Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions

Abstract

BACKGROUND: Electronic health record-derived data and novel analytics, such as machine learning, offer promising approaches to identify high-risk patients and inform nursing practice. PURPOSE: The aim was to identify patients at risk for readmissions by applying a machine-learning technique, Classification and Regression Tree, to electronic health record data from our 300-bed hospital. METHODS: We conducted a retrospective analysis of 2165 clinical encounters from August to October 2017 using data from our health system's data store. Classification and Regression Tree was employed to determine patient profiles predicting 30-day readmission. RESULTS: The 30-day readmission rate was 11.2% (n = 242). Classification and Regression Tree analysis revealed highest risk for readmission among patients who visited the emergency department, had 9 or more comorbidities, were insured through Medicaid, and were 65 years of age and older. CONCLUSIONS: Leveraging information through the electronic health record and Classification and Regression Tree offers a useful way to identify high-risk patients. Findings from our algorithm may be used to improve the quality of nursing care delivery for patients at highest readmission risk.

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