Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.

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.

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.

Background: The increasing prevalence of comorbidities worldwide has spurred the need for time-effective pre-hospital emergency medical services (EMS). Some pre-hospital emergency calls requesting EMS result in patient non-conveyance. Decisions for non-conveyance are sometimes driven by the patient or the clinician, which may jeopardize the patients’ healthcare outcomes. This study aimed to explore the distribution and determinants of patient non-conveyance to hospitals in a Middle Eastern national Ambulance Service that promotes the transportation of all emergency call patients and does not adopt clinician-based non-conveyance decision. Methods: Using R Language, descriptive, bivariate, and binary logistic regression analyses were conducted for 334,392 multi-national patient non-conveyance emergency calls from June 2018 to July 2022, from a total of 1,030,228 calls to which a response unit was dispatched. Results: After data pre-processing, 237,862 cases of patient non-conveyance to hospital were retained, with a monthly average of 41.96% (n = 8799) of the emergency service demands and a standard deviation of 5.49% (n = 2040.63). They predominantly involved South Asians (29.36%, n = 69,849); 64.50% (n = 153,427) were of the age category from 14 to 44 years; 61.22% (n = 145,610) were male; 74.59% (n = 177,424) from the urban setting; and 71.28% (n = 169,552) had received on-scene treatment. Binary logistic regression with full variables and backward methods identified the final models of the determinants of patient non-conveyance decisions with an Akaike information criterion prediction estimator, respectively, of (250,200) and (250,169), indicating no significant difference between both models (Chi-square test; p-value = 0.63). Conclusions: Despite exercising a cautious protocol by encouraging patient transportation to hospital, patient non-conveyance seems to be a problem in the healthcare system that strains the pre-hospital medical response teams’ resources. Policies and regulations should be adopted to encourage individuals to access other primary care centers when required rather than draining emergency services for non-emergency situations.

Since 1970, Middle East and North African (MENA) countries have witnessed evolutionary industrial development and long‐running terrorist and inter‐country conflicts. Additionally, the risk of accidental, deliberate or natural chemical, biological, radiological, and nuclear (CBRN) threats has also increased, requiring a collaborative review of the health sectors’ preparedness for potential CBRN risks within the region. This study aimed to explore the opinion of multidisciplinary experts interested in disaster management research in MENA about perspectives and challenges on readiness for potential CBRN incidents.A modified interview online link was sent to the participants. A qualitative thematic analysis was performed on the responses using Nvivo®12 software, following semi‐structured modified interviews using the Phonic® application.A total of 29 participants were sufficient to reach the data saturation. Through an inductive coding approach, five themes were identified: ‘CBRN‐Related Incidents are a Threat’, ‘Inadequate National Practice and Policy’, ‘Need for International Cooperation’, ‘Importance of Better Mass Gathering Management’, and ‘Ineffective Hospital Preparedness’.With the assistance of the World Health Organization, there is a consensus on the need for cooperation between the MENA countries to ensure adequate healthcare preparedness for CBRN threats. Suggested measures should be considered, such as creating a unified database and liaison officer designation.