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Systematic predicting human error in health care providers using SHERPA technique:
A review study
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Mohammadreza Rajabloo    , Seyed Ali Kalali Sani , Hadi Savari , Iman Taghizadeh Firozjaie , Samira Safarian |
| Department of Anesthesia, School of paramedicine, Golestan University of Medical Sciences, Gorgan, Iran , imantaghizade011@gmail.com |
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Abstract: (268 Views) |
Background and aims: The consequences of medical errors can be severe, with even seemingly simple mistakes potentially leading to extended hospital stays, health complications, disability, or even death for one or more individuals. Consequently, this review study was conducted to elucidate the dimensions and components influencing the prediction and reduction of human error, utilizing the systematic human error prediction approach (SHERPA).
Methods: This review study involved an electronic search for relevant scientific documents in both Persian and English, using keywords such as "SHERPA technique," "human error prediction," and "human errors in hospitals." A total of six studies were ultimately reviewed.
Results: The results indicated that most errors in healthcare settings were related to the type of “action”, while the fewest errors occurred in the category of “selection”. In assessing risk severity across all studies, errors categorized as having unacceptable risk levels were the least frequent, whereas those with undesirable risk severity were the most common.
Conclusion: The SHERPA technique is an effective method for identifying human errors in the medical field, as it accurately determines the causes, consequences, and risk levels associated with potential errors. Given that performance errors accounted for the highest portion of incidents and had the greatest prevalence of both unacceptable and undesirable risk, prioritizing these errors in the development of control programs is essential.
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| Keywords: Medical Errors, Health Care Systems, Human |
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Full-Text [PDF 1244 kb]
(55 Downloads)
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Type of Study: Review |
Subject:
Disaster Medicine
Received: 2024/01/31 | Accepted: 2024/04/27 | Published: 2024/08/3
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