S THE SPECIALTY OF HOSPItal medicine expands, the transfer of responsibility for p a t i e n t c a r e b e t w e e n hospital-based physicians (hospitalists) and primary care physicians becomes increasingly common, creating an urgent need to improve communication and information transfer between inpatient and outp a t i e n t p h y s i c i a n s a t h o s p i t a l discharge. [1][2][3] Timely transfer of accurate, relevant data about diagnostic findings, treatment, complications, consultations, tests pending at discharge, and arrangements for postdischarge follow-up may improve the continuity of this handoff. 4,5 By contrast, delayed communication or inaccuracies in information transfer among health care professionals, particularly during the early postdischarge period, may have substantial implications for continuity of care, patient safety, patient and clinician satisfaction, and resource use. [6][7][8][9][10] The discharge summary is the most common method for documenting a patient's diagnostic findings, hospital management, and arrangements for postdischarge follow-up. The Joint Context Delayed or inaccurate communication between hospital-based and primary care physicians at hospital discharge may negatively affect continuity of care and contribute to adverse events. Objectives To characterize the prevalence of deficits in communication and information transfer at hospital discharge and to identify interventions to improve this process. Data Sources MEDLINE (through November 2006), Cochrane Database of Systematic Reviews, and hand search of article bibliographies.Study Selection Observational studies investigating communication and information transfer at hospital discharge (n=55) and controlled studies evaluating the efficacy of interventions to improve information transfer (n=18). Data ExtractionData from observational studies were extracted on the availability, timeliness, content, and format of discharge communications, as well as primary care physician satisfaction. Results of interventions were summarized by their effect on timeliness, accuracy, completeness, and overall quality of the information transfer.Data Synthesis Direct communication between hospital physicians and primary care physicians occurred infrequently (3%-20%). The availability of a discharge summary at the first postdischarge visit was low (12%-34%) and remained poor at 4 weeks (51%-77%), affecting the quality of care in approximately 25% of follow-up visits and contributing to primary care physician dissatisfaction. Discharge summaries often lacked important information such as diagnostic test results (missing from 33%-63%), treatment or hospital course (7%-22%), discharge medications (2%-40%), test results pending at discharge (65%), patient or family counseling (90%-92%), and follow-up plans (2%-43%). Several interventions, including computer-generated discharge summaries and using patients as couriers, shortened the delivery time of discharge communications. Use of standardized formats to highlight the most pertinent information im...

Context Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-standardize readmission rates for purposes of hospital comparison. Objective To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. Data Sources MEDLINE, CINAHL, and Cochrane Library through March 2011, EMBASE through August 2011, and hand search of reference lists. Study Selection Dual review to identify English language studies of prediction models tested with medical patients, with both derivation and validation cohorts. Data Extraction Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Results Of 7,843 citations reviewed, 30 studies of 26 unique models met criteria. The most common outcome used was 30-day readmission; only one model specifically addressed preventable readmissions. Fourteen models relying on retrospective administrative data could be potentially used for standardization of readmission risk and hospital comparisons; of these, nine were tested in large US populations and had poor discriminative ability (c-statistics 0.55 – 0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c-statistics 0.56 – 0.72), and five could be used at hospital discharge (c-statistics 0.68 – 0.83). Six studies compared different models in the same population and two of these found that functional and social variables improved model discrimination. Though most models incorporated medical comorbidity and prior utilization variables, few examined variables associated with overall health and function, illness severity, or social determinants of health. Conclusions Most current readmission risk prediction models, whether designed for comparative or clinical purposes, perform poorly. Though in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.

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The period following discharge from the hospital is a vulnerable time for patients.About half of adults experience a medical error after hospital discharge, and 19%-23% suffer an adverse event, most commonly an adverse drug event. This article reviews several important challenges to providing high-quality care as patients leave the hospital. These include the discontinuity between hospitalists and primary care physicians, changes to the medication regimen, new self-care responsibilities that may stress available resources, and complex discharge instructions. We also discuss approaches to promoting more effective transitions of care,

Background: Approximately 20% to 50% of patients are not adherent to medical therapy. This review was performed to summarize, categorize, and estimate the effect size (ES) of interventions to improve medication adherence in chronic medical conditions.Methods: Randomized controlled trials published from January 1967 to September 2004 were eligible if they described 1 or more unconfounded interventions intended to enhance adherence with self-administered medications in the treatment of chronic medical conditions. Trials that reported at least 1 measure of medication adherence and 1 clinical outcome, with at least 80% follow-up during 6 months, were included. Study characteristics and results for adherence and clinical outcomes were extracted. In addition, ES was calculated for each outcome.Results: Among 37 eligible trials (including 12 informational, 10 behavioral, and 15 combined informational, be-havioral, and/or social investigations), 20 studies reported a significant improvement in at least 1 adherence measure. Adherence increased most consistently with behavioral interventionsthatreduceddosingdemands(3of3studies,large ES [0.89-1.20]) and those involving monitoring and feedback (3 of 4 studies, small to large ES [0.27-0.81]). Adherence also improved in 6 multisession informational trials (small to large ES [0.35-1.13]) and 8 combined interventions (small to large ES [absolute value, 0.43-1.20]). Eleven studies(4informational,3behavioral,and4combined)demonstrated improvement in at least 1 clinical outcome, but effects were variable (very small to large ES [0.17-3.41]) and not consistently related to changes in adherence. Conclusion:Several types of interventions are effective in improving medication adherence in chronic medical conditions, but few significantly affected clinical outcomes.