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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Patients undergoing diagnostic imaging studies in the emergency department (ED) commonly have incidental findings, which may represent unrecognized serious medical conditions, including cancer. Recognition of incidental findings frequently relies on manual review of textual radiology reports and can be overlooked in a busy clinical environment. Our study aimed to develop and validate a supervised machine learning model using natural language processing to automate the recognition of incidental findings in radiology reports of patients discharged from the ED. ⋯ Machine learning and natural language processing can classify incidental findings in CT reports of ED patients with high sensitivity and high negative predictive value across a broad range of ED settings. These findings suggest the utility of natural language processing in automating the review of free-text reports to identify incidental findings and may facilitate interventions to improve timely follow-up.