Haizhe Jin

Analysis of medication incident for improvement of medication process

Purpose. This study aims to develop a viewpoint list for the analysis of medication incidents since it is important for hospitals to tackle malpractice in order to deliver safe medical services. Incident reports were collected to achieve this goal. However, the number of accidents is not decreasing. In particular, medical incidents caused when nurses administer medication by injection or internally occur in many hospitals. Methodology/approach. A total of 513 incidents are analysed with the medication model to develop a viewpoint list to it make easier to extract direct error factors to develop countermeasures with questions. Each incident is stratified with the pattern described with the medication model. It was applied with 20 incidents to verify the effect. Findings. This method using the medication model and viewpoint list could detect error factors effectively. Research limitations/implications. The implementation of the revised viewpoint list to ward nurses is necessary since 20 incidents to verify the effect of the viewpoint list were analysed by the authors.

Quality management system for health care and its effectiveness

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 quality-centred 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.

A Study on the Methodology to Analyse and Prevent Medical Errors Due to Non-observance

It is necessary to tackle medical errors in order to provide safe healthcare. Medical errors are defined as departures from standards and can be divided into 2 categories: the first occurs even though workers follow standards, and the second occurs because they act contrary to standards and is called non-observance. Although some studies, as typified by error proofs, have been performed on the former, there are few studies on the latter. Therefore, errors due to non-observance chronically occur in hospitals. In this paper, we define non-observance as intentional departure from standards and discuss a mechanism generating non-observance. Furthermore, we propose a method to analyse non-observance and to prevent it by improving work methods and education.

Operational process improvement in medical TQM: a case study of human error in using devices

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.

Four steps to reduce medical incidents

Purpose – In order to improve working methods, this study proposes a method for the analysis of medication incidents and the systematic planning of error‐proofing (EP) countermeasures, in the hope that it might contribute to a reduction in medication incidents.Design/methodology/approach – In order to simplify the process of planning EP countermeasures, the following approaches are employed in this study. Improvement elements are extracted in order to plan EP countermeasures. The improvement elements that caused the error‐factor are called improvement objects, and the authors designed the extraction set of improvement objects. The authors correlated the improvement objects with recommended EP solutions. Finally, these parameters are collated. Moreover, these tools are summarized as a procedure for analysis of such incidents and for the creation of appropriate EP countermeasures.Findings – Using this approach, this paper suggests four steps to reduce medical incidents. The proposed procedure can facilitate...

The effect of workload on nurses' non-observance errors in medication administration processes: A cross-sectional study: Relationship between workload and non-observan

AIM This study, based on actual medical error cases involving nurses, sought to identify non-observance errors-defying the standard operating procedures-in medication administration processes, and clarify the relationship between nursing workload and such behaviours. METHODS Based on a cross-sectional survey, non-observance error cases were collected from three Japanese hospitals between January and December 2014, using self-reported data from participating nurses. Standard operating procedures and actual error content were compared to identify non-observance errors and workload. The statistical analysis was used to determine the relationship between non-observance error and workload. RESULTS A total of 637 error cases were found in administering medication, of which 163 (25.6%) were workload-related non-observance errors. Individual analysis of the 163 cases identified seven workload issues that caused non-observance error and six categories of non-observance errors. The relationship between workload and such errors was also clarified. CONCLUSION Our findings clarify the influence of workload on non-observance errors and may also help identify adjacent areas for specific improvements.