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Development of the Patient Scheduling System to Handle Patient No-Show Problem Using Data Mining and Simulation-Based Optimization Techniques.

机译：使用数据挖掘和基于仿真的优化技术开发可处理患者未出现问题的患者调度系统。

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摘要

The number of patients who do not show up for their appointments significantly impacts the delivery of care, cost, and resource planning in most healthcare systems. We studied the effects of the no-show problem on Veterans Healthcare Administration (VHA) in zone 16 and specifically Michael E. DeBakey VA Medical Center (MEDVAMC) in Houston with more than 750,000 visits per year. One of the promising methodologies to resolve the no-show problem is overbooking, i.e., booking more patients than available appointment slots. The objective of this research is to develop a methodology and decision support system to assist the appointment scheduler in patient overbooking and scheduling. The appointment scheduler needs to find the appropriate patient appointment slots that minimize the patients' waiting times and the overtime working of physicians and staff, as well as maximizing the resource utilization. Based on the relevant historical data for scheduled and walk-in patients collected for two recent years in MEDVAMC, three specific tasks have been conducted:;(1) We conducted statistical and economic analysis of no-show rates and costs in VA hospitals in zone 16 and specifically in MEDVAMC.;(2) We developed the prediction models for the patient show-up probability and the number of walk-in patients, respectively. We identified the significant factors on no-show by analyzing maximum likelihood estimates of the logistic regression model and the stepwise selection technique. Furthermore, for estimating the probability of patient's no-show, we developed two data mining models based on logistic regression and support vector machines (SVMs). We also developed the prediction model for the number of walk-ins by using the density function technique.;(3) We developed the dynamic outpatient appointment overbooking and scheduling model that builds the patient schedule sequentially through a call-in process. A simulation-based optimization model is developed that can involve the realistic characteristics of the appointment scheduling system. The proposed model relaxes unrealistic assumptions in the existing analytical models. We designed sensitivity analyses for the coefficients of the objective function and simulation iteration using numerical experiments. Finally, the efficiency of our model is tested by numerical experiments.
机译：不参加约会的患者数量会极大地影响大多数医疗保健系统中的护理，成本和资源计划的交付。我们研究了未出现问题对第16区退伍军人医疗管理局（VHA）的影响，特别是休斯顿的迈克尔E.德巴基VA医疗中心（MEDVAMC），每年的访问量超过750,000。解决未出现问题的一种有前途的方法是超额预订，即预订的病人多于可用的预约空位。这项研究的目的是开发一种方法和决策支持系统，以协助预约计划者进行患者超额预订和计划。预约计划者需要找到合适的患者预约空档，以最大程度地减少患者的等待时间以及医生和工作人员的加班工作，并最大程度地利用资源。根据最近两年在MEDVAMC中收集的定期和无固定时间患者的相关历史数据，已执行了三个具体任务：（1）对区域内VA医院的未出现率和费用进行了统计和经济分析（16），特别是在MEDVAMC中。（2）我们分别针对患者出现概率和门诊患者人数建立了预测模型。通过分析逻辑回归模型的最大似然估计和逐步选择技术，我们确定了未出现的重要因素。此外，为了估计患者未出现的可能性，我们基于逻辑回归和支持向量机（SVM）开发了两个数据挖掘模型。我们还使用密度函数技术开发了步入次数的预测模型。（3）开发了动态门诊预约超量预定和调度模型，该模型通过呼叫过程顺序建立患者调度。开发了一个基于仿真的优化模型，该模型可能涉及约会调度系统的实际特征。提出的模型放宽了现有分析模型中不切实际的假设。我们使用数值实验设计了目标函数系数的敏感性分析和仿真迭代。最后，通过数值实验测试了我们模型的效率。

著录项

  • 作者

    Kheirkhah, Parviz.;

  • 作者单位

    University of Houston.;

  • 授予单位 University of Houston.;
  • 学科 Engineering Industrial.;Health Sciences Health Care Management.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 100 p.
  • 总页数 100
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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