Data Governance Performance Improvement for Health Training Course

Course Overview

Data Governance Performance Improvement for Health Training Course is designed to equip healthcare professionals, health information managers, monitoring and evaluation specialists, public health practitioners, health program managers, researchers, policymakers, and digital health experts with comprehensive knowledge and practical skills in establishing robust data governance frameworks that improve organizational performance, healthcare quality, regulatory compliance, and evidence-based decision-making. As healthcare organizations continue to advance Health Management Information Systems (HMIS), Electronic Medical Records (EMRs), Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Universal Health Coverage (UHC), Sustainable Development Goals (SDGs), digital health transformation, implementation science, interoperability, healthcare analytics, artificial intelligence, and health systems strengthening, effective data governance has become essential for ensuring data quality, security, accessibility, accountability, and continuous performance improvement. This course provides participants with practical strategies for managing healthcare data as a strategic organizational asset.

The course introduces participants to internationally recognized data governance principles, including data stewardship, master data management, metadata management, data quality management, healthcare interoperability, information security, privacy compliance, FAIR data principles, ISO standards, data lifecycle management, risk management, business intelligence, performance measurement, and healthcare analytics. Participants will learn to integrate healthcare data from DHIS2, HMIS, Electronic Medical Records, Laboratory Information Management Systems (LIMS), SurveyCTO, KoboToolbox, Open Data Kit (ODK), Power BI, Tableau, Excel, SPSS, STATA, R, Python, and GIS into secure, high-quality information ecosystems that support operational excellence, healthcare planning, policy formulation, and donor reporting.

Participants will also explore advanced topics such as healthcare data governance maturity models, cloud computing, artificial intelligence for healthcare data management, predictive analytics, interoperability frameworks, cybersecurity, digital transformation, health information exchange, audit readiness, performance dashboards, regulatory compliance, and organizational learning. Practical exercises demonstrate how strong governance improves clinical decision-making, healthcare quality, patient safety, resource utilization, disease surveillance, emergency preparedness, research quality, and institutional accountability while reducing risks associated with poor-quality data.

Through instructor-led workshops, software demonstrations, practical laboratories, collaborative group exercises, governance simulations, and comprehensive healthcare case studies, participants will gain hands-on experience in designing, implementing, monitoring, and continuously improving healthcare data governance systems. Upon successful completion of the course, participants will possess the competencies required to strengthen organizational data governance, improve healthcare performance, enhance digital transformation initiatives, support evidence-based policy development, optimize healthcare service delivery, and foster a culture of data-driven decision-making across ministries of health, hospitals, universities, research institutions, NGOs, humanitarian organizations, and international development agencies.

Course Objectives

1. Understand the principles and frameworks of healthcare data governance.
2. Develop effective data governance strategies for healthcare organizations.
3. Strengthen healthcare data quality, integrity, and reliability.
4. Implement healthcare data privacy, security, and regulatory compliance measures.
5. Utilize digital technologies to improve healthcare data management.
6. Design performance monitoring systems using healthcare data.
7. Develop interactive dashboards for healthcare performance improvement.
8. Apply analytics to support evidence-based healthcare decision-making.
9. Strengthen organizational accountability through effective data governance.
10. Promote continuous performance improvement through data-driven management.

Organizational Benefits

1. Strengthens organizational data governance frameworks.
2. Improves healthcare data quality, consistency, and accessibility.
3. Enhances compliance with healthcare regulations and standards.
4. Improves evidence-based planning and strategic decision-making.
5. Strengthens monitoring, evaluation, accountability, and learning systems.
6. Enhances patient safety and healthcare quality.
7. Improves operational efficiency through digital transformation.
8. Supports healthcare performance monitoring and continuous improvement.
9. Reduces organizational risks associated with poor data governance.
10. Builds institutional capacity for sustainable digital health management.

