Development of a Clinical Risk and Readmission Prediction Model Using Indian EHR Data

Objective: To design and validate a predictive model that identifies patients at high risk of hospital readmission using electronic health records from Indian healthcare providers.

Problem & Context: Unplanned hospital readmissions are a significant challenge for Indian hospitals, increasing costs and indicating potential gaps in quality of care. Predicting which patients are at risk is difficult due to fragmented EHR systems, variability in clinical pathways, and limited use of advanced analytics in most Indian settings.

Approach/Methodology: The team will use anonymized EHR datasets (e.g., from the MIMIC-III database and/or Indian hospital partners) to analyze patient demographics, comorbidities, treatment history, and discharge summaries. They will apply data cleaning, feature engineering, and machine learning techniques to build a classifier for readmission risk, validating it with real-world Indian data where available.

Deliverables & Analysis: Key deliverables include a documented business case, a detailed methodology, a reproducible predictive model (with code), a dashboard/report for clinicians, and an analysis of model accuracy and practical implications. The team will also provide recommendations for model deployment and integration in Indian hospital workflows.

Business Impact: This solution will help hospital administrators and clinicians proactively identify high-risk patients, enabling targeted interventions, reducing unnecessary readmissions, and optimizing resource allocation. It informs decisions on patient follow-up, discharge planning, and quality improvement initiatives.