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Performance Evaluation of Public Non-Profit Hospitals Using a BP Artificial Neural Network: The Case of Hubei Province in China

Author

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  • Chunhui Li

    (School of Public Health, Wuhan University, 115 Donghu Road, Wuhan 430071, China)

  • Chuanhua Yu

    (School of Public Health, Wuhan University, 115 Donghu Road, Wuhan 430071, China
    Global Health Institute, Wuhan University, 115 Donghu Road, Wuhan 430071, China)

Abstract

To provide a reference for evaluating public non-profit hospitals in the new environment of medical reform, we established a performance evaluation system for public non-profit hospitals. The new “input-output” performance model for public non-profit hospitals is based on four primary indexes (input, process, output and effect) that include 11 sub-indexes and 41 items. The indicator weights were determined using the analytic hierarchy process (AHP) and entropy weight method. The BP neural network was applied to evaluate the performance of 14 level-3 public non-profit hospitals located in Hubei Province. The most stable BP neural network was produced by comparing different numbers of neurons in the hidden layer and using the “Leave-one-out” Cross Validation method. The performance evaluation system we established for public non-profit hospitals could reflect the basic goal of the new medical health system reform in China. Compared with PLSR, the result indicated that the BP neural network could be used effectively for evaluating the performance public non-profit hospitals.

Suggested Citation

  • Chunhui Li & Chuanhua Yu, 2013. "Performance Evaluation of Public Non-Profit Hospitals Using a BP Artificial Neural Network: The Case of Hubei Province in China," IJERPH, MDPI, vol. 10(8), pages 1-15, August.
    • Handle: RePEc:gam:jijerp:v:10:y:2013:i:8:p:3619-3633:d:28003
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    References listed on IDEAS

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    2. Berg, Marc & Meijerink, Yvonne & Gras, Marit & Goossensen, Anne & Schellekens, Wim & Haeck, Jan & Kallewaard, Marjon & Kingma, Herre, 2005. "Feasibility first: Developing public performance indicators on patient safety and clinical effectiveness for Dutch hospitals," Health Policy, Elsevier, vol. 75(1), pages 59-73, December.
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    1. Zhenyi Jia & Shenglu Zhou & Quanlong Su & Haomin Yi & Junxiao Wang, 2017. "Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network," IJERPH, MDPI, vol. 15(1), pages 1-14, December.
    2. Piotr Boniecki & Małgorzata Idzior-Haufa & Agnieszka A. Pilarska & Krzysztof Pilarski & Alicja Kolasa-Wiecek, 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm," IJERPH, MDPI, vol. 16(18), pages 1-9, September.

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