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Flexible Services and Manufacturing Journal

, Volume 28, Issue 4, pp 593–616 | Cite as

RFID analytics for hospital ward management

  • Chun-Hung Cheng
  • Yong-Hong Kuo
Article

Abstract

In this paper, we present an RFID-enabled platform for hospital ward management. Active RFID tags are attached to individuals and assets in the wards. Active RFID readers communicate with the tags continuously and automatically to keep track of the real-time information about the locations of the tagged objects. The data regarding the locations and other transmitted information are stored in the ward management system. This platform enables capabilities of real-time monitoring and tracking of individuals and assets, reporting of ward statistics, and providing intelligence and analytics for hospital ward management. All of these capabilities benefit hospital ward management by enhanced patient safety, increased operational efficiency and throughput, and mitigation of risk of infectious disease widespread. A prototype developed based on our proposed architecture of the platform was tested in a pilot study, which was conducted in two medical wards of the intensive care unit of one of the largest public general hospitals in Hong Kong. This pilot study demonstrates the feasibility of the implementation of this RFID-enabled platform for practical use in hospital wards. Furthermore, the data collected from the pilot study are used to provide data analytics for hospital ward management.

Keywords

Big data analytics Real-time tracking and monitoring  Traceability Healthcare management Hospital ward management Infectious disease risk mitigation 

Notes

Acknowledgments

Professor Cheng’s work is substantially supported by a grant from Asian Institute of Supply Chains and Logistics (Project No. 8116027), a grant from PROCORE: France/Hong Kong Joint Research Scheme of Research Grant Council Hong Kong and the Consulate General of France in Hong Kong (Project No. 2900239) and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 14201314). Dr Kuo’s work is partially supported by a grant from Microsoft Research Asia Collaborative Research Fund and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 14202115). The authors are grateful to the Prince of Wales Hospital in Hong Kong for their assistance in this project and also indebted to the editors and the anonymous referees for their valuable comments and suggestions, which have greatly enhanced the quality of this paper.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatinHong Kong
  2. 2.Stanley Ho Big Data Decision Analytics Research CentreThe Chinese University of Hong KongShatinHong Kong

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