BACKGROUND: Rehospitalization is a prominent target for healthcare quality improvement and performance-based reimbursement. The generalizability of existing evidence on best practices is unknown.

BACKGROUNDRestrictions in the hours residents can be on duty have resulted in increased sign‐outs, that is, transfer of patient care information and responsibility from one physician to a cross‐coverage physician, leading to discontinuity in patient care. This sign‐out process, which occurs primarily in the inpatient setting, traditionally has been informal, unstructured, and idiosyncratic. Although studies show that discontinuity may be harmful to patients, this is little data to assist residency programs in redesigning systems to improve sign‐out and manage the discontinuity.PURPOSEThis article reviews the relevant medical literature, current practices in non–health professions in managing discontinuity, and summarizes the existing practice and experiences at 3 academic internal medicine hospitalist‐based programs.CONCLUSIONSWe provide recommendations and strategies for best practices to design safe and effective sign‐out systems for residents that may also be useful to hospitalists working in academic and community settings. Journal of Hospital Medicine 2006;1:257–266. © 2006 Society of Hospital Medicine.

BACKGROUND:Readmissions are costly both financially for our healthcare system and emotionally for our patients. Identifying factors that increase risk for readmissions may be helpful to focus resources to optimize the discharge process and reduce avoidable readmissions.OBJECTIVE:To identify factors associated with readmission within 30 days for general medicine patients.METHODS:We performed a retrospective observational study of an administrative database at an urban 550‐bed tertiary care academic medical center. Cohort patients were discharged from the general medicine service over a 2‐year period from June 1, 2006, to May 31, 2008. Clinical, operational, and sociodemographic factors were evaluated for association with readmission.RESULTS:Our cohort included 10,359 consecutive admissions (6805 patients) discharged from the general medicine service. The 30‐day readmission rate was 17.0%. In multivariate analysis, factors associated with readmission included black race (odds ratio [OR], 1.43; 95% confidence interval [CI], 1.24–1.65), inpatient use of narcotics (1.33; 1.16–1.53) and corticosteroids (1.24; 1.09–1.42), and the disease states of cancer (with metastasis 1.61; 1.33–1.95; without metastasis 1.95; 1.54–2.47), renal failure (1.19; 1.05–1.36), congestive heart failure (1.30; 1.09–1.56), and weight loss (1.26; 1.09–1.47). Medicaid payer status (1.15; 0.97–1.36) had a trend toward readmission.CONCLUSION:Readmission of general medicine patients within 30 days is common and associated with several easily identifiable clinical and nonclinical factors. Identification of these risk factors can allow providers to target interventions to reduce potentially avoidable readmissions. Journal of Hospital Medicine 2010. © 2010 Society of Hospital Medicine

BACKGROUND: Readmissions cause significant distress to patients and considerable financial costs. Identifying hospitalized patients at high risk for readmission is an important strategy in reducing readmissions. We aimed to evaluate how well physicians, case managers, and nurses can predict whether their older patients will be readmitted and to compare their predictions to a standardized risk tool (Probability of Repeat Admission, or P ra ). METHODS: Patients aged ≥65 discharged from the general medical service at University of California, San Francisco Medical Center, a 550-bed tertiary care academic medical center, were eligible for enrollment over a 5-week period. At the time of discharge, the inpatient team members caring for each patient estimated the chance of unscheduled readmission within 30 days and predicted the reason for potential readmission. We also calculated the P ra for each patient. We identified readmissions through electronic medical record (EMR) review and phone calls with patients/caregivers. Discrimination was determined by creating ROC curves for each provider group and the P ra . RESULTS: One hundred sixty-four patients were eligible for enrollment. Of these patients, five died during the 30-day period post-discharge. Of the remaining 159 patients, 52 patients (32.7%) were readmitted. Mean readmission predictions for the physician providers were closest to the actual readmission rate, while case managers, nurses, and the P ra all overestimated readmissions. The ability to discriminate between readmissions and non-readmissions was poor for all provider groups and the P ra (AUC from 0.50 for case managers to 0.59 for interns, 0.56 for P ra ). None of the provider groups predicted the reason for readmission with accuracy. CONCLUSIONS: This study found (1) overall readmission rates were higher than previously reported, possibly because we employed a more thorough follow-up methodology, and (2) neither providers nor a published algorithm were able to accurately predict which patients were at highest risk of readmission. Amid increasing pressure to reduce readmission rates, hospitals do not have accurate predictive tools to guide their efforts.

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