METHODS: Staff members of a teaching hospital medical imaging department were invited to complete the generic short version of the Safety Attitude Questionnaire (SAQ). Internal consistency and reliability were evaluated using Cronbach's α. Confirmatory factor analysis (CFA) was conducted to examine model fit. A cut-off of 60% was used to define the percentage positive responses (PPR). PPR values were compared between occupational groups.
RESULTS: A total of 300 complete responses were received and the response rate was 75.4%. In reliability analysis, the Cronbach's α for the original 32-item SAQ was 0.941. Six subscales did not demonstrate good fit with CFA. A modified five-subscale, 22-item model (SAQ-MI) showed better fit (goodness-to-fit index ≥0.9, comparative fit index ≥ 0.9, Tucker-Lewis index ≥0.9 and root mean square error of approximation ≤0.08). The Cronbach's α for the 22 items was 0.921. The final five subscales were safety and teamwork climate, job satisfaction, stress recognition, perception of management and working condition, with PPR of 62%, 68%, 57%, 61% and 60%, respectively. Statistically significant differences in PPR were observed between radiographers, doctors and others occupational groups.
CONCLUSION: The modified five-factor, 22-item SAQ-MI is a suitable tool for the evaluation of patient safety culture in a medical imaging department. Differences in patient safety culture exist between occupation groups, which will inform future intervention studies.
DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.
FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.
RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.
PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.
ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.