Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66220
Title: An intelligent medical replenishment system for managing the medical resources in the healthcare industry
Authors: Tang, V
Cheng, SWY
Choy, KL 
Siu, PKY
Ho, GTS
Lam, HY
Keywords: Care and attention home service
Fuzzy association rule mining
Fuzzy logic
Healthcare industry
Medical resource replenishment
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016, 7737682, p. 154-161 How to cite?
Abstract: Due to rapidly ageing population, the need for care and attention homes for the elderly and patient with chronic illnesses has increased significantly in recent years. However, the continuous increase in operation and medical costs and the problem of drugs shortages bring increasing pressure to care and attention homes in regard to medical resource allocation. In such situations, patients may not receive appropriate treatment and hence dissatisfaction with the quality of service may result. Therefore, it is essential to have a decision support system to ensure that an optimal amount of medical resources are stored so as to maintain a sustainable healthcare service. In this paper, an intelligent medical replenishment system (IMRS) is proposed to assist healthcare workers in arranging the appropriate type and quantity of drugs, based on the needs of patients. In IMRS, artificial intelligent techniques, i.e. fuzzy association rules mining and fuzzy logic, are applied to evaluate the historical diagnosis records of patients and determine the amount and frequency of medical resources for replenishment. To validate the feasibility of the proposed system, a pilot study is conducted in a care and attention home located in Hong Kong. The result shows that the IMRS is effective in improving the healthcare service quality for the elderly in terms of the elderly satisfaction and medical resources fulfillment.
Description: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, Vancouver, Canada, 24-29 July 2016
URI: http://hdl.handle.net/10397/66220
ISBN: 9781509006250
DOI: 10.1109/FUZZ-IEEE.2016.7737682
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