The complexity and importance of teamwork in health care demand drastic improvement in the existing methodology of quality assurance. There is a need to develop a quality management system (QMS) for the healthcare sector. The purpose of this paper is to propose and to verify the effectiveness of the qualitycentred management system for healthcare (QMS-H) model in providing safe and reliable health care at the organisational level. This paper presents the QMS-H model derived through an analogy with the QMS model applied in the manufacturing industry and modified according to the features of the healthcare sector, and we discuss the form it should assume and the necessary type of body of knowledge (BOK). We are trying to implement the model in several hospitals in a QMS-H research group, and we are also trying to verify the effectiveness of the model in the research group. At present, the basic foundations of QMS-H have been laid, and many hospitals have now obtained the ISO 9001 certification. Some hospitals have launched policy management and improvement at the organisational level. Since some management indices have been improved, the effectiveness of the model has been suggested.

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Our findings clarify the influence of workload on non-observance errors and may also help identify adjacent areas for specific improvements.

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.

Preventing human error in healthcare is a difficult challenge, with multiple approaches to developing prevention methods and tools. The purpose of this article was to construct a method for preventing human error in medical device use from the perspective of Total Quality Management (TQM). Drawing on cases of errors made when using medical devices, error mechanisms were identified. Considering aspects of humans, medical devices, and interactions between these, we investigated error behaviours, as well as their inducing factors and situations. The methods of eliminating those factors causing medical error behaviours were proposed based on the behavioural mechanism of the error. The findings indicate that TQM is an effective way to reduce medical errors.