An APP-led COPD transitional care model increased pulmonary follow-up scheduling to 59.7% (p<0.001) and order set utilization to 75%, achieving an overall readmission rate of 23.9%.
Observational (n=2,202)
Yes
Does a data-driven, APP-led transitional care model reduce 30-day all-cause readmissions in patients hospitalized with COPD exacerbations?
An APP-led transitional care model with real-time data analytics improved COPD order set utilization and post-discharge follow-up scheduling across a large health system.
Abstract Rationale Chronic Obstructive Pulmonary Disease (COPD) exacerbations remain a leading cause of hospital readmissions and healthcare utilization. National data indicate that timely and structured post-discharge care is essential to reducing preventable readmissions. Our health system, comprised of multiple hospitals throughout Northeast Ohio, serves a diverse patient population across the socioeconomic spectrum with high rates of unplanned COPD readmissions. We hypothesized that development of a process-of-care model to enhance coordination, ensure timely follow-up, and deliver consistent, evidence-based care would reduce avoidable readmissions. Methods In 2025, a multidisciplinary team launched a COPD quality improvement initiative across nine Cleveland Clinic hospitals in a large integrated health network. This initiative identified a model of care enlisting an APP at each location to “champion” COPD transitional care by tracking hospitalized patients, facilitating discharge planning, and coordinating outpatient visits. To connect the initiative across sites, a daily huddle of the champion APPs and stakeholders was created to report out on real-time electronic health record (EHR) data of key metrics. A centralized data dashboard further enabled continuous tracking of performance metrics, identification of high-risk patients, and feedback to site-level teams. Primary objective was to reduce 30-day all-cause readmission count by 10 percent. Secondary measures included increased utilization of COPD order set and follow-up appointment scheduling and completion. Data was compared using t-tests for categorical variables, chi square for continuous variables, and z tests for proportions, with a significant two-tailed p value of 0.05 for all parameters. Results Following implementation of the intervention (January 1 - November 3, 2025), there were 2,202 discharges, with 526 readmissions (23.9% readmission rate). The proportion of patients discharged with pulmonary follow-up increased significantly to 59.7% (p 0.001). The completion rate of scheduled pulmonary follow-ups declined slightly to 41.1% (p = 0.14), while the readmission rate among those who had pulmonary follow-ups decreased to 20.3% (p = 0.082). In addition, following implementation of the huddle, COPD order set utilization increased from 25% to 75% in a two-month time frame. Conclusions A data-informed, APP-led COPD transitional care model significantly improved utilization of a COPD exacerbation order set, facilitated timely post-discharge follow-up scheduling, and reduced readmissions within a large health system. Our findings highlight that integration of real-time data analytics and a specialist APP champion into care transitions represents a scalable, sustainable strategy for improving COPD outcomes and optimizing system-level efficiency. This abstract is funded by: None
Wisen et al. (2026) conducted an observational in Chronic Obstructive Pulmonary Disease (COPD) exacerbations (n=2,202). Data-driven, APP-led COPD transitional care model vs. Pre-implementation baseline was evaluated on 30-day all-cause readmission count. An APP-led COPD transitional care model increased pulmonary follow-up scheduling to 59.7% (p<0.001) and order set utilization to 75%, achieving an overall readmission rate of 23.9%.