BACKGROUND: Discharge planning is a routine feature of health systems in many countries. The aim of discharge planning is to reduce hospital length of stay and unplanned readmission to hospital, and improve the co-ordination of services following discharge from hospital. OBJECTIVES: To determine the effectiveness of planning the discharge of individual patients moving from hospital. SEARCH METHODS: We updated the review using the Cochrane EPOC Group Trials Register, MEDLINE, EMBASE and the Social Science Citation Index (last searched in March 2012). SELECTION CRITERIA: Randomised controlled trials (RCTs) that compared an individualised discharge plan with routine discharge care that was not tailored to the individual patient. Participants were hospital inpatients. DATA COLLECTION AND ANALYSIS: Two authors independently undertook data analysis and quality assessment using a pre designed data extraction sheet. Studies are grouped according to patient group (elderly medical patients, patients recovering from surgery and those with a mix of conditions) and by outcome. Our statistical analysis was done on an intention to treat basis, we calculated risk ratios for dichotomous outcomes and mean differences for continuous data using fixed-effect meta-analysis. When combining outcome data was not possible, because of differences in the reporting of outcomes, we have presented the data in narrative summary tables. MAIN RESULTS: We included twenty-four RCTs (8098 patients); three RCTS were identified in this update. Sixteen studies recruited older patients with a medical condition, four recruited patients with a mix of medical and surgical conditions, one recruited patients from a psychiatric hospital, one from both a psychiatric hospital and from a general hospital, and two trials patients admitted to hospital following a fall (110 patients). Hospital length of stay and readmissions to hospital were statistically significantly reduced for patients admitted to hospital with a medical diagnosis and who were allocated to discharge planning (mean difference length of stay -0.91, 95% CI -1.55 to -0.27, 10 trials; readmission rates RR 0.82, 95% CI 0.73 to 0.92, 12 trials). For elderly patients with a medical condition there was no statistically significant difference between groups for mortality (RR 0.99, 95% CI 0.78 to 1.25, five trials) or being discharged from hospital to home (RR 1.03, 95% CI 0.93 to 1.14, two trials). This was also the case for trials recruiting patients recovering from surgery and a mix of medical and surgical conditions. In three trials, patients allocated to discharge planning reported increased satisfaction. There was little evidence on overall healthcare costs. AUTHORS' CONCLUSIONS: The evidence suggests that a discharge plan tailored to the individual patient probably brings about reductions in hospital length of stay and readmission rates for older people admitted to hospital with a medical condition. The impact of discharge planning on mortality, health outcomes and cost remains uncertain.
BACKGROUND: Discharge planning is a routine feature of health systems in many countries. The aim of discharge planning is to reduce hospital length of stay and unplanned readmission to hospital, and improve the co-ordination of services following discharge from hospital. OBJECTIVES: To determine the effectiveness of planning the discharge of individual patients moving from hospital. SEARCH METHODS: We updated the review using the Cochrane EPOC Group Trials Register, MEDLINE, EMBASE and the Social Science Citation Index (last searched in March 2012). SELECTION CRITERIA: Randomised controlled trials (RCTs) that compared an individualised discharge plan with routine discharge care that was not tailored to the individual patient. Participants were hospital inpatients. DATA COLLECTION AND ANALYSIS: Two authors independently undertook data analysis and quality assessment using a pre designed data extraction sheet. Studies are grouped according to patient group (elderly medical patients, patients recovering from surgery and those with a mix of conditions) and by outcome. Our statistical analysis was done on an intention to treat basis, we calculated risk ratios for dichotomous outcomes and mean differences for continuous data using fixed-effect meta-analysis. When combining outcome data was not possible, because of differences in the reporting of outcomes, we have presented the data in narrative summary tables. MAIN RESULTS: We included twenty-four RCTs (8098 patients); three RCTS were identified in this update. Sixteen studies recruited older patients with a medical condition, four recruited patients with a mix of medical and surgical conditions, one recruited patients from a psychiatric hospital, one from both a psychiatric hospital and from a general hospital, and two trials patients admitted to hospital following a fall (110 patients). Hospital length of stay and readmissions to hospital were statistically significantly reduced for patients admitted to hospital with a medical diagnosis and who were allocated to discharge planning (mean difference length of stay -0.91, 95% CI -1.55 to -0.27, 10 trials; readmission rates RR 0.82, 95% CI 0.73 to 0.92, 12 trials). For elderly patients with a medical condition there was no statistically significant difference between groups for mortality (RR 0.99, 95% CI 0.78 to 1.25, five trials) or being discharged from hospital to home (RR 1.03, 95% CI 0.93 to 1.14, two trials). This was also the case for trials recruiting patients recovering from surgery and a mix of medical and surgical conditions. In three trials, patients allocated to discharge planning reported increased satisfaction. There was little evidence on overall healthcare costs. AUTHORS' CONCLUSIONS: The evidence suggests that a discharge plan tailored to the individual patient probably brings about reductions in hospital length of stay and readmission rates for older people admitted to hospital with a medical condition. The impact of discharge planning on mortality, health outcomes and cost remains uncertain.
Authors: Suzanne E Mitchell; Jessica Martin; Sally Holmes; Carol van Deusen Lukas; Ramon Cancino; Michael Paasche-Orlow; Cindy Brach; Brian Jack Journal: J Healthc Qual Date: 2016 Mar-Apr Impact factor: 1.095
Authors: Samuel D Towne; Kayla Fair; Matthew Lee Smith; Diane M Dowdy; SangNam Ahn; Obioma Nwaiwu; Marcia G Ory Journal: Health Serv Res Date: 2017-08-31 Impact factor: 3.402
Authors: W Li; L Stocchi; D Cherla; G Liu; A Agostinelli; C P Delaney; S R Steele; E Gorgun Journal: Tech Coloproctol Date: 2017-08-17 Impact factor: 3.781