OBJECTIVES: Although 72-hour emergency department (ED) revisits are increasingly used as a hospital metric, there is no known empirical basis for this 72-hour threshold. The objective of this study was to determine the timing of ED revisits for adult patients within 30 days of ED discharge. METHODS: This was a retrospective cohort study of all nonfederal ED discharges in Florida and Nebraska from April 1, 2010, to March 31, 2011, using data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). ED discharges were followed forward to identify ED revisits occurring at any hospital within the same state within 30 days. The cumulative hazard of an ED revisit was plotted. Parametric and nonparametric modeling was performed to characterize the rate of ED revisits. RESULTS: There were 4,782,045 ED discharges, with 7.5% (95% confidence interval [CI] = 7.4% to 7.5%) associated with 3-day revisits, and 22.4% (95% CI = 22.3% to 22.4%) associated with 30-day revisits, inclusive of the 3-day revisits. A double-exponential model fit the data best (p < 0.0001), and a single hinge point at 9 days (multivariate adaptive regression splines [MARS] model) yielded the best linear fit to the data, suggesting 9 days as the most reasonable cutoff for identification of acute ED revisits. Multiple stratified and subgroup analyses produced similar results. Future work should focus on identifying primary reasons for potentially avoidable return ED visits instead of on the revisit occurrence itself, thus more directly measuring potential lapses in delivery of high-quality care. CONCLUSIONS: Almost one-quarter of ED discharges are linked to 30-day ED revisits, and the current 72-hour ED metric misses close to 70% of these patients. Our findings support 9 days as a more inclusive cutoff for studies of ED revisits.
OBJECTIVES: Although 72-hour emergency department (ED) revisits are increasingly used as a hospital metric, there is no known empirical basis for this 72-hour threshold. The objective of this study was to determine the timing of ED revisits for adult patients within 30 days of ED discharge. METHODS: This was a retrospective cohort study of all nonfederal ED discharges in Florida and Nebraska from April 1, 2010, to March 31, 2011, using data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). ED discharges were followed forward to identify ED revisits occurring at any hospital within the same state within 30 days. The cumulative hazard of an ED revisit was plotted. Parametric and nonparametric modeling was performed to characterize the rate of ED revisits. RESULTS: There were 4,782,045 ED discharges, with 7.5% (95% confidence interval [CI] = 7.4% to 7.5%) associated with 3-day revisits, and 22.4% (95% CI = 22.3% to 22.4%) associated with 30-day revisits, inclusive of the 3-day revisits. A double-exponential model fit the data best (p < 0.0001), and a single hinge point at 9 days (multivariate adaptive regression splines [MARS] model) yielded the best linear fit to the data, suggesting 9 days as the most reasonable cutoff for identification of acute ED revisits. Multiple stratified and subgroup analyses produced similar results. Future work should focus on identifying primary reasons for potentially avoidable return ED visits instead of on the revisit occurrence itself, thus more directly measuring potential lapses in delivery of high-quality care. CONCLUSIONS: Almost one-quarter of ED discharges are linked to 30-day ED revisits, and the current 72-hour ED metric misses close to 70% of these patients. Our findings support 9 days as a more inclusive cutoff for studies of ED revisits.
Authors: Margaret E Samuels-Kalow; Karin V Rhodes; Mira Henien; Emily Hardy; Thomas Moore; Felicia Wong; Carlos A Camargo; Caroline T Rizzo; Cynthia Mollen Journal: Acad Emerg Med Date: 2017-04-24 Impact factor: 3.451
Authors: Kristin L Rising; David N Karp; Rhea E Powell; Timothy W Victor; Brendan G Carr Journal: Health Serv Res Date: 2017-01-19 Impact factor: 3.402
Authors: Arjun K Venkatesh; Hao Mei; Keith E Kocher; Michael Granovsky; Ziad Obermeyer; Erica S Spatz; Craig Rothenberg; Harlan M Krumholz; Zhenqui Lin Journal: Acad Emerg Med Date: 2017-03-17 Impact factor: 3.451
Authors: Emily C Earl-Royal; Elinore J Kaufman; Alexandra L Hanlon; Daniel N Holena; Kristin L Rising; M Kit Delgado Journal: Am J Emerg Med Date: 2017-03-21 Impact factor: 2.469
Authors: Elinore Kaufman; Kristin Rising; Douglas J Wiebe; David J Ebler; Marie L Crandall; M Kit Delgado Journal: Am J Emerg Med Date: 2016-06-15 Impact factor: 2.469