Assessing the experience and attitude of emergency medical services staff toward linguistic diversity challenges in a Middle Eastern pre-hospital emergency care environment using machine learning analysis methods.

Background: Language barriers significantly impact healthcare delivery, particularly in emergency medical services (EMS) operating in linguistically diverse environments. The demographic composition of Qatar, with its predominantly expatriate population, presents unique challenges for effective communication in pre-hospital care settings. The aim of this was to assess the opinions of personnel from the Hamad Medical Corporation Ambulance Service (HMCAS) regarding the impact of language barriers on pre-hospital emergency care.

Methods: A cross-sectional study was conducted using an anonymous survey with a five-point Likert scale among 312 frontline personnel of HMCAS. Fisher's exact and Kruskal-Wallis tests were used to compare ordinal outcomes across groups. Machine learning algorithms, including ordinal logistic regression, support vector machines (SVM), and naive Bayes, were used to develop predictive models for HMCAS staff opinions on their language learning needs.

Results: Both bivariate and multivariate analyses revealed significant differences in the frequency of experiencing communication challenges. The most influential factors identified were strong opinions on language barriers and the willingness of staff to enhance their language skills. Variables related to using family members as interpreters showed relatively low importance. The SVM model demonstrated the best predictive capability concerning staff perceptions about language learning needs, with an accuracy of 0.50 and an average area under the curve score of 0.74.

Conclusion: Language barriers significantly impact pre-hospital emergency care in Qatar. The findings highlight the need for targeted interventions, such as language training programs and mobile translation apps. These strategies could enhance communication in multicultural EMS settings, improving patient care and reducing miscommunication risks. Future research should evaluate the long-term impact of these interventions on patient outcomes.