Program Structure

Module 1: Foundations of Healthcare Analytics and AI

• Understanding clinical data and why healthcare analytics is unique.
• Key AI use cases in hospitals, clinics, and public health systems.
• Connecting analytics outputs to real clinical decisions.

Module 2: Healthcare Data Preparation and Management

• Clinical data sources including EHRs, lab results, vitals, and outcomes.
• Handling missing data, coding inconsistencies, and noisy records.
• Privacy protection, data security, and compliance basics.

Module 3: Exploratory Analytics and Feature Design

• Building meaningful features from patient history and clinical events.
• Identifying patient cohorts and risk groups.
• Creating dashboards and summary metrics for clinical reporting.

Module 4: Predictive Modeling for Clinical Outcomes

• Risk prediction for readmissions, complications, and deterioration.
• Choosing suitable models for healthcare prediction tasks.
• Evaluating performance using clinically relevant metrics.

Module 5: Time-Based Analysis and Monitoring

• Tracking trends in admissions, infections, and care demand.
• Using time-based indicators for alerts and monitoring.
• Supporting early intervention through analytics.

Module 6: Clinical Text Analytics

• Analyzing clinical notes, reports, and discharge summaries.
• Extracting symptoms, conditions, and treatment information.
• Supporting documentation and reporting workflows.

Module 7: Operational Analytics for Healthcare Facilities

• Analyzing bed occupancy, staffing needs, and patient flow.
• Forecasting resource requirements and service demand.
• Identifying inefficiencies and workflow bottlenecks.

Module 8: Explainability, Validation, and Safety

• Communicating analytics results to clinicians and administrators.
• Validating models across populations and time periods.
• Managing uncertainty and ensuring patient safety.

Module 9: Ethics, Bias, and Governance

• Recognizing bias and fairness issues in healthcare analytics.
• Responsible reporting and documentation practices.
• Governance, monitoring, and audit readiness.

Final Project

• Design an AI-driven healthcare analytics solution.
• Define data requirements, workflow, evaluation metrics, and safeguards.
• Example projects include readmission risk analysis, resource forecasting, or clinical reporting dashboards.