title: Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials.
abstract: OBJECTIVE
  Reducing early (<30 days) hospital readmissions is a policy priority aimed at improving health care quality. The cumulative complexity model conceptualizes patient context. It predicts that highly supportive discharge interventions will enhance patient capacity to enact burdensome self-care and avoid readmissions.
  
  
  OBJECTIVE
  To synthesize the evidence of the efficacy of interventions to reduce early hospital readmissions and identify intervention features--including their impact on treatment burden and on patients' capacity to enact postdischarge self-care--that might explain their varying effects.
  
  
  METHODS
  We searched PubMed, Ovid MEDLINE, Ovid EMBASE, EBSCO CINAHL, and Scopus (1990 until April 1, 2013), contacted experts, and reviewed bibliographies.
  
  
  METHODS
  Randomized trials that assessed the effect of interventions on all-cause or unplanned readmissions within 30 days of discharge in adult patients hospitalized for a medical or surgical cause for more than 24 hours and discharged to home.
  
  
  METHODS
  Reviewer pairs extracted trial characteristics and used an activity-based coding strategy to characterize the interventions; fidelity was confirmed with authors. Blinded to trial outcomes, reviewers noted the extent to which interventions placed additional work on patients after discharge or supported their capacity for self-care in accordance with the cumulative complexity model.
  
  
  METHODS
  Relative risk of all-cause or unplanned readmission with or without out-of-hospital deaths at 30 days postdischarge.
  
  
  RESULTS
  In 42 trials, the tested interventions prevented early readmissions (pooled random-effects relative risk, 0.82 [95% CI, 0.73-0.91]; P < .001; I² = 31%), a finding that was consistent across patient subgroups. Trials published before 2002 reported interventions that were 1.6 times more effective than those tested later (interaction P = .01). In exploratory subgroup analyses, interventions with many components (interaction P = .001), involving more individuals in care delivery (interaction P = .05), and supporting patient capacity for self-care (interaction P = .04) were 1.4, 1.3, and 1.3 times more effective than other interventions, respectively. A post hoc regression model showed incremental value in providing comprehensive, postdischarge support to patients and caregivers.
  
  
  CONCLUSIONS
  Tested interventions are effective at reducing readmissions, but more effective interventions are complex and support patient capacity for self-care. Interventions tested more recently are less effective.
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota.
authors.forename: Aaron L
authors.initials: AL
authors.lastname: Leppin
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota2Mayo Graduate School, Mayo Clinic, Rochester, Minnesota.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Gionfriddo
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota3Department of Medicine, Mayo Clinic, Rochester, Minnesota.
authors.forename: Maya
authors.initials: M
authors.lastname: Kessler
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota3Department of Medicine, Mayo Clinic, Rochester, Minnesota4Mayo Clinic Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota.
authors.forename: Juan Pablo
authors.initials: JP
authors.lastname: Brito
authors.affiliation: General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom.
authors.forename: Frances S
authors.initials: FS
authors.lastname: Mair
authors.affiliation: General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom.
authors.forename: Katie
authors.initials: K
authors.lastname: Gallacher
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota4Mayo Clinic Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Mayo Clinic Libraries, Mayo Clinic, Rochester, Minnesota.
authors.forename: Patricia J
authors.initials: PJ
authors.lastname: Erwin
authors.affiliation: medical student at St Louis University School of Medicine, St Louis, Missouri.
authors.forename: Tanya
authors.initials: T
authors.lastname: Sylvester
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota8graduate student at University of Minnesota School of Public Health, Minneapolis.
authors.forename: Kasey
authors.initials: K
authors.lastname: Boehmer
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota.
authors.forename: Henry H
authors.initials: HH
authors.lastname: Ting
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota4Mayo Clinic Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota.
authors.forename: M Hassan
authors.initials: MH
authors.lastname: Murad
authors.affiliation: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis.
authors.forename: Nathan D
authors.initials: ND
authors.lastname: Shippee
authors.affiliation: Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota3Department of Medicine, Mayo Clinic, Rochester, Minnesota4Mayo Clinic Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota.
authors.forename: Victor M
authors.initials: VM
authors.lastname: Montori
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D010351:Patient Discharge; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011787:Quality of Health Care; D016032:Randomized Controlled Trials as Topic