Background A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. Methods In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed. Results Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month. Conclusions An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.
Hospice and palliative care has been recognized as an essential part of emergency medicine; however, there is no consensus on the optimal model for the delivery of hospice and palliative care in the emergency department (ED). Therefore, we conducted a novel implementation in a tertiary medical center in Taiwan. In the preintervention period, we recruited a specialist for hospice and palliative medicine in the ED to lead our intervention. In the early stage of the intervention, starting on July 1, 2014, we encouraged and funded ED physicians and nurses to receive training for hospice and palliative medicine and residents of emergency medicine to rotate to the hospice ward. In the late stage of the intervention, we initiated educational programs in the ED, an interdisciplinary meeting with the hospice team every month, sharing information and experience via a cell phone communication app, and setting aside an emergency hospice room for end-of-life patients. We compared the outcomes among pre-, during, and postintervention periods. Compared with 4 in the preintervention period, the cases of do not resuscitate (DNR) per month increased significantly to 30.1 in the early stage of intervention, 23.9 in late stage of intervention, and 34.6 in the postintervention period (all P < .001 compared with the preintervention period). Compared with 10.8% in the preintervention period, the ratio of DNR orders signed in the ED/total DNR orders signed in the study hospital was increased to 17.1% in early stage of intervention, 12.5% in late stage of intervention, and 22.8% in postintervention. Compared with zero in preintervention and early intervention, the cases of consultation with the hospice team increased significantly to 19 cases per month in the late stage of intervention and postintervention. The ability of nurses in hospice and palliative care, including knowledge and the timing and method of consultation with the hospice team, was also significantly improved. We successfully implemented a novel model of hospice and palliative care in the ED via a champion, education, and close collaboration with the hospice team, which could be an important reference for other EDs and intensive care unit in the future.
Background Posttraumatic psychiatric disorders (PTPDs) are common in disaster workers; however, their incidence and resilience in healthcare providers (HCPs) following a disastrous earthquake are still unclear. Therefore, we conducted an interventional study to clarify this issue. Methods After a medical response to the scene of a collapsed huge building, we conducted an assessment of the HCPs using an immediate self-administered questionnaire and a follow-up questionnaire 1 month later. Psychological support after the operation was implemented. We performed analysis of the risk for PTPDs and comparison between immediate and follow-up questionnaires. Results The mean age (standard deviation) of the HCPs was 32.7 (5.2) years, with 33.5 (5.8) years for nurses and 32.4 (4.4) years for physicians. The proportion of females among the nurses and physicians was 94.3% and 12.5%, respectively. In total, 16.4% (11/67) of HCPs fit the criteria of PTPDs. Nurses had a trend of higher incidence than physicians. Female HCPs had a trend of higher incidence than male HCPs. After intervention, none of the HCPs reported PTPDs in the follow-up questionnaire (p < 0.05). Conclusion This study delineated that PTPDs were common in HCPs following medical response to an earthquake; however, the resilience was good after the early intervention.
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