Predicting emergency department visits among children with asthma in two academic medical systems.

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

The Autonomy Toolbox: A Multicenter Collaborative to Promote Resident Autonomy.

OBJECTIVES: Autonomy is necessary for resident professional development and well-being. A recent focus on patient safety has increased supervision and decreased trainee autonomy. Few validated interventions exist to improve resident autonomy. We aimed to use quality improvement methods to increase our autonomy metric, the Resident Autonomy Score (RAS), by 25% within 1 year and sustain for 6 months. METHODS: We developed a bundled-intervention approach to improve senior resident (SR) perception of autonomy on Pediatric Hospital Medicine (PHM) services at 5 academic children's hospitals. We surveyed SR and PHM faculty perceptions of autonomy and targeted interventions toward areas with the highest discordance. Interventions included SR and faculty development, expectation-setting huddles, and SR independent rounding. We developed a Resident Autonomy Score (RAS) index to track SR perceptions over time. RESULTS: Forty-six percent of SRs and 59% of PHM faculty completed the needs assessment survey querying how often SRs were afforded opportunities to provide autonomous medical care. Faculty and SR ratings were discordant in these domains: SR input in medical decisions, SR autonomous decision-making in straightforward cases, follow-through on SR plans, faculty feedback, SR as team leader, and level of attending oversight. The RAS increased by 19% (3.67 to 4.36) 1 month after SR and faculty professional development and before expectation-setting and independent rounding. This increase was sustained throughout the 18-month study period. CONCLUSIONS: SRs and faculty perceive discordant levels of SR autonomy. We created an adaptable autonomy toolbox that led to sustained improvement in perception of SR autonomy.