A Primary Care-Based Quality Improvement Project to Reduce Asthma Emergency Department Visits.

BACKGROUND AND OBJECTIVES: Asthma exacerbation is a common and often preventable cause of Emergency Department (ED) utilization. Children eligible for Medicaid are at increased risk of poor asthma control and subsequent ED visits. In 2010, we implemented a multicomponent longitudinal quality improvement project to improve pediatric asthma care for our primary care population, which was 90% Medicaid-eligible. Our goal was to reduce asthma-related ED visits by patients ages 2 to 18 years by 3% annually. METHODS: The setting was a multisite large urban high-risk primary care network affiliated with a children's hospital. We implemented 5 sequential interventions within our network of pediatric primary care centers to increase: use of asthma action plans by clinicians, primary care-based Asthma Specialty Clinic visits (extended asthma visits in the main primary care site), use of a standard asthma note at all visits, documentation of the Asthma Control Test, and step-up therapy for children with poorly controlled asthma. RESULTS: At baseline in 2010, there were 21.7 asthma-related ED visits per 1000 patients per year. By 2019, asthma-related ED visits decreased to 14.5 per 1000 patients per year, a 33% decrease, with 2 center line shifts over time. We achieved and sustained our goal metrics for 4 of 5 key interventions. CONCLUSIONS: We reduced ED utilization for asthma in a large, high-risk pediatric population. The interventions implemented and used over time in this project demonstrate that sustainable outcomes can be achieved in a large network of primary care clinics.

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.