Annals of emergency medicine
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Inappropriate antibiotic prescribing for acute respiratory tract infections is a common source of low-value care in the emergency department (ED). Racial and socioeconomic disparities have been noted in episodes of low-value care, particularly in children. We evaluated whether prescribing rates for acute respiratory tract infections when antibiotics would be inappropriate by guidelines differed by race and socioeconomics. ⋯ Our results suggest that although overall inappropriate prescribing was relatively low, White patients and patients from wealthier areas were more likely to receive an inappropriate antibiotic prescription.
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This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously validated risk stratification scores using variables extracted from the electronic health record at the time of diagnosis. The combination of these approaches yielded an natural language processing-based clinical decision support tool that can identify patients presenting to the emergency department (ED) with low-risk PE as candidates for outpatient management. ⋯ This study demonstrates that a natural language processing-based model utilizing real-time radiology reports can accurately identify patients with PE. Further, this model, used in combination with a validated risk stratification score (sPESI), provides a clinical decision support tool that accurately identifies patients in the ED with low-risk PE as candidates for outpatient management.
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The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contribute to effective and responsible implementation of such tools. This study sought to evaluate 3 uses for generative artificial intelligence for clinical documentation in pediatric emergency medicine, measuring time savings, effort reduction, and physician attitudes and identifying potential risks and barriers. ⋯ Pediatric emergency medicine attendings in our study perceived that ChatGPT can deliver high-quality summaries while saving time and effort in many scenarios, but not all.
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Unnecessary diagnostic tests and treatments in children cared for in emergency departments (EDs) do not benefit patients, increase costs, and may result in harm. To address this low-value care, a taskforce of pediatric emergency medicine (PEM) physicians was formed to create the first PEM Choosing Wisely recommendations. Using a systematic, iterative process, the taskforce collected suggested items from an interprofessional group of 33 ED clinicians from 6 academic pediatric EDs. ⋯ All recommendations focused on decreasing diagnostic testing related to respiratory conditions, medical clearance for psychiatric conditions, seizures, constipation, and viral respiratory tract infections. A multinational PEM taskforce developed the first Choosing Wisely recommendation list for pediatric patients in the ED setting. Future activities will include dissemination efforts and interventions to improve the quality and value of care specific to recommendations.
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Observational Study
Concordance Between Electronic Health Record-Recorded Race and Ethnicity and Patient Report in Emergency Department Patients.
We assessed the concordance of patient-reported race and ethnicity for emergency department (ED) patients compared with what was recorded in the electronic health record. ⋯ Documentation discordance regarding race and ethnicity exists between electronic health records and self-reported data for our ED patients, particularly for ethnically Hispanic and Latino/a patients. Future efforts should focus on ensuring that demographic information in the electronic health record is accurately collected.