Application of “Human Factor Analysis and Classification System” (HFACS) Model to the Prevention of Medical Errors and Adverse Events: A Systematic Review
Bibliographic record
Abstract
Hospitals, as one of most important subsectors in human societies, are responsible for providing safe and effective medical services to clients. But sometimes these hospitals are the source of injury and death in patients by creating medical errors. In this systematic review study, the application of human factor analysis and classification system (HFACS) method in the classification of medical errors was investigated. Major electronic databases including Scopus, Web of Science, and MEDLINE were searched. All studies that investigated the application of HFACS method for coding, causation, and classification of medical errors and adverse events conducted from 2001 until February 2021 were included. A total of 108 articles were found. Due to duplication, 18 studies were removed from the review list. After reading the titles and abstracts, 50 of these publications were excluded because they had objectives different from this review. The remaining 40 publications were retrieved for further assessment. Of these, 28 publications were excluded because it did not meet the inclusion criteria. Finally, 12 articles remained for the final systematic review. We found that in 65% of the selected studies, preconditions for unsafe acts have been the major causal level of medical errors and adverse events. In the majority of the studies, communication and coordination, adverse mental states, physical environment, crew resource management, and technological environment have also been recognized as the most important causal categories in this study. As a result, to prevent medical errors and adverse events, the main focus should be on controlling the preconditions for unsafe acts including personnel factors, operator conditions, and environmental factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".