Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Clinical Analytics focuses on extracting actionable insight from the data generated by care itself. You learn to work with electronic health records and clinical data — with all their messiness — and build models for risk prediction, outcome and readmission analysis, and quality and cost improvement. The course covers the distinct challenges of clinical data (missingness, bias, temporality) and the validation and fairness that health analytics demands. Grounded in real clinical questions, it turns data into better care. You finish able to apply AI analytics to a clinical problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in clinical analytics — mining electronic health records and clinical data for risk prediction, outcomes analysis and quality improvement.
📋 Course Objectives
1. Work with electronic health records and clinical data.
2. Build clinical risk-prediction models.
3. Analyse outcomes and readmissions.
4. Support quality and cost improvement.
5. Address bias, missingness and validation.
👥 Who Should Enroll?
• Clinical and health-data analysts
• Healthcare data scientists
• Quality and informatics teams
• Students of clinical informatics
🚀 Key Learning Outcomes
• The ability to apply AI to clinical data.
• A clinical-analytics project.
• A validation- and fairness-first approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Building an Analytics-Ready Clinical Dataset
• Extracting from EHR and claims sources into a common data model such as OMOP
• Terminology mapping across SNOMED, ICD, LOINC and RxNorm
• Cohort definition and phenotyping that another team could reproduce
Measuring Care and Outcomes
• Risk-adjusted outcome measurement and case-mix confounding
• Process versus outcome metrics and the gaming each invites
• Variation analysis across clinicians, sites and time
Flow, Capacity and Resource Analytics
• Patient flow, length of stay and bottleneck identification
• Demand forecasting for beds, theatres and staffing
• Simulation for capacity planning under uncertainty
Comparative Effectiveness From Routine Data
• Propensity scoring, matching and their assumptions
• Instrumental variables and difference-in-differences in health settings
• Sensitivity analysis for unmeasured confounding
Dashboards Clinicians Will Actually Use
• Statistical process control charts instead of month-on-month arrows
• Presenting uncertainty in small-denominator comparisons
• Governance, information security and access control for clinical analytics
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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