11th EAI International Conference on Mobile Multimedia Communications

Research Article

A Distributed Procurement Cost Control Scheme of Medical Materials for Regional Medical Consortiums

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  • @INPROCEEDINGS{10.4108/eai.21-6-2018.2276581,
        author={Yunxia Feng and Longkai Chai and Xu Li and Shujun Zhang and Bo Song},
        title={A Distributed Procurement Cost Control Scheme of Medical Materials for Regional Medical Consortiums},
        proceedings={11th EAI International Conference on Mobile Multimedia Communications},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2018},
        month={9},
        keywords={material demand forecast; procurement cost control; hadoop; arima},
        doi={10.4108/eai.21-6-2018.2276581}
    }
    
  • Yunxia Feng
    Longkai Chai
    Xu Li
    Shujun Zhang
    Bo Song
    Year: 2018
    A Distributed Procurement Cost Control Scheme of Medical Materials for Regional Medical Consortiums
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.21-6-2018.2276581
Yunxia Feng1, Longkai Chai1,*, Xu Li2, Shujun Zhang1, Bo Song1
  • 1: College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061
  • 2: College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061
*Contact email: 1243707486@qq.com

Abstract

Medical materials are the base of hospitals for carring out medical activities. Related research shows that the procurement cost of medical materials accounts for 20%-43.5% of the total cost of the hospital. However, the hospital's material procurement plan is still based on the experience of the planner.This procurement method is prone to overpurchase. Overpurchasing easily leads to backlog of inventory, which will seriously occupy or even waste hospital costs. In this paper, we present a precise medical material demand forecast and procurement cost control scheme, called DMPCCS (Distributed Material Procurement Cost Control Scheme) based on distributed data mining. DMPCCS assumes that the regional medical data is stored and managed on a distributed big data platform, such as Hadoop. DMPCCS uses an improved distributed ARIMA (Autoregressive Integrated Moving Average) model to predict the expected demand of each medical material for every medical institution in the region. Based on the predicted results, DMPCCS makes a corresponding procurement plan.We evaluate performance of the proposed scheme use real data from three hospitals. Results show that the proposed scheme greatly reduces the purchasing redundancy simultaneously and avoids the purchasing inefficiency. DMPCCS can be used as a reference for the purchasing plan of the medical institutions to realize more reasonable control on the purchasing cost and the cost of warehouse management.