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A predictive model for patient length of stay at a teaching hospital.

机译：病人在教学医院住院时间的预测模型。

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摘要

This research attempts to construct mathematical models for estimating length of stay per admission and investigating the effects of patients' characteristics and clinical indicators on the length of stay for the top ten Diagnosis-Related Groups (DRGs) at a teaching hospital. It is also concerned with the development of cost per admission and cost per patient day functions. Further, these functions are used for analysis to determine a value of the length of stay that would minimize cost per patient day. Also, the effects of changing the length of stay (from the actual to the projected levels) on the total cost per year and the cost per patient day are examined. Moreover, the current cost system for the teaching hospital is evaluated and a new cost system (Activity-Based Costing) is proposed. The nuclear medicine unit is selected to implement the new cost system. The results indicate that the patients' characteristics and the clinical indicators explain approximately 64% of the variation in the length of stay; also model prediction is 79% accurate. The effects of the clinical indicators on the length of stay are much stronger than the patients' characteristics. The cost models fit the data as shown by the following indicators: the average of R2 is 0.79 and the mean of MAPE is 15. The cost variation analysis demonstrates that if a hospital can control the length of stay at the projected level, on an average, the cost per admission and the cost per patient day will decrease. Based on the top ten DRGs (6,367 admissions) in the year 1999, the cost per year and the cost per patient day decreased approximately 13% and 11%, respectively using the cost minimization analysis. The research confirms that the Activity-Based Costing can be applied to healthcare industry, and provides more accurate cost information than the current system. It assists management in effective cost reduction by focusing on non-value-added and providing more accurate statistics for pricing. Overall, this research offers a new decision support instrument for healthcare administrators.
机译：这项研究试图构建数学模型，以估计每次住院的住院时间，并调查教学医院中前十名与诊断相关的人群（DRG）的患者特征和临床指标对住院时间的影响。它还关注每次入院成本和每个患者日间功能成本的发展。此外，这些功能用于分析以确定停留时间的值，该值将使每位患者每天的花费最小化。同样，研究了改变住院时间（从实际水平到预计水平）对每年总费用和每位患者每天费用的影响。此外，对教学医院的当前成本系统进行了评估，并提出了一种新的成本系统（基于活动的成本核算）。选择核医学部门实施新的费用系统。结果表明，患者的特征和临床指标可解释住院时间变化的约64％。模型预测的准确率也达到79％。临床指标对住院时间的影响远强于患者的特征。费用模型符合以下指标所示的数据：R 2 的平均值为0.79，MAPE的平均值为15。费用变化分析表明，如果医院可以控制住院时间，在预计水平上，平均每次住院费用和每位患者每天的费用将减少。基于1999年的前十大DRG（入院6,367例），使用成本最小化分析，每年的成本和每位患者每天的成本分别降低了约13％和11％。研究证实，基于活动的成本核算可以应用于医疗保健行业，并且比当前系统提供更准确的成本信息。它专注于非增值业务并提供更准确的价格统计信息，从而帮助管理层有效降低成本。总体而言，这项研究为医疗保健管理员提供了一种新的决策支持工具。

著录项

  • 作者

    Suthummanon, Sakesun.;

  • 作者单位

    University of Miami.;

  • 授予单位 University of Miami.;
  • 学科 Engineering Industrial.; Health Sciences Health Care Management.; Business Administration General.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 263 p.
  • 总页数 263
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;预防医学、卫生学;贸易经济;
  • 关键词

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