Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

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.

Enhancing Patient Safety in Prehospital Environment: Analyzing Patient Perspectives on Non-Transport Decisions With Natural Language Processing and Machine Learning.

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.

A review on waste management and compost production in the Middle East-North Africa region.

Over the last two decades, solid waste management in the Middle East-North Africa (MENA) region has been one of the major challenges due to increasing solid waste quantities and poor waste management practices. With the tremendously increasing amounts of organic waste, MENA countries are under great pressure and are facing the threats of acute air pollution, contamination of water bodies and climate change. As a result, these countries are adopting different methods to cope with this rising challenge of waste management, including composting. This review reports on the different MENA countries' organic waste quantities, disposal methods, organic waste management practices and challenges, along with the potential use and demand of compost, where information is available. The reported data are from 2009 to 2021, with the bulk of the papers being from 2014 and onwards. The total amount of municipal waste collected in the 21 countries ranged from 0.56 million tons in Mauritania to 90 million tons in Egypt, with an average of 16.42 million tons, equivalent to 1.08 kg per capita waste generation per day. Around 55% of this material is biogenous. Many treatments and repurposing methods of this material are adopted in the MENA region, mainly through composting, as it presents one of the most sustainable solutions that lead to immediate climate change mitigation. This article also presents the biotic and abiotic stressors faced by this region, which in turn affect the successful implementation of composting solutions, and proposes some solutions based on different studies conducted.

Large pedunculated colonic lipoma: a rare cause of colorectal intussusception in adults.

Colo-rectal intussusception is rare in adults and is often secondary to malignant lesions, rarely benign lesions such as colonic lipomas can also be the cause. We present the case a 60-year-old man who presented to the emergency department with acute abdominal pain. On physical examination, the abdomen was distended with diffuse tenderness. CT scan of the abdomen revealed a colo-rectal intussusception secondary to a rectal lipoma with parietal pneumatosis of the invaginated loop. An emergency laparotomy was performed. Intraoperatively the radiological findings were confirmed. A rectosigmoid resection (Hartmann's procedure) taking off the lipoma and the invaginated segment of the colon was performed and the patient had an unevent full recovery. Histopathology confirmed a 6cm sub-mucosal lipoma without evidence of malignancy. As the diagnosis of a benign disease in patients presenting with colonic intussusception can only be made on pathological examination, this entity should be managed as a malignant lesion due to the high incidence of malignancy.