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Background: Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers., Objectives: This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data., Methods: We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation., Results: The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data., Conclusion: A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Many medical specialties have found publication misrepresentation in residency and fellowship applications, but pediatric anesthesia fellowship application data is lacking. Aims: We sought to determine the prevalence of publication misrepresentation among pediatric anesthesia fellowship applications. Methods: In this retrospective cohort study, fellowship applications to Stanford University's pediatric anesthesiology fellowship program from 2009 to 2019 were reviewed. Only peer‐reviewed journal articles listed as accepted or published were included. Nine additional variables were collected: applicant age, gender, citizenship status, American vs. international medical school, public vs. private medical school, allopathic doctor versus osteopathic doctor, number of years between college and medical school, additional degrees, and application year. The primary outcome was the rate of publication misrepresentation, defined as peer‐reviewed journal citations listed on the application that could not be verified or on which the applicant was not listed as an author. Secondary outcomes were the associations between publication misrepresentation and the additional collected variables. Results: 1280 peer‐reviewed journal publications from 877 applicants were reviewed. 3.4% of all citations listed as peer‐reviewed journal articles were misrepresented and 9.0% of all applicants with at least 1 publication had ≥1 misrepresented publications. 30.2% of publications labelled "misrepresented" were located in our search of databases but did not have the applicant as an author, and 69.8% could not be located using the search databases. Only one of the 9 collected variables (public vs private medical school) was significantly associated with publication misrepresentation. Conclusions: In this single‐center retrospective study, publication misrepresentation was found to occur in one out of 11 pediatric anesthesia fellowship applications with at least one publication. Since residency and fellowship applicant publications may be heavily weighted during the application process, programs may want to include additional inquiries into the accuracy of applicant publications. [ABSTRACT FROM AUTHOR]