Isken Mark W, Rajagopalan Balaji
Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, Michigan 48309, USA.
J Med Syst. 2002 Apr;26(2):179-97. doi: 10.1023/a:1014814111524.
Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.
美国不断攀升的医疗保健成本促使各机构持续应对优化稀缺资源利用这一挑战。优化资源的首要步骤之一是有效利用容量。对于诸如住院病床分配等医院容量规划问题，计算机模拟通常是首选方法。将模拟模型用于此类研究时，较困难的方面之一是创建一组可管理的患者类型纳入模型。本文的目的是展示使用数据挖掘技术，特别是诸如K均值等聚类技术的潜力，以帮助指导患者类型定义的制定，从而构建医院患者流的计算机模拟或分析模型。本研究使用来自美国中西部一家医院的数据，提出了研究人员在一般情况下应用聚类技术，特别是应用于医院数据时需要解决的几个重要问题。
