Integrating Inter-Professional Insights for Enhanced Disaster Response: A Cross-Sectional Analysis in Jubail's Royal Commission Hospital, Saudi Arabia.

Introduction: This study aimed to evaluate disaster preparedness and management among an inter-professional team at the Royal Commission Hospital (RCH) in Jubail, Saudi Arabia. Methods: Conducted between May and July 2023, this cross-sectional study involved healthcare providers in both patient-facing and non-patient-facing roles. Participants responded to a comprehensive online questionnaire comprising 22 questions across seven sections covering aspects of emergency response, disaster management, and infection control. The study targeted a minimum sample size of 500 participants, successfully garnering responses from 512 individuals. Results: Of the 512 participants, 59.9% (n=312) were healthcare providers in patient-facing roles, and 40.1% (n=209) were in non-patient-facing roles. The results revealed notable disparities in awareness and preparedness between these two groups. Healthcare providers demonstrated higher awareness levels compared to their non-patient-facing counterparts. For instance, 76.9% of healthcare providers were aware of the hospital's emergency response plan compared to 56.2% of non-healthcare providers (χ² = 52.165, p < 0.001). Similar disparities were observed in understanding the term "disaster" (86.5% vs 54.1%, χ² = 27.931, p < 0.001), and awareness of a command center (73.4% vs 45.2%, χ² = 42.934, p < 0.001). Discussion: These findings underscore the critical need for enhancing awareness, education, and preparedness within healthcare facilities, emphasizing an integrated approach that includes both healthcare and non-healthcare staff. By addressing these gaps, healthcare facilities can significantly improve their emergency response efficiency, disaster management capabilities, and infection control measures, thereby enhancing the overall safety and quality of patient care.

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.

Evaluation of the implementation of the objective structured clinical examination in health sciences education from a low-income context in Tunisia: A cross-sectional study.

Background: Objective structured clinical examination (OSCE) is well-established and designed to evaluate students' clinical competence and practical skills in a standardized and objective manner. While OSCEs are widespread in higher-income countries, their implementation in low-resource settings presents unique challenges that warrant further investigation. Aim: This study aims to evaluate the perception of the health sciences students and their educators regarding deploying OSCEs within the School of Health Sciences and Techniques of Sousse (SHSTS) in Tunisia and their efficacity in healthcare education compared to traditional practical examination methods. Methods: This cross-sectional study was conducted in June 2022, focusing on final-year Health Sciences students at the SHSTS in Tunisia. The study participants were students and their educators involved in the OSCEs from June 6th to June 11th, 2022. Anonymous paper-based 5-point Likert scale satisfaction surveys were distributed to the students and their educators, with a separate set of questions for each. Spearman, Mann-Whitney U and Krusakll-Wallis tests were utilized to test the differences in satisfaction with the OSCEs among the students and educators. The Wilcoxon Rank test was utilized to examine the differences in students' assessment scores in the OSCEs and the traditional practical examination methods. Results: The satisfaction scores were high among health sciences educators and above average for students, with means of 3.82 ± 1.29 and 3.15 ± 0.56, respectively. The bivariate and multivariate analyzes indicated a significant difference in the satisfaction between the students' specialities. Further, a significant difference in their assessment scores distribution in the practical examinations and OSCEs was also demonstrated, with better performance in the OSCEs. Conclusion: Our study provides evidence of the relatively high level of satisfaction with the OSCEs and better performance compared to the traditional practical examinations. These findings advocate for the efficacy of OSCEs in low-income countries and the need to sustain them.

Epidemiology of prehospital emergency calls according to patient transport decision in a middle eastern emergency care environment: Retrospective cohort-based.

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.

Exploring factors influencing time from dispatch to unit availability according to the transport decision in the pre-hospital setting: an exploratory study.

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.

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.

Romidepsin Plus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone Versus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone in Patients With Previously Untreated Peripheral T-Cell Lymphoma: Final Analysis of the Ro-CHOP Trial.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.The primary analysis of the Ro-CHOP phase III randomized controlled trial (ClinicalTrials.gov identifier: NCT01796002) established that romidepsin (Ro) plus cyclophosphamide, doxorubicin, vincristine, prednisone (CHOP) did not yield an increased efficacy compared with CHOP alone as first-line treatment of peripheral T-cell lymphoma. We report the planned final analysis 5 years after the last patient enrolled. With a median follow-up of 6 years, median progression-free survival (PFS) was 12.0 months compared with 10.2 months (hazard ratio [HR], 0.79 [95% CI, 0.62 to 1.005]; P = .054), while median overall survival was 62.2 months (35.7-86.6 months) and 43.8 months (30.1-70.2 months; HR, 0.88 [95% CI, 0.68 to 1.14]; P = .324) in the Ro-CHOP and CHOP arms, respectively. In an exploratory analysis, the median PFS in the centrally reviewed follicular helper T-cell lymphoma subgroup was significantly longer in the Ro-CHOP arm (19.5 v 10.6 months, HR, 0.703 [95% CI, 0.502 to 0.985]; P = .039). Second-line treatments were given to 251 patients with a median PFS2 and OS2 after relapse or progression of 3.3 months and 11.5 months, respectively. Within the limits of highly heterogeneous second-line treatments, no specific regimen seemed to provide superior disease control. However, a potential benefit was observed with brentuximab vedotin in association with chemotherapy even after excluding anaplastic large-cell lymphoma subtype or after adjusting for histology and international prognostic index in a multivariate model (HR for PFS, 0.431 [95% CI, 0.238 to 0.779]; P = .005).

Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques.

Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.