Data-driven approach to improving the risk assessment process of medical failures

Shih Heng Yu, Emily Chia Yu Su, Yi Tui Chen

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number2069
JournalInternational Journal of Environmental Research and Public Health
Volume15
Issue number10
DOIs
Publication statusPublished - Oct 1 2018

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Keywords

  • Data envelopment analysis
  • Failure mode and effects analysis
  • Healthcare
  • Medical failure
  • Novel data-driven approach

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

Cite this

Data-driven approach to improving the risk assessment process of medical failures. / Yu, Shih Heng; Su, Emily Chia Yu; Chen, Yi Tui.

In: International Journal of Environmental Research and Public Health, Vol. 15, No. 10, 2069, 01.10.2018.

Research output: Contribution to journalArticle

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