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Hospital discharge planning has been hampered by the lack of predictive models.To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs).Retrospective cohort study using split validation.Integrated health care delivery system serving 3.9 million members.A total of 360,036 surviving adults who experienced 609,393 overnight hospitalizations at 21 hospitals between June 1, 2010 and December 31, 2013.A composite outcome (nonelective rehospitalization and/or death within 7 or 30 days of discharge).Nonelective rehospitalization rates at 7 and 30 days were 5.8% and 12.4%; mortality rates were 1.3% and 3.7%; and composite outcome rates were 6.3% and 14.9%, respectively. Using data from a comprehensive EMR, we developed 4 models that can generate risk estimates for risk of the combined outcome within 7 or 30 days, either at the time of admission or at 8 AM on the day of discharge. The best was the 30-day discharge day model, which had a c-statistic of 0.756 (95% confidence interval, 0.754–0.756) and a Nagelkerke pseudo-R2 of 0.174 (0.171–0.178) in the validation dataset. The most important predictors—a composite acute physiology score and end of life care directives—accounted for 54% of the predictive ability of the 30-day model. Incorporation of diagnoses (not reliably available for real-time use) did not improve model performance.It is possible to develop robust predictive models, suitable for use in real time with commercially available EMRs, for nonelective rehospitalization and postdischarge mortality.