In a world increasingly vulnerable to environmental and health crises, sustainable healthcare systems are crucial. This study focuses on the resilience and sustainability of healthcare services in Saudi Arabia’s Eastern Province, assessing their readiness to endure and adapt amidst disaster scenarios. Conducted through a cross-sectional survey involving 522 hospital employees, the research provides an in-depth analysis of various sustainability-related aspects of healthcare preparedness. This includes examining both structural and non-structural safety elements, standard operating procedures, staff training in disaster response, data security, and sustainable management of resources during emergencies. The results reveal a noteworthy commitment to sustainable disaster preparedness: 53.7% of hospitals, predominantly governed by the Ministry of Health, have implemented comprehensive disaster recovery plans. Furthermore, the study found a strong representation of female participants (54.2%) in these hospitals. A significant 64.1% of respondents confirmed the existence of disaster recovery plans in their workplaces, and an even higher percentage, 70.6%, reported receiving specific training in disaster response. These findings underscore the proactive efforts of Eastern Province hospitals in fostering a sustainable and resilient healthcare framework, demonstrating an approach that prioritizes preparedness beyond immediate crisis response. This proactive stance is pivotal in ensuring the continuity of healthcare services amidst the ever-evolving nature of global disasters.

Background: Hazardous Material—Chemical, Biological, Radiological, and Nuclear (HazMat‐CBRN) incidents, though infrequent, are environmentally precarious and perilous to living beings. They can be deliberate or accidental or follow the re‐emergence of highly contagious diseases. Successful management of such incidents in pre‐hospital settings requires having well‐trained and prepared healthcare workers.Aims: This study aimed to explore the reliability and validity of a satisfaction survey, answered by Specialized Emergency Management (SEM) personnel from a national Middle Eastern ambulance service, with a “Hazardous Material Incident Management” course offered to them as a continuing professional development activity and seek their opinion regarding Hamad Medical Corporation Ambulance Service personnel needs for other HazMat‐CBRN related training topics.Method: In the cross‐sectional study, we conducted an online satisfaction survey for this group of course participants to obtain their feedback as subject matter experts. Aiken's content validity coefficient (CVC) was calculated to assess the content validity. Cronbach'sαcoefficient was determined to explore the survey's reliability. IBM®‐SPSS® version 26 was utilized to explore the data.Results: The SEM satisfaction survey demonstrated important satisfaction with the implemented training with its robust reliability and content validity (Cronbach's α = 0.922 and CVC = 0.952). The participants also recommended additional related topics.Conclusion: Sustaining and reinforcing the HazMat‐CBRN Incident Management course was strongly recommended, considering the increase of HazMat‐CBRN threats worldwide.

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.

Objective: Chemical, biological, radiological, and nuclear (CBRN) incidents require meticulous preparedness, particularly in the Middle East and North Africa (MENA) region. This study evaluated CBRN response operational flowcharts, tabletop training scenarios methods, and a health sector preparedness assessment tool specific to the MENA region. Methods: An online Delphi survey engaging international disaster medicine experts was conducted. Content validity indices (CVIs) were used to validate the items. Consensus metrics, including interquartile ranges (IQRs) and Kendall’s W coefficient, were utilized to assess the panelists’ agreement levels. Advanced artificial intelligence computing methods, including sentiment analysis and machine-learning methods (t-distributed stochastic neighbor embedding [t-SNE] and k-means), were used to cluster the consensus data. Results: Forty experts participated in this study. The item-level CVIs for the CBRN response flowcharts, preparedness assessment tool, and tabletop scenarios were 0.96, 0.85, and 0.84, respectively, indicating strong content validity. Consensus analysis demonstrated an IQR of 0 for most items and a strong Kendall’s W coefficient, indicating a high level of agreement among the panelists. The t-SNE and k-means identified four clusters with greater European response engagement. Conclusions: This study validated essential CBRN preparedness and response tools using broad expert consensus, demonstrating their applicability across different geographic areas.

This study aimed to determine whether the Hamad Medical Corporation Ambulance Service (HMCAS) personnel fulfil the pre-hospital readiness requirements for hazardous material and chemical, biological, radiological, and nuclear (HazMat-CBRN) incidents. This cross-sectional study performed an online assessment of non-specialist paramedics’ behaviour and knowledge about HazMat-CBRN incident management, followed by a ‘HazMat-CBRN incident management’ course with pre-and post-activity assessments. The validity and reliability of the knowledge assessment questions were also tested. The pre-and-post course assessement responses revealed certain deficiencies in staff knowledge. The multiple linear regression and paired groups t-test demonstrated that this was rectified after the training intervention. The results indicate that the implemented course helped HMCAS staff acquire a satisfactory level of knowledge to ensure their readiness for safe and effective responses to potential HazMat-CBRN incidents in Qatar.