Data mining to support simulation modeling of patient flow in hospitals.
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.
Isken
Mark W
MW
Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, Michigan 48309, USA. isken@oakland.edu
Rajagopalan
Balaji
B
D000465:Algorithms; D001509:Bed Occupancy; D016000:Cluster Analysis; D003198:Computer Simulation; D003625:Data Collection; D003660:Decision Support Systems, Management; D003953:Diagnosis-Related Groups; D005260:Female; D006742:Hospital Bed Capacity; D006801:Humans; D007297:Inpatients / Q000145:classification*; D007902:Length of Stay; D015146:Midwestern United States; D015233:Models, Statistical; D009775:Obstetrics and Gynecology Department, Hospital / Q000706:statistics & numerical data*; D011247:Pregnancy