Analysis of medication incident for improvement of medication process

Applying Intelligent Algorithms to Automate the Identification of Error Factors

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.

Underlying influence of perception of management leadership on patient safety climate in healthcare organizations - A mediation analysis approach

Objective

We aim to draw insights on how medical staff's perception of management leadership affects safety climate with key safety related dimensions-teamwork climate, job satisfaction and working conditions.

Design/Setting

A cross-sectional survey using Safety Attitude Questionnaire (SAQ) was performed in a medical center in Taichung City, Taiwan. The relationships among the dimensions in SAQ were then analyzed by structural equation modeling with a mediation analysis.

Participants

2205 physicians and nurses of the medical center participated in the survey. Because not all questions in the survey are suitable for entire hospital staff, only the valid responses (n = 1596, response rate of 72%) were extracted for analysis.

Main Outcome Measure(s)

Key measures are the direct and indirect effects of teamwork climate, job satisfaction, perception of management leadership, and working conditions on safety climate.

Results

Outcomes show that effect of perception of management leadership on safety climate is significant (standardized indirect effect of 0.892 with P-value 0.002) and fully mediated by other dimensions, where 66.9% is mediated through teamwork climate, 24.1% through working conditions and 9.0% through job satisfaction.

Conclusions

Our findings point to the importance of management leadership and the mechanism of its influence on safety climate. To improve safety climate, the implication is that commitment by management on leading safety improvement needs to be demonstrated when it implements daily supportive actions for other safety dimensions. For future improvement, development of a management system that can facilitate two-way trust between management and staff over the long term is recommended.