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Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems

Author

Listed:
  • Kambombo Mtonga

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Santhi Kumaran

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Chomora Mikeka

    (Department of Physics, University of Malawi, Zomba P.O. Box 280, Malawi)

  • Kayalvizhi Jayavel

    (Department of Information Technology, School of Computing, SRM Institute of Science and Technology, Kattankulathur 603203, India)

  • Jimmy Nsenga

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

Abstract

A mismatch between staffing ratios and service demand leads to overcrowding of patients in waiting rooms of health centers. Overcrowding consequently leads to excessive patient waiting times, incomplete preventive service delivery and disgruntled medical staff. Worse, due to the limited patient load that a health center can handle, patients may leave the clinic before the medical examination is complete. It is true that as one health center may be struggling with an excessive patient load, another facility in the vicinity may have a low patient turn out. A centralized hospital management system, where hospitals are able to timely exchange patient load information would allow excess patient load from an overcrowded health center to be re-assigned in a timely way to the nearest health centers. In this paper, a machine learning-based patient load prediction model for forecasting future patient loads is proposed. Given current and historical patient load data as inputs, the model outputs future predicted patient loads. Furthermore, we propose re-assigning excess patient loads to nearby facilities that have minimal load as a way to control overcrowding and reduce the number of patients that leave health facilities without receiving medical care as a result of overcrowding. The re-assigning of patients will imply a need for transportation for the patient to move from one facility to another. To avoid putting a further strain on the already fragmented ambulatory services, we assume the existence of a scheduled bus system and propose an Internet of Things (IoT) integrated smart bus system. The developed IoT system can be tagged on buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport, which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol.

Suggested Citation

  • Kambombo Mtonga & Santhi Kumaran & Chomora Mikeka & Kayalvizhi Jayavel & Jimmy Nsenga, 2019. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems," Future Internet, MDPI, vol. 11(11), pages 1-24, November.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:11:p:236-:d:285968
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    References listed on IDEAS

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    1. Alexopoulos, Christos & Goldsman, David & Fontanesi, John & Kopald, David & Wilson, James R., 2008. "Modeling patient arrivals in community clinics," Omega, Elsevier, vol. 36(1), pages 33-43, February.
    2. Georgios Fragkos & Pavlos Athanasios Apostolopoulos & Eirini Eleni Tsiropoulou, 2019. "ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems," Future Internet, MDPI, vol. 11(1), pages 1-17, January.
    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Alberto Mozo & Bruno Ordozgoiti & Sandra Gómez-Canaval, 2018. "Forecasting short-term data center network traffic load with convolutional neural networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-31, February.
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    Cited by:

    1. Abderrahim Zannou & Abdelhak Boulaalam & El Habib Nfaoui, 2020. "SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process," Future Internet, MDPI, vol. 13(1), pages 1-23, December.

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