J Dunstan, F Villena, J P Hoyos, V Riquelme, M Royer, H Ramírez, J Peypouquet
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引用次数: 2
摘要
智利的公共卫生系统为全国74%的人口提供服务，平均有19%的医疗预约因未赴约而错过。国家目标是15%，这与私营医疗系统报告的平均缺勤率一致。我们的案例研究是Luis Calvo Mackenna医生医院，这是一家位于智利圣地亚哥的公立高复杂性儿科医院和教学中心。从历史上看，它的缺勤率很高，在某些医学专业高达29%。使用机器学习算法根据人口统计、社会和历史变量预测儿科患者的缺席情况。提出并评估评估这些模型的指标，考虑可能的干预策略的成本效益影响，以减少缺勤。我们通过以下传统机器学习算法:随机森林、逻辑回归、支持向量机、AdaBoost，以及缓解班级失衡问题的算法，如RUS Boost、Balanced Random Forest、Balanced Bagging和Easy Ensemble，分析了2015年至2018年间缺勤与人口、社会和历史变量之间的关系。这些阶层不平衡的原因是未到期率相对于总预约人数而言相对较低。我们没有使用每种方法使用的默认阈值，而是根据成本效益标准，通过最小化类型I和II错误的加权平均值来计算可选的阈值。在395,963次预约中，有20.4%的人没有预约，其中眼科的预约率最高，为29.1%。根据他们的保险类型和居住公社，最贫困的社会经济群体的患者和第二次婴儿的患者有最高的缺勤率。不出席的历史与未来的不出席密切相关。一项为期8周的实验设计表明，与对照组相比，使用我们的提醒策略时，缺席率降低了10.3个百分点。在分析的变量中，那些与患者的历史行为相关的变量，预约创建的预约延迟，以及与最弱势的社会经济群体相关的变量，与预测缺勤最相关。此外，引入新的成本效益指标显著影响我们的预测模型的有效性。使用一个原型来打电话给有最高失约风险的病人，结果显著降低了总体失约率。
Predicting no-show appointments in a pediatric hospital in Chile using machine learning.
The Chilean public health system serves 74% of the country's population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients' historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.
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.
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