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.

Work-related injuries in Qatar for 1 year: an initial report from the work-related injury unified registry for Qatar.

BACKGROUND: The Ministry of Public Health National Health Strategy 2018-2022 has recognized the need for accurate, updated, and representative data that truly reflects the occupational health and safety status in Qatar. In 2015, the Hamad Trauma Center received a research grant to create a unified registry for work-related injuries in Qatar [WURQ], whose processes and research findings have been reported earlier. This paper shall describe the findings from the initial 1-year collection of data on work-related injuries [WRIs] and deaths in Qatar for the year 2020 through the WURQ database. METHODS: The WURQ database was queried for all WRIs from January 1 to December 31, 2020. These data were classified by date of injury, age, sex, nationality, mechanism of injury, severity of injury, location of medical consultation, and clinical outcome. RESULTS: Out of a total worker population of 2,174,828 [2.29 occupational fatalities per 100,000 workers, there were 50 deaths caused by WRIs]. The majority of WRI deaths were in the prehospital setting [60%] with the majority of fatal injuries occurring at the worksite [64%] and 22% due to falls. Five hundred six workers sustained severe WRIs [23.26 severe occupational injuries per 100,000 workers], and 37,601 workers sustained mild to moderate WRIs [1,728.91 mild to moderate occupational injuries per 100,000 workers]. The severe WRIs were most commonly due to falls [226 out of 506] from height [45%] and falling heavy objects [80 out of 506] [16%]. Road traffic injuries [RTI] make up one-fourth [133 out of 506] of all severe WRIs. CONCLUSION: WURQ has described WRIs in Qatar using a purpose-built and nationally linked occupational injury registry. Occupational injury and injury fatality statistics, for Qatar in 2020, are lower than or comparable with those from other high-income countries. This data can be used to inform worksite inspections, investigations, worker safety education, environmental improvements, and injury prevention programs to make Qatar safer for all its workers.