behavior, and gender diversity. By applying ML techniques to a comprehensive dataset of over 77,000 surgeries, we achieved a 24% improvement in the mean absolute error (MAE) over a model that mimics the current approach of the decision maker. Our results also underscore the critical role of surgeon experience and team composition dynamics in enhancing prediction accuracy. These advancements can lead to more efficient operational planning and resource allocation in hospitals, potentially reducing downtime in operating rooms and improving healthcare delivery.

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利用机器学习技术预测手术持续时间的新方法。

本研究介绍了一种利用机器学习（ML）预测手术持续时间的方法。该方法结合了一组新的预测因子，强调了手术团队动态和组成的重要性，包括经验、熟悉程度、社交行为和性别多样性。通过将 ML 技术应用于超过 77,000 例手术的综合数据集，我们发现平均绝对误差 (MAE) 比模仿决策者当前方法的模型提高了 24%。我们的研究结果还强调了外科医生经验和团队组成动态对提高预测准确性的关键作用。这些进步可以提高医院的运营规划和资源分配效率，从而减少手术室的停工时间，改善医疗服务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Health Care Management Science HEALTH POLICY & SERVICES-

CiteScore

7.20

自引率

5.60%

发文量

期刊介绍： Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged. Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate. Editorial statements for the individual departments are provided below. Health Care Analytics Departmental Editors: Margrét Bjarnadóttir, University of Maryland Nan Kong, Purdue University With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes. The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics. Health Care Operations Management Departmental Editors: Nilay Tanik Argon, University of North Carolina at Chapel Hill Bob Batt, University of Wisconsin The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society. Health Care Management Science Practice Departmental Editor: Vikram Tiwari, Vanderbilt University Medical Center The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful. Health Care Productivity Analysis Departmental Editor: Jonas Schreyögg, University of Hamburg The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity. Public Health Policy and Medical Decision Making Departmental Editors: Ebru Bish, University of Alabama Julie L. Higle, University of Southern California The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems. The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that: Study high-impact problems involving health policy, treatment planning and design, and clinical applications; Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines; Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations. Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies. Emerging Topics Departmental Editor: Alec Morton, University of Strathclyde Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.