Background: International research shows that medical errors (MEs) are a major threat to patient safety. The present study aimed to describe MEs and barriers to reporting them in Shiraz public hospitals, Iran. Methods: A cross-sectional, retrospective study was conducted in 10 Shiraz public hospitals in the south of Iran, 2013. Using the standardised checklist of Shiraz University of Medical Sciences (referred to the Clinical Governance Department and recorded documentations) and Uribe questionnaire, we gathered the data in the hospitals. Results: A total of 4379 MEs were recorded in 10 hospitals. The highest frequency (27.1%) was related to systematic errors. Besides, most of the errors had occurred in the largest hospital (54.9%), internal wards (36.3%), and morning shifts (55.0%). The results revealed a significant association between the MEs and wards and hospitals (p < 0.001). Moreover, individual and organisational factors were the barriers to reporting ME in the studied hospitals. Also, a significant correlation was observed between the ME reporting barriers and the participants’ job experiences (p < 0.001). Conclusion: The medical errors were highly frequent in the studied hospitals especially in the larger hospitals, morning shift and in the nursing practice. Moreover, individual and organisational factors were considered as the barriers to reporting MEs.

OBJECTIVES: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. METHODS: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. RESULTS: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). CONCLUSIONS: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
Cardiac Surgical Procedures ; Critical Care* ; Decision Support Techniques ; Forecasting ; Heart Diseases ; Humans ; Intensive Care Units* ; Iran ; Length of Stay* ; Methods ; Thoracic Surgery*