Training Course on Data Analytics For Healthcare Training Course: Transforming Data Into Actionable Insights

Module 1: Introduction to Healthcare Data Analytics
Foundations of Healthcare Data Analytics
Definition, purpose, and impact of data analytics in healthcare.
Role of data analytics in improving patient care, operational efficiency, and strategic planning.
Types of Healthcare Data
Clinical data (EMR, lab results, imaging reports).
Administrative and operational data (staffing, workflow, scheduling).
Financial data (billing, claims, insurance).
Patient-generated data (wearables, surveys, mobile apps).
Key Concepts
Data-driven decision-making, key performance indicators (KPIs), benchmarks, and metrics.
Activity: Explore sample healthcare datasets to identify potential insights and analytics opportunities.

Module 2: Data Collection, Cleaning, and Management
Healthcare Data Sources
Electronic Medical Records (EMRs), Hospital Information Systems (HIS), laboratory systems, insurance claims, and patient-generated data.
Data Quality and Cleaning
Detecting and handling missing, inconsistent, or duplicate data.
Data validation and integrity checks.
Database Management and Structuring
Organizing data for efficient querying and analysis.
Introduction to relational databases and data warehouses in healthcare.
Workshop: Clean and transform a raw healthcare dataset, ensuring accuracy, consistency, and readiness for analysis.

Module 3: Descriptive Analytics in Healthcare
Data Summarization and Exploration
Frequency distributions, central tendency, variability, and trends.
Identifying patterns, anomalies, and outliers in patient and operational data.
Data Visualization Techniques
Charts, histograms, heatmaps, dashboards, and infographics.
Visual storytelling for healthcare decision-makers.
Reporting and Communication
Preparing reports for clinicians, administrators, and policy makers.
Exercise: Generate descriptive analytics reports and dashboards using sample hospital or clinic datasets to identify operational and clinical insights.

Module 4: Predictive and Advanced Analytics
Introduction to Predictive Modelling
Regression analysis, classification models, decision trees, clustering, and time-series forecasting.
Predicting patient outcomes, resource utilization, and operational bottlenecks.
Advanced Analytics Techniques
Machine learning basics, model training, testing, and validation.
Risk stratification and predictive scoring for patient care.
Ethics and Compliance
Ensuring patient privacy, security, and compliance with regulations (HIPAA, GDPR).
Lab: Build and test predictive models using real or simulated patient and operational datasets, interpreting outputs to inform healthcare decisions.

Module 5: Data-Driven Decision Making and Visualization
Translating Analytics into Action
Identifying actionable insights for clinical, administrative, and operational improvement.
Decision support frameworks for healthcare management.
Interactive Dashboards and KPI Tracking
Designing user-friendly dashboards for monitoring performance and outcomes.
Real-time analytics for hospital operations, patient care, and resource allocation.
Stakeholder Communication
Presenting data insights effectively to clinicians, management, and policymakers.
Creating reports that inform strategic planning and policy decisions.
Capstone Project: Develop a comprehensive analytics report and interactive dashboard using a real-world healthcare dataset, providing actionable
recommendations for operational, clinical, and strategic decision-making