Resources and expenditures management in the field of medical supplies using K-means clustering algorithm: A case study

Abstract

Corresponding Author: Behrouz Moradhasel e-mail addresses: bzmoradhasel@gmail.com Introduction: One of the main concerns of health system managers has always been to control and reduce health care costs. Optimal use of health center resources to maintain and improve services requires cost analysis and application of the results obtained in the process of supply and consumption of required items. The purpose of this study was to extract a suitable model for managing the costs of medical supplies in health centers. Methods: This is an applied research and the used descriptive research method was cross-sectional. The annual purchase cost of medical supplies of health centers of Hamadan University of Medical Sciences was extracted from the hospital information system and after processing, the results were analyzed using the experiences of researchers and the criteria for medical equipment of the country. The K-means algorithm in the Rapidminer software (version 9.6) environment was used for the data clustering. Results: The annual purchase costs of medical supplies were clustered into nine categories based on the unit and total purchase price using the K-means, and then based on the analysis of each category, cost management strategies such as price reduction through centralized purchasing, concluding contracts with authorized suppliers, careful monitoring and control of consumption, and concluding a memorandum of understanding with other medical centers for the exchange of surplus or required goods were presented. Conclusion: Cost analysis of medical supplies based on price, purchase, and consumption in a given period of time using clustering algorithm provides valuable information for resources and expenditures management of the health system managers and leads to maintaining, continuing, and improving the level of services of health centers. Received: 05/Nov/2020 Modified: 09/Dec/2020 Accepted: 19/Dec/2020 Available online: 11/Jan/2021