Using cluster analysis for medical resource decision making.
Escalating costs of health care delivery have in the recent past often made the health care industry investigate, adapt, and apply those management techniques relating to budgeting, resource control, and forecasting that have long been used in the manufacturing sector. A strategy that has contributed much in this direction is the definition and classification of a hospital's output into "products" or groups of patients that impose similar resource or cost demands on the hospital. Existing classification schemes have frequently employed cluster analysis in generating these groupings. Unfortunately, the myriad articles and books on clustering and classification contain few formalized selection methodologies for choosing a technique for solving a particular problem, hence they often leave the novice investigator at a loss. This paper reviews the literature on clustering, particularly as it has been applied in the medical resource-utilization domain, addresses the critical choices facing an investigator in the medical field using cluster analysis, and offers suggestions (using the example of clustering low-vision patients) for how such choices can be made.
Department of Management Sciences, University of Waterloo, Ontario, Canada.
D
D
Dilts
J
J
Khamalah
A
A
Plotkin
D000465:Algorithms; D016000:Cluster Analysis; D003661:Decision Support Techniques; D015276:Health Care Rationing; D006801:Humans; D010361:Patients / Q000145:classification*; D015203:Reproducibility of Results; D014786:Vision Disorders / Q000145:classification