Applying intelligent algorithms to automate the identification of error factors

Haizhe Jin*, Qingxing Qu, Masahiko Munechika, Masataka Sano, Chisato Kajihara, Vincent G. Duffy, Han Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Objectives: Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. Methods: In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Results: Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)–back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. Conclusions: The combination of “error-related items, their different levels, and the GA-BPNN model” was proposed as an error-factor identification technology, which could automatically identify medical error factors.

Original languageEnglish
Pages (from-to)E918-E928
JournalJournal of Patient Safety
Issue number8
Publication statusPublished - 2021 Dec 1


  • Error-factor identification technology
  • Error-related items
  • Healthcare
  • Human error
  • Neural networks
  • Process approach

ASJC Scopus subject areas

  • Leadership and Management
  • Public Health, Environmental and Occupational Health


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