Application of human reliability analysis to nursing errors in hospitals.
Adverse events in hospitals, such as in surgery, anesthesia, radiology, intensive care, internal medicine, and pharmacy, are of worldwide concern and it is important, therefore, to learn from such incidents. There are currently no appropriate tools based on state-of-the art models available for the analysis of large bodies of medical incident reports. In this study, a new model was developed to facilitate medical error analysis in combination with quantitative risk assessment. This model enables detection of the organizational factors that underlie medical errors, and the expedition of decision making in terms of necessary action. Furthermore, it determines medical tasks as module practices and uses a unique coding system to describe incidents. This coding system has seven vectors for error classification: patient category, working shift, module practice, linkage chain (error type, direct threat, and indirect threat), medication, severity, and potential hazard. Such mathematical formulation permitted us to derive two parameters: error rates for module practices and weights for the aforementioned seven elements. The error rate of each module practice was calculated by dividing the annual number of incident reports of each module practice by the annual number of the corresponding module practice. The weight of a given element was calculated by the summation of incident report error rates for an element of interest. This model was applied specifically to nursing practices in six hospitals over a year; 5,339 incident reports with a total of 63,294,144 module practices conducted were analyzed. Quality assurance (QA) of our model was introduced by checking the records of quantities of practices and reproducibility of analysis of medical incident reports. For both items, QA guaranteed legitimacy of our model. Error rates for all module practices were approximately of the order 10(-4) in all hospitals. Three major organizational factors were found to underlie medical errors: "violation of rules" with a weight of 826 x 10(-4), "failure of labor management" with a weight of 661 x 10(-4), and "defects in the standardization of nursing practices" with a weight of 495 x 10(-4).
Inoue
Kayoko
K
Department Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Koizumi
Akio
A
D006761:Hospitals; D006801:Humans; D019300:Medical Errors; D008962:Models, Theoretical; D009731:Nursing Audit / Q000379:methods*; D009738:Nursing Service, Hospital / Q000458:organization & administration* / Q000592:standards*; D017064:Process Assessment, Health Care / Q000379:methods; D011786:Quality Control; D012306:Risk; D012308:Risk Management; D013647:Task Performance and Analysis