Implementation of a Novel Algorithm to Decrease Unnecessary Hospitalizations in Patients Presenting to a Community Emergency Department With Atrial Fibrillation

OBJECTIVES: Atrial fibrillation (AFib) is the most common dysrhythmia in the United States. Patients seen in the emergency department (ED) in rapid AFib are often started on intravenous rate-controlling agents and admitted for several days. Although underlying and triggering illnesses must be addressed, AFib, intrinsically, is rarely life-threatening and can often be safely managed in an outpatient setting. At our academic community hospital, we implemented an algorithm to decrease hospital admissions for individuals presenting with a primary diagnosis of AFib. We focused on lenient oral rate control and discharge home. Our study evaluates outcomes after implementation of this algorithm. METHODS: Study design is a retrospective cohort analysis pre- and postimplementation of the algorithm. The primary outcome was hospital admissions. Secondary outcomes were 3- and 30-day ED visits and any associated hospital admissions. These outcomes were compared before (March 2013-February 2014) and after (March 2015-February 2016) implementation. Chi-square tests and logistic regressions were run to test for significant changes in the three outcome variables. RESULTS: A total of 1,108 individuals met inclusion criteria with 586 patients in the preimplementation group and 522 in the postimplementation group. Cohorts were broadly comparable in terms of demographics and health histories. Admissions for persons presenting with AFib after implementation decreased significantly (80.4% pre vs. 67.4% post, adjusted odds ratio [OR] = 3.4, p < 0.001). Despite this difference there was no change in ED return rates within 3 or 30 days (adjusted ORs = 0.93 and 0.89, p = 0.91 and 0.73, respectively). CONCLUSIONS: Implementation of a novel algorithm to identify and treat low-risk patients with AFib can significantly decrease the rate of hospital admissions without increased ED returns. This simple algorithm could be adopted by other community hospitals and help lower costs.