Annals of emergency medicine
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Observational Study
Clinician and Caregiver Determinations of Acuity for Children Transported by Emergency Medical Services: A Prospective Observational Study.
Many Emergency Medical Services (EMS) agencies have developed alternative disposition processes for patients with nonemergency problems, but there is a lack of evidence demonstrating EMS clinicians can accurately determine acuity in pediatric patients. Our study objective was to determine EMS and other stakeholders' ability to identify low acuity pediatric EMS patients. ⋯ All 4 groups studied had a limited ability to identify which children transported by EMS would have no emergency resource needs, and support for alternative disposition was limited. For children to be included in alternative disposition processes, novel triage tools, training, and oversight will be required to prevent undertriage.
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To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department (ED) patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision. ⋯ This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.
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Observational Study
Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention.
The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program. ⋯ Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.