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Using cluster analysis for medical resource decision making

D Dilts1, J Khamalah, A Plotkin

  • 1Department of Management Sciences, University of Waterloo, Ontario, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|October 1, 1995
PubMed
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This study addresses healthcare cost management by classifying hospital patients into groups based on resource demands. It guides researchers in selecting appropriate cluster analysis techniques for medical resource utilization, using low-vision patients as an example.

Area of Science:

  • Health Services Management
  • Operations Research
  • Biostatistics

Background:

  • Rising healthcare costs necessitate efficient management strategies, drawing from manufacturing sector techniques.
  • Classifying hospital outputs into patient groups with similar resource demands aids cost control.
  • Cluster analysis is frequently used for patient classification but lacks clear methodological guidance.

Purpose of the Study:

  • To review clustering literature applied to medical resource utilization.
  • To identify critical choices for investigators using cluster analysis in healthcare.
  • To provide guidance on selecting cluster analysis techniques, exemplified by low-vision patient data.

Main Methods:

  • Literature review of clustering techniques in healthcare.

Related Experiment Videos

  • Analysis of decision-making processes for cluster analysis selection.
  • Case study: Clustering of low-vision patients to illustrate methodology.

    • Main Results:

    • Identified a gap in formalized methodologies for selecting cluster analysis techniques in medical research.
    • Provided a framework for navigating choices in applying cluster analysis to healthcare data.
    • Demonstrated practical application through the clustering of low-vision patients.

    Conclusions:

    • Effective patient classification using cluster analysis can improve healthcare resource management and cost control.
    • Clearer guidelines are needed for selecting appropriate cluster analysis methods in medical resource utilization studies.
    • This work offers a valuable resource for researchers aiming to apply cluster analysis in healthcare settings.