Sungkyunkwan University · 医学
Professor Won Chul's research lab specializes in emergency medicine and healthcare systems optimization, with a strong focus on improving emergency department (ED) efficiency and patient outcomes. The lab investigates ED crowding, patient safety, and clinical decision support through data-driven approaches, including machine learning and real-time dashboards. Key research directions include predicting critical events like cardiac arrest, developing multidisciplinary care models for advanced cancer patients, and implementing innovative health information systems to enhance clinical workflows. The lab emphasizes practical, user-centered solutions with real-world implementation and validation.
Figures are computed from collected data and may differ slightly.
In this study, we evaluated national differences in emergency department (ED) crowding to identify factors significantly associated with crowding in institutes and communities across Korea. This was a cross-sectional nationwide observational study using data abstracted from the National Emergency Department Information System (NEDIS). We calculated mean occupancy rates to quantify ED crowding status and divided EDs into three groups according to their occupancy rates (cutoffs: 0.5 and 1.0). Fact
The ED crowding was associated with increased hazard for hospital mortality for pediatric patients in mixed EDs.
We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability.
A multidisciplinary mobile care system for patients with advanced gastrointestinal cancer was developed with clinically oriented measures. A prospective study was performed for its evaluation, which showed favorable satisfaction.
We developed a real-time autonomous ED dashboard and successfully used it for 5 years with good evaluation from users.
After introduction of the ICP, ED LOS decreased without an increase in hospital capacity.
In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians' designation and specialty, patients' severity and chief complaints, and alert and medication type.
We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the
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