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.

New financial penalties for institutions with high readmission rates have intensified efforts to reduce rehospitalization. Several interventions that involve multiple components (e.g., patient needs assessment, medication reconciliation, patient education, arranging timely outpatient appointments, and providing telephone follow-up), have successfully reduced readmission rates for patients discharged to home. The effect of interventions on readmission rates is related to the number of components implemented, whereas single-component interventions are unlikely to reduce readmissions significantly. For patients discharged to post-acute care facilities, multicomponent interventions have reduced readmissions through enhanced communication, medication safety, advanced care planning, and enhanced training to manage common medical conditions that commonly precipitate readmission. To help hospitals direct resources and services to patients with greater likelihood of readmission, a number of risk stratification methods are available. Future work should better define the role of home-based services, information technology, mental health care, caregiver support, community partnerships, and new transitional care personnel.

Background Clinically important medication errors are common after hospital discharge. They include preventable or ameliorable adverse drug events as well as medication discrepancies or non-adherence with high potential for future harm (potential adverse drug events). Objective The Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study sought to determine the effect of a tailored intervention on the occurrence of clinically important medication errors after hospital discharge. Design Randomized controlled trial with concealed allocation and blinded outcome assessors. Setting Two tertiary care academic hospitals. Patients Adults hospitalized with acute coronary syndromes or acute decompensated heart failure. Intervention Pharmacist-assisted medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up after discharge. Measurements The primary outcome was the number of clinically important medication errors per patient during the first 30 days after hospital discharge. Secondary outcomes included preventable or ameliorable adverse drug events, as well as potential adverse drug events. Results Among 851 participants, 432 (50.8%) experienced 1 or more clinically important medication errors; 23% of such errors were judged to be serious, and 2% life-threatening. Adverse drug events occurred in 258 patients (30.3%) and potential adverse drug events in 253 (29.7%). The intervention did not significantly alter the per-patient number of clinically important medication errors (IRR=0.92; 95% CI, 0.77 to 1.10) or adverse drug events (IRR=1.09; CI, 0.86 to 1.39). Intervention patients tended to have fewer potential adverse drug events (IRR=0.80; CI, 0.61 to 1.04). Limitations The characteristics of the study hospitals and participants may limit generalizability. Conclusions Clinically important medication errors were present among half of patients after hospital discharge and were not significantly reduced by a health-literacy sensitive, pharmacist-delivered intervention.

Background:The optimal structure and intensity of interventions to reduce hospital readmission remains uncertain, due in part to lack of head-to-head comparison. To address this gap, we evaluated two forms of an evidence-based, multi-component transitional care intervention.Methods: A quasi-experimental evaluation design compared outcomes of Transition Care Coordinator (TCC) Care to Usual Care, while controlling for sociodemographic characteristics, comorbidities, readmission risk, and administrative factors. The study was conducted between January 1, 2013 and April 30, 2015 as a quality improvement initiative. Eligible adults (N=7038) hospitalized with pneumonia, congestive heart failure, or chronic obstructive pulmonary disease were identified for program evaluation via an electronic health record algorithm. Nurse TCCs provided either a full intervention (delivered in-hospital and by post-discharge phone call) or a partial intervention (phone call only).

A housestaff-led intervention utilizing education and data feedback with goal setting and peer comparison resulted in safe, significant reductions in daily laboratory testing rates.