Improving the Evidence-based Care of Febrile Neonates: A Quality Improvement Initiative.

Unlabelled: Our emergency department updated our care algorithm to provide evidence-based, standardized care to 0- to 60-day-old febrile neonates. Specifically, we wanted to increase the proportion of visits for which algorithm-adherent care was provided from 90% to 95% for infants 0-28 days, and from 67% to 95% for infants 29-60 days, by June 30, 2020.

Methods: Our emergency medicine team outlined our theory for improvement and used multiple plan-do-study-act cycles to test interventions aimed at key drivers.

Research Interest in Pediatric Emergency Medicine Fellows.

Objective: Factors predictive of research career interest among pediatric emergency medicine (PEM) fellows are not known. We sought to determine the prevalence and determinants of interest in research careers among PEM fellows.

Methods: We performed an electronically distributed national survey of current PEM fellows.

Essentials of Pediatric Emergency Medicine Fellowship: Part 3: Clinical Education and Experience.

This article is the third in a 7-part series that aims to comprehensively describe the current state and future directions of pediatric emergency medicine fellowship training from the essential requirements to considerations for successfully administering and managing a program to the careers that may be anticipated upon program completion. This article focuses on the clinical aspects of fellowship training including the impact of the clinical environment, modalities for teaching and evaluation, and threats and opportunities in clinical education.

Essentials of Pediatric Emergency Medicine Fellowship: Part 1: An Overview.

This article is the first in a 7-part series (Table 1) that aims to comprehensively describe the current state and future directions of pediatric emergency medicine fellowship training from the essential requirements to considerations for successfully administering and managing a program to the careers that may be anticipated on program completion. This overview article provides a framework for the series.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.

Objectives: (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials.

Data And Methods: We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period.