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Barnes Jewish Hospital (BJH) has developed intervention programs for patients 65 and older diagnosed with acute myocardial infarction (AMI), congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and pneumonia (PNA). Eligible patients are high-risk inpatients identified using the LACE Index Scoring Tool, which is the most widely used method to quantify the risk of readmission, but only achieves a C-statistic of 0.59 in our population, compared to range 0.63–0.70 in other systems. Yu et al. reported that “the institution specific readmission risk prediction framework is more flexible and more effective than the one-size-fit-all models like the LACE”, and several groups have used machine learning models to improve readmissions predictions. Therefore, in this proof of concept study, we use available variables to better identify at-risk patients (compared to the LACE score) at BJH in order to enroll them into the current readmission reduction programs early in their hospital course. We developed a predictive model, which improved our predictions over the LACE score for real-time electronic medical record integration into the electronic medical record.

Development of an Institution-Specific Readmission Risk Prediction Model for Real-time Prediction and Patient-Centered Interventions