During spring 2020, SARS-CoV-2 pandemic induced shortage of medical equipment, hospital capacity and staff. To tackle this issue, medical students have been strongly involved in early patient triage through medical phone call regulation. Here, we present an intelligent web-based decision support system for COVID-19 phone call regulation, developed by and for, medical students to help them during this difficult but crucial task. The system is divided into 5 tabs. The first tab displays administrative information, clinical data related to life-threatening emergency, and personalized recommendations for patient management. The second tab displays a PDF report summarizing the clinical situation; the third tab displays the guidelines used for establishing the recommendations, and the fourth tab displays the overall algorithm in the form of a decision tree. The fifth tab provided a short user guide. The system was assessed by 21 medical staff. More than 90% of them appreciated the navigation and the interface, and found the content relevant. 90,5% of them would like to use it during the medical regulation. In the future, we plan to use this system during simulation-based medical learning for the initial medical training of medical students.
In critical situations such as pandemic, medical students are often called to help in emergency call centers. However, they may encounter difficulties in phone triage because of a lack of medical skills. Here, we aim at developing a Clinical Decision Support System for helping medical students in phone call triage of pediatric patients. The system is based on the PAT (Pediatric Assessment Triangle) and local guidelines. It is composed of two interfaces. The first allows a quick assessment of severity signs, and the second provides recommendations and additional elements such as “elements to keep in mind” or “medical advice to give to patient”. The system was evaluated by 20 medical students, with two fictive clinical cases. 75% of them found the content useful and clear, and the navigation easy. 65% would feel more reassured to have this system in emergency call centers. Further works are planned to improve the system before implementation in real-life.
Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.
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