Hongsoo Kim1, William W Hung2, Myunghee Cho Paik3, Joseph S Ross4, Zhonglin Zhao5, Gi-Soo Kim3, Kenneth Boockvar6. 1. Graduate School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea. 2. Icahn School of Medicine at Mount Sinai, New York, NY, USA James J Peters VA Medical Center, Geriatrics Research, Education and Clinical Center, Bronx, NY, USA. 3. Department of Statistics, College of Natural Sciences, Seoul National University, Seoul, South Korea. 4. General Internal Medicine, Yale University School of Medicine, New Haven, CT, USA. 5. Independent Statistical Consultant, Washington, DC, USA. 6. Icahn School of Medicine at Mount Sinai, New York, NY, USA James J Peters VA Medical Center, Geriatrics Research, Education and Clinical Center, Bronx, NY, USA Jewish Home Lifecare, Research Institute on Aging, New York, NY, USA.
Abstract
OBJECTIVES: To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital. DESIGN: A population-based, secondary analysis using multilevel causal modeling. SETTING: Acute care hospitals in California in the USA. PARTICIPANTS: In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge. INTERVENTION: No intervention. MAIN OUTCOME MEASURE(S): Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions. RESULTS: Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group. CONCLUSIONS: Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.
OBJECTIVES: To examine patient, hospital and market factors and outcomes associated with readmission to a different hospital compared with the same hospital. DESIGN: A population-based, secondary analysis using multilevel causal modeling. SETTING: Acute care hospitals in California in the USA. PARTICIPANTS: In total, 509 775 patients aged 50 or older who were discharged alive from acute care hospitals (index hospitalizations), and 59 566 who had a rehospitalization within 30 days following their index discharge. INTERVENTION: No intervention. MAIN OUTCOME MEASURE(S): Thirty-day unplanned readmissions to a different hospital compared with the same hospital and also the costs and health outcomes of the readmissions. RESULTS: Twenty-one percent of patients with a rehospitalization had a different-hospital readmission. Compared with the same-hospital readmission group, the different-hospital readmission group was more likely to be younger, male and have a lower income. The index hospitals of the different-hospital readmission group were more likely to be smaller, for-profit hospitals, which were also more likely to be located in counties with higher competition. The different-hospital readmission group had higher odds for in-hospital death (8.1 vs. 6.7%; P < 0.0001) and greater readmission hospital costs ($15 671.8 vs. $14 286.4; P < 0.001) than the same-hospital readmission group. CONCLUSIONS:Patient, hospital and market characteristics predicted different-hospital readmissions compared with same-hospital readmissions. Mortality and cost outcomes were worse among patients with different-hospital readmissions. Strategies for better care coordination targeting people at risk for different-hospital readmissions are necessary.
Authors: Jay G Berry; Sara L Toomey; Alan M Zaslavsky; Ashish K Jha; Mari M Nakamura; David J Klein; Jeremy Y Feng; Shanna Shulman; Vincent W Chiang; Vincent K Chiang; William Kaplan; Matt Hall; Mark A Schuster Journal: JAMA Date: 2013-01-23 Impact factor: 56.272
Authors: Frank W K Chan; Fiona Y Y Wong; Carrie H K Yam; Wai-ling Cheung; Eliza L Y Wong; Michael C M Leung; William B Goggins; Eng-kiong Yeoh Journal: BMC Health Serv Res Date: 2011-08-10 Impact factor: 2.655
Authors: Joseph R Linzey; Jeffrey L Nadel; D Andrew Wilkinson; Venkatakrishna Rajajee; Badih J Daou; Aditya S Pandey Journal: Neurosurgery Date: 2020-01-01 Impact factor: 4.654
Authors: Rebecca Kartje; Brian E Dixon; Ashley L Schwartzkopf; Vivian Guerrero; Kimberly M Judon; Joanne C Yi; Kenneth Boockvar Journal: J Am Board Fam Med Date: 2021 Mar-Apr Impact factor: 2.657
Authors: Jacek Kryś; Błażej Łyszczarz; Zofia Wyszkowska; Kornelia Kędziora-Kornatowska Journal: Int J Environ Res Public Health Date: 2019-07-02 Impact factor: 3.390
Authors: Aditi Vasan; John W Morgan; Nandita Mitra; Chang Xu; Judith A Long; David A Asch; Shreya Kangovi Journal: Health Serv Res Date: 2020-07-08 Impact factor: 3.402
Authors: Carl Willers; Anne-Marie Boström; Lennart Carlsson; Anton Lager; Rikard Lindqvist; Elisabeth Rydwik Journal: PLoS One Date: 2021-03-22 Impact factor: 3.240
Authors: Michael Urbich; Gary Globe; Krystallia Pantiri; Marieke Heisen; Craig Bennison; Heidi S Wirtz; Gian Luca Di Tanna Journal: Pharmacoeconomics Date: 2020-11 Impact factor: 4.981