Identification of elderly patients at risk for 30-day readmission: Clinical insight beyond big data prediction.

AIM: This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model. BACKGROUND: Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate. METHODS: This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk. RESULTS: A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively). CONCLUSIONS: Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients. IMPLICATIONS FOR NURSING MANAGEMENT: This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.

Preventing Hospital Readmissions: Healthcare Providers' Perspectives on "Impactibility" Beyond EHR 30-Day Readmission Risk Prediction.

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 multi-source 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 low-medium 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.