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Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures

Author

Listed:
  • Shih-Heng Yu

    (Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei 10845, Taiwan)

  • Emily Chia-Yu Su

    (Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
    Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 11031, Taiwan)

  • Yi-Tui Chen

    (Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei 10845, Taiwan)

Abstract

In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method—failure mode and effects analysis (FMEA)—for risk assessment and quality improvement, this paper presents two models developed using data envelopment analysis (DEA). One is called the slacks-based measure DEA (SBM-DEA) model, and the other is a novel data-driven approach (NDA) that combines FMEA and DEA. The relative advantages of the three models are compared. In this paper, an infant security case consisting of 16 failure modes at Western Wake Medical Center in Raleigh, North Carolina, U.S., was employed. The results indicate that both SBM-DEA and NDA may improve the discrimination and accuracy of detection compared to the traditional method of FMEA. However, NDA was found to have a relative advantage over SBM-DEA due to its risk assessment capability and precise detection of medical failures.

Suggested Citation

  • Shih-Heng Yu & Emily Chia-Yu Su & Yi-Tui Chen, 2018. "Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures," IJERPH, MDPI, vol. 15(10), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2069-:d:171193
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    References listed on IDEAS

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    1. Joe Zhu, 2014. "Quantitative Models for Performance Evaluation and Benchmarking," International Series in Operations Research and Management Science, Springer, edition 3, number 978-3-319-06647-9, September.
    2. Juan Aparicio & José Ruiz & Inmaculada Sirvent, 2007. "Closest targets and minimum distance to the Pareto-efficient frontier in DEA," Journal of Productivity Analysis, Springer, vol. 28(3), pages 209-218, December.
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    4. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    5. R. Parameshwaran & P.S.S. Srinivasan & M. Punniyamoorthy, 2010. "An integrated approach for performance enhancement in automobile repair shops," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 3(1), pages 77-104.
    6. Yasar A. Ozcan, 2014. "Health Care Benchmarking and Performance Evaluation," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4899-7472-3, September.
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    Cited by:

    1. Shuzhi Lin & Ningsheng Wang & Biqi Ren & Shuang Lei & Bianling Feng, 2022. "Use of Failure Mode and Effects Analysis (FMEA) for Risk Analysis of Drug Use in Patients with Lung Cancer," IJERPH, MDPI, vol. 19(23), pages 1-20, November.

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