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Medical consumable usage control based on Canopy_K-means clustering and WARM

  • Ying Yang
  • Huijing WuEmail author
  • Caixia Yan
Article
  • 6 Downloads

Abstract

Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.

Keywords

Medical consumable management Medical consumable usage control CANOPY_K-means clustering WARM 

Notes

Acknowledgements

This article was funded by Research Center of Resource Recycling Science and Engineering, Shanghai Polytechnic University and Gaoyuan Discipline of Shanghai—Environmental Science and Engineering (Resource Recycling Science and Engineering). This paper is an achievement of Project “Study of the operational mechanism and its optimization of resource management in surgical operations (71371120)” supported by National Natural Science Foundation of China. This research was supported by Shanghai Science Committee of China under Grant No. 17495810503.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Polytechnic UniversityShanghaiChina
  2. 2.Shanghai General Hospital, School of MedicineShanghai Jiaotong UniversityShanghaiChina

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