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Objective: This research explored the experiences and perspectives of patients declining hospital transportation after receiving prehospital emergency care using advanced computational techniques., Method: Between 15th June and 1st August 2023, 210 patients in Qatar, treated by Hamad Medical Corporation Ambulance Service (HMCAS) but refusing transportation to hospital, were interviewed. Key outcome variables stratified by demographics included "reasons for refusing transport," "satisfaction with HMCAS service," and "postrefusal actions." Responses underwent sentiment analysis and topic modeling using latent Dirichlet allocation. Machine learning models, such as Naïve Bayes, K-nearest neighboring, random forest, and support vector machine, were used to predict patients' subsequent actions., Results: Participants had an average age of 38.61 ± 19.91 years. The chief complaints were primarily chest and abdominal pains (18.49%; n = 39). Sentiment Analysis revealed a generally favorable perception of HMCAS-provided service. Latent Dirichlet allocation identified two main topics pertaining to refusal reasons and service satisfaction. Naïve Bayes and support vector machine algorithms were most effective in predicting postrefusal actions with an accuracy rate of 81.58%., Conclusions: This study highlighted the utility of Natural Language Processing and ML in enhancing our understanding of patient behaviors and sentiments in prehospital settings. These advanced computational methodologies allowed for a nuanced exploration of patient demographics and sentiments, providing insights for Quality Improvement initiatives. The study also advocates for continuously integrating automated feedback mechanisms to improve patient-centered care in the prehospital context. Continuous integration of automated feedback systems is recommended to improve prehospital patient-centered care., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation., Methods: ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated., Results: All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients' transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified "Transported" cases (False Positive)., Conclusion: The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Farhat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Efficient resource distribution is important. Despite extensive research on response timings within ambulance services, nuances of time from unit dispatch to becoming available still need to be explored. This study aimed to identify the determinants of the duration between ambulance dispatch and readiness to respond to the next case according to the patients' transport decisions., Methods: Time from ambulance dispatch to availability (TDA) analysis according to the patients' transport decision (Transport versus Non-Transport) was conducted using R-Studio™ for a data set of 93,712 emergency calls managed by a Middle Eastern ambulance service from January to May 2023. Log-transformed Hazard Ratios (HR) were examined across diverse parameters. A Cox regression model was utilised to determine the influence of variables on TDA. Kaplan-Meier curves discerned potential variances in the time elapsed for both cohorts based on demographics and clinical indicators. A competing risk analysis assessed the probabilities of distinct outcomes occurring., Results: The median duration of elapsed TDA was 173 min for the transported patients and 73 min for those not transported. The HR unveiled Significant associations in various demographic variables. The Kaplan-Meier curves revealed variances in TDA across different nationalities and age categories. In the competing risk analysis, the 'Not Transported' group demonstrated a higher incidence of prolonged TDA than the 'Transported' group at specified time points., Conclusions: Exploring TDA offers a novel perspective on ambulance services' efficiency. Though promising, the findings necessitate further exploration across diverse settings, ensuring broader applicability. Future research should consider a comprehensive range of variables to fully harness the utility of this period as a metric for healthcare excellence., (© 2024. The Author(s).)

Background and Aim: Though emergency medical services (EMS) respond to all types of emergency calls, they do not always result in the patient being transported to the hospital. This study aimed to explore the determinants influencing emergency call-response-based conveyance decisions in a Middle Eastern ambulance service., Methods: This retrospective quantitative analysis of 93,712 emergency calls to the Hamad Medical Corporation Ambulance Service (HMCAS) between January 1 and May 31, 2023, obtained from the HMCAS electronic system, was analyzed to determine pertinent variables. Sociodemographic, emergency dispatch-related, clinical, and miscellaneous predictors were analyzed. Descriptive, bivariate, ridge logistic regression, and combination analyses were evaluated., Results: 23.95% ( N  = 21,194) and 76.05% ( N  = 67,285) resulted in patient nontransport and transportation, respectively. Sociodemographic analysis revealed that males predominantly activated EMS resources, and 60% of males ( n  = 12,687) were not transported, whilst 65% of females ( n  = 44,053) were transported. South Asians represented a significant proportion of the transported patients (36%, n  = 24,007). "Home" emerged as the primary emergency location (56%, n  = 37,725). Bivariate analysis revealed significant associations across several variables, though multicollinearity was identified as a challenge. Ridge regression analysis underscored the role of certain predictors, such as missing provisional diagnoses, in transportation decisions. The upset plot shows that hypertension and diabetes mellitus were the most common combinations in both groups., Conclusions: This study highlights the nuanced complexities governing conveyance decisions. By unveiling patterns such as male predominance, which reflects Qatar's expatriate population, and specific temporal EMS activity peaks, this study accentuates the importance of holistic patient assessment that transcends medical histories., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors. Health Science Reports published by Wiley Periodicals LLC.)

Adverse drug events encompass a wide range of potential unintended and harmful events, from adverse drug reactions to medication errors, many of which in retrospect, are considered preventable. However, the primary challenge towards reducing their burden lies in consistently identifying and monitoring these occurrences, a challenge faced across the spectrum of healthcare, including the emergency medical services. The aim of this study was to identify and describe medication related adverse events (AEs) in the out-of-hospital setting. The medication components of a dedicated patient safety register were analysed and described for the period Jan 2017-Sept 2020. Univariate descriptive analysis was used to summarize and report on basic case and patient demographics, intervention related AEs, medication related AEs, and AE severity. Multivariable logistic regression was used to assess the odds of AE severity, by AE type. A total of 3475 patient records were assessed where 161 individual medication AEs were found in 150 (4.32%), 12 of which were categorised as harmful. Failure to provide a required medication was found to be the most common error (1.67%), followed by the administration of medications outside of prescribed practice guidelines (1.18%). There was evidence to suggest a 63% increase in crude odds of any AE severity [OR 1.63 (95% CI 1.03-2.6), p = 0.035] with the medication only AEs when compared to the intervention only AEs. Prehospital medication related adverse events remain a significant threat to patient safety in this setting and warrant greater widespread attention and future identification of strategies aimed at their reduction., (© 2022. The Author(s).)

Background : Prehospital care provided by emergency medical services (EMS) plays an important role in improving patient outcomes. Globally, prehospital care varies across countries and even within the same country by the geographic location and access to medical services. We aimed to explore the prehospital trauma care and in-hospital outcomes within the urban and rural areas in the state of Qatar. Methods : A retrospective analysis was conducted utilizing data from the Qatar National Trauma Registry for trauma patients who were transported by EMS to a level 1 trauma center between 2017 and 2018. Data were analyzed and compared between urban and rural areas and among the different municipalities in which the incidents occurred. Results: Across the study duration, 1761 patients were transported by EMS. Of that, 59% were transported from an urban area and 41% from rural areas. There were significant differences in the on-scene time and total prehospital time as a function of urban and rural areas and municipalities; however, the response time across the study groups was comparable. There were no significant differences in blood transfusion, intubation, hospital length of stay, and mortality. Conclusion : Within different areas in Qatar, the EMS response time and in-hospital outcomes were comparable. This indicates that the provision of prehospital care across the country is similar. The prehospital and acute in-hospital care are accessible for everyone in the country at no cost. Understanding the differences in EMS utilization and prehospital times contributes to the policy development in terms of equitable distribution of healthcare resources.