Fig 1.
The process of medical error reporting and classification as a foundation for enhancing healthcare system resilience and safety.
Fig 2.
(A) Reason’s classification: Error categories. Adapted from [25]. (B) Reason’s classification: Definitions.
Fig 3.
Round’s classification: Descriptions.
Fig 4.
Flowchart illustrating the methodology for examining medical error associations.
The methodology involved collecting data from 124 participants and 13 tutors, exploring the relationship between medical error classifications using Kendall’s tau correlation coefficient, and identifying significant associations with thresholding. Data cleaning, inputting missing values, and linear rescaling of the resulting data matrix (105 × 15) were performed, followed by analysis to identify the most common types of medical errors and their associations with different classification systems.
Fig 5.
Relative frequency of medical errors observed during the simulation sessions as reported by participants and tutors.
The bar charts distinguish and compare two distinct medical error classification systems—the Reason’s and Round’s taxonomies. The x-axis denotes the categories of medical errors, revealing the most and least frequent types of errors within each taxonomy. RBM—Rule-based Mistakes; KBM—Knowledge-based Mistakes.
Fig 6.
Boxplot showing participants’ and tutors’ rankings of medical errors.
The y-axis represents the ranking, while the x-axis displays the categories of medical errors. The order of medical mistakes on the x-axis corresponds to the sorted frequencies, as shown in Fig 5. RBM—Rule-based Mistakes; KBM—Knowledge-based Mistakes.
Table 1.
Detected correlations between the two presented medical error classification systems: Reason’s and Round’s taxonomies.
Fig 7.
Identified correlations between Reason’s and Round’s error taxonomies, with proposed interpretations and simulation training strategies.