DESIGN: Qualitative study utilising semi-structured in-depth interviews. The interviews were conducted in English language.
SETTING: Different healthcare facilities across the UAE. These facilities were accessed for data collection over a period of 3 months from January 2023 to March 2023.
PARTICIPANTS: 14 purposively selected healthcare practitioners.
INTERVENTION: No specific intervention was implemented; this study primarily aimed at gaining insights through interviews.
PRIMARY AND SECONDARY OUTCOMES: To understand the implications of language barriers on service quality, patient safety, and healthcare providers' well-being.
RESULTS: Three main themes emerged from our analysis of participants' narratives: Feeling left alone, Trying to come closer to their patients and Feeling guilty, scared and dissatisfied.
CONCLUSIONS: Based on the perspectives and experiences of participating healthcare professionals, language barriers have notably influenced the delivery of healthcare services, patient safety and the well-being of both patients and practitioners in the UAE. There is a pressing need, as highlighted by these professionals, for the inclusion of professional interpreters and the provision of training to healthcare providers to enhance effective collaboration with these interpreters.
AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.