Analyzing Electronic Health Records to Identify Risk Factors for Unplanned Hospital Readmissions
Keywords:
Electronic health records, unplanned hospital readmission, risk factors, machine learning, healthcare analytics, predictive modeling, comorbidityAbstract
Unplanned hospital readmissions are a significant indicator of healthcare quality and system efficiency. With the increasing adoption of electronic health records (EHRs), advanced analytics now offer new opportunities to identify risk factors contributing to readmission events. This paper examines the application of EHR data for predictive modeling and risk factor analysis related to unplanned readmissions. We synthesize findings from prior literature, apply statistical analysis to simulated datasets, and explore a contemporary healthcare landscape where AI and EHR integration drive outcome improvements. Key risk factors include comorbidities, discharge planning issues, and social determinants of health.
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Copyright (c) 2026 Amanda Roselin, Ijtihadi Phyu Thwe (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
