The PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.

BACKGROUND Recent efforts to prevent readmissions are increasingly focusing on early identification of high‐risk patients. OBJECTIVE To test whether information on functioning during hospitalization contributes to the ability to accurately identify older adults at high risk of readmission beyond their baseline risk. DESIGN Prospective cohort study. SETTING Internal medicine wards at 2 medical centers. PATIENTS Five hundred fifty‐nine community‐dwelling older adults (aged ≥70 years) discharged to their homes. MEASUREMENTS Data on unplanned 30‐day readmissions were retrieved from electronic health records. Data on at‐admission activities of daily living (ADL) and in‐hospital ADL decline were collected using validated questionnaires. Multivariate logistic regression was used to model the association between functioning and readmission controlling for known risk factors. RESULTS Higher in‐hospital ADL decline was significantly associated with readmission (odds ratio for each 10‐point decrease in ADL = 1.32, 95% confidence interval = 1.02‐1.72) but did not contribute to the overall discrimination of the model, as compared with the at‐admission data (C statistic = 0.81 for each model). Identifying high‐risk (10th highest percentile) patients by the at‐admission model did not detect 7/55 (12.7%) of patients who would have been categorized as high risk if risk identification was postponed to the discharge date and included data on in‐hospital ADL decline. CONCLUSIONS The study highlights the ability to identify patients at high risk for readmission already early in the index hospitalization using data on functioning, nutrition, chronic morbidity, and prior hospitalizations. Nonetheless, at‐discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization. Journal of Hospital Medicine 2016;11:636–641. © 2016 Society of Hospital Medicine

Objective: The objective of this study was to evaluate the incremental predictive power of electronic medical record (EMR) data, relative to the information available in more easily accessible and standardized insurance claims data. Data and Methods: Using both EMR and Claims data, we predicted outcomes for 118,510 patients with 144,966 hospitalizations in 8 hospitals, using widely used prediction models. We use cross-validation to prevent overfitting and tested predictive performance on separate data that were not used for model training. Main Outcomes: We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. Results: We achieve nearly the same prediction accuracy using both EMR and claims data relative to using claims data alone in predicting 30-day readmissions [area under the receiver operating characteristic curve (AUC): 0.698 vs. 0.711; positive predictive value (PPV) at top 10% of predicted risk: 37.2% vs. 35.7%], and 1-year mortality (AUC: 0.902 vs. 0.912; PPV: 64.6% vs. 57.6%). EMR data, especially from the first 2 days of the index admission, substantially improved prediction of length of stay (AUC: 0.786 vs. 0.837; PPV: 58.9% vs. 55.5%) and inpatient mortality (AUC: 0.897 vs. 0.950; PPV: 24.3% vs. 14.0%). Results were similar for sensitivity, specificity, and negative predictive value across alternative cutoffs and for using alternative types of predictive models. Conclusion: EMR data are useful in predicting short-term outcomes. However, their incremental value for predicting longer-term outcomes is smaller. Therefore, for interventions that are based on long-term predictions, using more broadly available claims data is equally effective.

BACKGROUND: Predictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized. OBJECTIVE: We aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM). DESIGN: This cross-sectional study employed a multisource mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach. PARTICIPANTS: Nurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night. MAIN MEASURES: We performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP. KEY RESULTS: We collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a lowmedium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home. CONCLUSIONS: Our study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.

Background Most of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving patient outcomes. Thus, at-discharge high-risk identification may be too late for effective intervention. Nonetheless, the tradeoff between early versus at-discharge prediction and the optimal timing of the risk prediction model application remains to be determined. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge. Methods An historical prospective study of hospitalized adults (≥65 years) discharged alive from internal medicine units in Clalit’s (the largest integrated payer-provider health fund in Israel) general hospitals in 2015. The outcome was all-cause 30-day emergency readmissions to any internal medicine ward at any hospital. We used the previously validated Preadmission Readmission Detection Model (PREADM) and developed a new model incorporating PREADM with hospital data (PREADM-H). We compared the percentage of overlap between the models and calculated the positive predictive value (PPV) for the subgroups identified by each model separately and by both models. Results The final cohort included 35,156 index hospital admissions. The PREADM-H model included 17 variables with a C-statistic of 0.68 (95% CI: 0.67–0.70) and PPV of 43.0% in the highest-risk categories. Of patients categorized by the PREADM-H in the highest-risk decile, 78% were classified similarly by the PREADM. The 22% ( n = 229) classified by the PREADM-H at the highest decile, but not by the PREADM, had a PPV of 37%. Conversely, those classified by the PREADM into the highest decile but not by the PREADM-H ( n = 218) had a PPV of 31%. Conclusions The timing of readmission risk prediction makes a difference in terms of the population identified at each prediction time point – at-admission or at-discharge. Our findings suggest that readmission risk identification should incorporate a two time-point approach in which preadmission data is used to identify high-risk patients as early as possible during the index admission and an “all-hospital” model is applied at discharge to identify those that incur risk during the hospital stay.