Training Course on Healthcare Analytics & Data Training Course: Transforming Healthcare Through Data-driven Insights

Module 1: Introduction to Healthcare Analytics
Foundations of Healthcare Analytics
Definition, scope, and significance of analytics in modern healthcare systems.
Role in improving patient outcomes, operational efficiency, and strategic planning.
Types of Analytics
Descriptive analytics: summarizing past performance.
Diagnostic analytics: understanding why outcomes occur.
Predictive analytics: forecasting future trends and risks.
Prescriptive analytics: recommending optimal actions.
Healthcare Data Sources
Clinical data (EMRs, lab results, imaging).
Administrative and operational data (staffing, workflow, appointments).
Financial and insurance data (billing, claims, reimbursement).
Patient-generated data (wearables, surveys, mobile apps).
Activity: Examine sample healthcare datasets and identify opportunities for analytics-driven insights.

Module 2: Data Management and Preparation
Data Collection and Integration
Best practices for gathering structured and unstructured healthcare data.
Combining multiple data sources for comprehensive analysis.
Data Cleaning and Validation
Handling missing values, outliers, inconsistencies, and duplicates.
Ensuring accuracy, reliability, and usability of datasets.
Database Design and Management
Structuring data for efficient querying and analysis.
Introduction to relational databases, data warehouses, and data lakes.
Workshop: Prepare, clean, and structure a raw healthcare dataset for analysis, ensuring quality and consistency.

Module 3: Descriptive Analytics and Visualization
Data Summarization Techniques
Frequency counts, averages, trends, distributions, and cross-tabulations.
Identifying patterns, anomalies, and correlations.
Visualization for Insights
Creating charts, heatmaps, dashboards, and infographics.
Visual storytelling for clinical, operational, and strategic decisions.
Reporting
Translating data into actionable reports for stakeholders.
Exercise: Develop descriptive analytics reports and dashboards from sample hospital datasets, highlighting key insights and operational improvements.

Module 4: Predictive and Advanced Analytics
Predictive Modelling in Healthcare
Regression analysis, classification, clustering, and time-series forecasting.
Identifying high-risk patients, predicting disease outbreaks, and optimizing resource allocation.
Machine Learning Applications
Introduction to supervised and unsupervised learning.
Model training, testing, validation, and performance evaluation.
Ethical Considerations
Ensuring data privacy, confidentiality, and compliance with regulations (HIPAA, GDPR).
Lab: Build and evaluate predictive models using healthcare datasets, interpreting outputs to guide clinical and operational decision-making.

Module 5: Data-Driven Decision Making and Healthcare Transformation
Translating Insights into Action
Applying analytics findings to improve patient care, workflow, and strategic planning.
Evidence-based decision-making frameworks.
Interactive Dashboards and KPI Monitoring
Real-time dashboards for tracking clinical and operational metrics.
Customizing dashboards for different stakeholders: administrators, clinicians, and policymakers.
Communication and Stakeholder Engagement
Presenting analytics insights in a clear, concise, and actionable format.
Using data to advocate for process improvements, policy changes, and strategic initiatives.
Capstone Project: Develop a comprehensive healthcare analytics report and interactive dashboard using a real-world dataset, providing actionable
recommendations for operational, clinical, and strategic improvement