📝 General Introduction 

In the era of digital transformation, health data has become one of the most valuable assets for medical institutions seeking to improve care quality, optimize operations, and make evidence-based decisions.

Analyzing health data goes beyond collecting numbers—it involves identifying patterns, predicting risks, and guiding strategic actions.

Hospitals and medical centers that leverage data effectively can enhance patient outcomes, reduce errors, and allocate resources more efficiently. This 5-day workshop is designed to empower healthcare professionals with the skills to understand, analyze, and interpret health data for informed decision-making.

Participants will learn how to collect, clean, and process data using modern tools and techniques, and how to translate insights into actionable strategies. Through practical exercises and real-world applications, attendees will gain the confidence to use data as a powerful tool for driving performance, improving services, and shaping the future of healthcare.

🎯 Target Audience

• Hospital and medical center managers
• Quality and health planning officers
• Health data analysts and IT professionals
• Medical and administrative team leaders
• Students of public health and healthcare management

🎯 Expected Objectives

• Understand the importance of health data analysis in decision-making
• Learn tools and techniques for analyzing health data
• Develop skills in reading and interpreting health performance indicators
• Use data to improve service quality and operational efficiency
• Build analytical models to support strategic decisions

📚 Scientific Topics:

Module 1: Introduction to Health Data Analysis

Session 1: Types and Sources of Health Data

• Patient records and clinical data
• Operational and administrative data
• Internal vs. external data sources

Session 2: The Role of Data in Healthcare Decision-Making

• Evidence-based decision support
• Enhancing care quality
• Risk prediction and prevention

Module 2: Tools and Techniques for Analysis

Session 1: Data Collection and Cleaning Methods

• Surveys and electronic reports
• Handling missing and duplicate data
• Structuring databases

Session 2: Descriptive and Statistical Analysis Techniques

• Averages and standard deviation
• Trend and correlation analysis
• Tables and visualizations

Module 3: Performance Indicators and Predictive Analysis

Session 1: Building Health Performance Indicators

• Quality and safety metrics
• Operational efficiency indicators
• Patient experience measures

Session 2: Predictive Analysis for Strategic Decisions

• Forecasting models in healthcare
• Scenario planning
• Strategic decision support

Module 4: Practical Applications in Data Analysis

Session 1: Analyzing Departmental Health Data

• Emergency and outpatient data
• HR and staffing metrics
• Cost and productivity data

Session 2: Creating Effective Analytical Reports

• Designing executive dashboards
• Presenting results clearly
• Data-driven recommendations

Module 5: Evaluation and Model Development

Session 1: Reviewing Participant Data Projects

• Showcasing applied models
• Evaluating analysis quality
• Peer and trainer feedback

Session 2: Building a Decision-Support Model

• Defining analytical goals
• Selecting appropriate tools
• Integrating analysis into institutional decision cycles