Optimizing Healthcare & Clinical Analytics with AI

Aim

This course teaches how AI strengthens healthcare analytics for better clinical and operational decisions. Participants learn how to structure healthcare data, build meaningful KPIs, apply predictive methods for risk and outcomes, and design dashboards and monitoring plans that are safe, explainable, and adoption-ready.

Who This Course Is For

• Hospital administrators, quality teams, and clinical operations leaders
• Clinicians and clinical informatics professionals working with health data
• HealthTech product/analytics teams building decision-support dashboards
• Data analysts, BI professionals, and AI teams in healthcare
• Researchers and students working on clinical analytics projects

Prerequisites

• No coding required (optional analytics demos can be included)
• Basic understanding of clinical workflows and patient journey is helpful
• Comfort with KPIs and dashboards is a plus

What You’ll Learn

• Healthcare data basics: EHR, labs, imaging reports, claims, and operational data
• Data quality: missingness, coding standards (overview), and bias awareness
• Clinical KPI design: outcomes, safety, utilization, readmissions, LOS, mortality (context-based)
• Risk stratification and patient cohorting for targeted interventions
• Predictive analytics: early warning signals, deterioration risk, no-show risk, readmission risk
• Clinical pathways analytics: bottleneck detection and variation analysis
• Dashboard design: actionable views for clinicians vs administrators
• Model evaluation: performance, subgroup checks, explainability basics
• Monitoring and governance: drift, thresholds, audit trails, and safe deployment

Program Structure

Module 1: Clinical Analytics and Decision-Making

• How analytics supports quality improvement and patient safety
• Key stakeholders and decision points in hospitals
• Defining success metrics and adoption goals

Module 2: Healthcare Data and Preparation

• Data types: structured, semi-structured, and clinical notes (overview)
• Data cleaning, data linkage, and cohort definition
• Bias and representativeness checks

Module 3: KPI Frameworks for Clinical and Operational Performance

• Outcome, process, and balance measures
• Dashboards for utilization and throughput
• Designing KPIs that lead to action (not just reporting)

Module 4: Risk Stratification and Predictive Models

• Risk scoring concepts and patient prioritization
• Predicting readmissions, deterioration, and length of stay (overview)
• Setting thresholds and escalation pathways

Module 5: Clinical Pathway and Variation Analytics

• Pathway mapping and bottleneck detection
• Variation analysis across units, clinicians, and time windows
• Linking variation to improvement actions

Module 6: Dashboard and Reporting Design

• Role-based dashboards: clinical, nursing, admin, and leadership views
• Alert fatigue reduction and prioritization logic
• Monthly/weekly reporting packs and review cadence

Module 7: Evaluation, Explainability, and Safety Checks

• Performance metrics and subgroup evaluation
• Explainability basics for clinical trust
• Human oversight and safe workflow integration

Module 8: Monitoring, Governance, and Implementation

• Monitoring drift, model stability, and threshold performance
• Governance: documentation, approvals, audit trails
• Implementation plan: training, adoption, and impact measurement

Tools & Templates Covered

• Clinical KPI selection worksheet (role-based)
• Cohort definition and risk stratification checklist
• Dashboard layout and alert design guidelines
• Evaluation and monitoring plan template
• Governance checklist for analytics and AI deployment

Outcomes

• Build a KPI and analytics plan aligned to clinical and operational goals
• Design risk stratification and predictive analytics workflows
• Create dashboards and alerts that support action and reduce noise
• Apply evaluation, monitoring, and governance for safe implementation

Certificate Criteria (Optional)

• Complete learning checkpoints
• Submit a clinical analytics plan (KPIs + dashboard + monitoring approach)