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

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. Interventions included constructing an updated care algorithm, clinician, and nurse education, integrating an updated opt-out order set, and streamlined discharge instructions. Our primary outcome was the proportion of patient encounters in which clinicians ordered algorithm-adherent care. In addition, our quality improvement team manually reviewed all failures to determine the reasons for failure and inform further interventions. Results: We evaluated 2,248 visits between January 2018 and October 2021. Algorithm-adherent care for 29- to 60-day-old infants improved from 67% to 92%. Algorithm-adherent care for 0- to 28-day infants improved from 90% to 96%. We sustained these improvements for 22 months. Failure to adhere to the algorithm in the 29- to 60-day-old infant group was primarily due to clinicians not ordering procalcitonin. Conclusions: Using quality improvement methods, we successfully increased algorithm-adherent evaluation of febrile neonates 0-60 days old in our pediatric emergency departments. Education and opt-out order sets were keys to implementing our new algorithm.

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. We assessed demographics, barriers to successful research, and beliefs about research using 4-point ordinal scales. The primary outcome was the fellow-reported predicted percentage of time devoted to clinical research 5 years after graduation. We measured the association between barriers and beliefs and the predicted future clinical research time using the Spearman correlation coefficient. RESULTS: Of 458 current fellows, 231 (50.4%) submitted complete responses to the survey. The median predicted future clinical research time was 10% (interquartile range, 5%-20%). We identified no association between sex, residency type, and previous research exposure and predicted future research time. The barrier that most correlated with decreased predicted clinical research time was difficulty designing a feasible fellowship research project (Spearman coefficient [ρ], 0.20; P = 0.002). The belief that most correlated with increased predicted clinical research time was excitement about research (ρ = 0.69, P < 0.001). CONCLUSIONS: Most fellows expect to devote a minority of their career to clinical research. Excitement about research was strongly correlated with career research interest.

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. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial-patient matches and a reference standard of historical trial-patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. RESULTS: Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial-patient matches. DISCUSSION AND CONCLUSION: By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.