Target Participants

• Health Information Managers
• Monitoring and Evaluation Officers
• Public Health Specialists
• Health Program Managers
• Hospital Administrators
• Ministry of Health Officials
• Digital Health Specialists
• Data Governance Officers
• Data Analysts
• Epidemiologists
• Health Researchers
• ICT Managers
• Health Informatics Specialists
• Quality Improvement Officers
• Policy Analysts
• NGO and Humanitarian Project Staff
• Development Partners
• Healthcare Consultants
• Clinical Managers
• Professionals responsible for healthcare data management, governance, analytics, monitoring, evaluation, and organizational performance improvement.

Course Outline

Module 1: Introduction to Healthcare Data Governance

• Principles of data governance
• Healthcare data ecosystems
• Governance frameworks
• Organizational roles and responsibilities
• Data governance maturity models
• International best practices

Case Study: Establishing a national healthcare data governance framework to improve service delivery.

Module 2: Data Governance Policies and Standards

• Governance policies
• Data ownership
• Data stewardship
• Metadata management
• Standard operating procedures
• ISO and healthcare standards

Case Study: Developing enterprise data governance policies for a regional hospital network.

Module 3: Healthcare Data Quality Management

• Data quality dimensions
• Data validation
• Data cleansing
• Data standardization
• Data quality assessment
• Continuous improvement

Case Study: Improving routine health information quality across district health facilities.

Module 4: Data Privacy, Security, and Compliance

• Healthcare data privacy
• Information security
• Access control
• Cybersecurity
• Regulatory compliance
• Risk management

Case Study: Implementing secure patient data management systems compliant with national regulations.

Module 5: Digital Health Information Systems

• Health Management Information Systems (HMIS)
• DHIS2
• Electronic Medical Records
• Laboratory Information Systems
• Health Information Exchange
• Interoperability standards

Case Study: Integrating multiple health information systems to improve patient care coordination.

Module 6: Digital Data Collection and Integration

• SurveyCTO
• KoboToolbox
• Open Data Kit (ODK)
• Mobile health technologies
• Cloud integration
• Data synchronization

Case Study: Digitizing healthcare data collection for nationwide disease surveillance.

Module 7: Performance Measurement and Analytics

• Key Performance Indicators (KPIs)
• Healthcare scorecards
• Business intelligence
• Statistical analysis
• Predictive analytics
• Benchmarking

Case Study: Measuring hospital performance using integrated healthcare analytics.

Module 8: Dashboard Development and Visualization

• Power BI dashboards
• Tableau visualization
• Executive dashboards
• Interactive reports
• Geographic Information Systems (GIS)
• Data storytelling

Case Study: Designing executive dashboards to monitor healthcare quality and operational performance.

Module 9: Artificial Intelligence and Digital Innovation

• Artificial Intelligence applications
• Machine learning
• Automated data quality monitoring
• Predictive healthcare analytics
• Intelligent decision support
• Digital transformation strategies

Case Study: Implementing AI-powered healthcare data governance to improve predictive patient care.

Module 10: Organizational Performance Improvement

• Continuous quality improvement
• Lean healthcare principles
• Performance optimization
• Organizational learning
• Change management
• Strategic planning

Case Study: Using healthcare performance data to improve emergency department efficiency.

Module 11: Monitoring, Evaluation, Accountability and Learning (MEAL)

• Results-Based Management
• Performance monitoring
• Evaluation systems
• Organizational accountability
• Adaptive management
• Learning frameworks

Case Study: Integrating data governance into national healthcare monitoring and evaluation systems.

Module 12: Emerging Trends in Healthcare Data Governance

• FAIR data principles
• Cloud computing
• Blockchain in healthcare
• Digital twins
• Precision public health
• Future trends in healthcare governance

Case Study: Designing an integrated national healthcare data governance ecosystem that combines HMIS, DHIS2, Artificial Intelligence, cloud computing, blockchain, predictive analytics, and executive dashboards to strengthen healthcare performance, accountability, and evidence-based decision-making.

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, software demonstrations, collaborative group work, governance simulations, and real-world healthcare case studies. Our facilitators are seasoned experts with over a decade of experience in public health, digital health, healthcare data governance, monitoring and evaluation, health information systems, and organizational performance improvement.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call +254712260031.
14. Website: Visit www.fdc-k.org for more information.