Data Quality Assessment Advanced Skills for Health Training Course

Course Overview

Data Quality Assessment Advanced Skills for Health Training Course is designed to equip healthcare professionals, monitoring and evaluation specialists, health information managers, researchers, and development practitioners with advanced knowledge and practical skills to assess, improve, and maintain the quality of health data used for planning, monitoring, evaluation, reporting, and decision-making. As healthcare systems increasingly rely on Health Management Information Systems (HMIS), Electronic Medical Records (EMRs), Digital Health Information Systems (DHIS2), Results-Based Management (RBM), Universal Health Coverage (UHC), Sustainable Development Goals (SDGs), and donor-funded performance reporting, ensuring high-quality health data has become essential for improving healthcare outcomes and organizational performance. This comprehensive training provides internationally recognized methodologies for conducting systematic Data Quality Assessments (DQAs) that enhance data accuracy, completeness, timeliness, consistency, integrity, validity, and reliability across healthcare programs and institutions.

The course provides participants with practical competencies in designing and implementing Data Quality Assessment frameworks, developing data quality standards, conducting routine data verification, performing data audits, analyzing data quality indicators, and implementing continuous quality improvement strategies. Participants will learn how to evaluate health data generated from hospitals, laboratories, disease surveillance systems, maternal and child health programs, HIV/AIDS services, immunization programs, nutrition interventions, emergency response initiatives, and community health information systems. The training integrates advanced digital tools including DHIS2, ODK, KoboToolbox, Microsoft Power BI, Microsoft Excel, SQL databases, GIS, statistical software, and health information dashboards to strengthen data quality monitoring and reporting.

Participants will further explore advanced concepts including data governance, metadata management, interoperability standards, master data management, data security, data validation rules, root cause analysis, risk management, automated quality control systems, and organizational data governance frameworks. The course emphasizes the importance of aligning Data Quality Assessments with national health information systems, WHO data quality frameworks, donor reporting standards, and international health information management guidelines. Through practical exercises and healthcare-focused case studies, participants will develop the expertise required to identify data quality challenges, implement corrective actions, improve reporting accuracy, and support evidence-based healthcare management.

Using interactive workshops, practical demonstrations, simulations, collaborative group work, and real-world healthcare case studies, participants will gain hands-on experience in planning, conducting, analyzing, and reporting comprehensive Data Quality Assessments. Upon successful completion of the course, participants will possess the technical skills needed to strengthen health information systems, improve organizational data governance, enhance program accountability, support strategic planning, and promote high-quality healthcare decision-making through reliable and accurate health information.

Course Objectives

1. Understand international principles and frameworks for health Data Quality Assessment.
2. Conduct comprehensive Data Quality Assessments across healthcare programs.
3. Evaluate data accuracy, completeness, consistency, validity, and timeliness.
4. Apply advanced data verification and validation techniques.
5. Develop organizational data quality improvement plans.
6. Strengthen Health Management Information Systems (HMIS) through quality assurance.
7. Utilize digital tools for automated data quality monitoring.
8. Conduct root cause analysis for data quality challenges.
9. Prepare professional Data Quality Assessment reports and recommendations.
10. Promote evidence-based healthcare planning through high-quality data.

Organizational Benefits

1. Improves the accuracy and reliability of health information.
2. Strengthens organizational data governance and accountability.
3. Enhances evidence-based planning and policy development.
4. Improves compliance with donor and regulatory reporting requirements.
5. Supports continuous quality improvement initiatives.
6. Reduces reporting errors and data inconsistencies.
7. Strengthens Health Management Information Systems (HMIS).
8. Improves organizational performance monitoring and evaluation.
9. Enhances strategic decision-making using reliable health data.
10. Builds institutional capacity for sustainable data quality management.

Target Participants

This course is suitable for:

• Monitoring and Evaluation Officers
• Health Information Managers
• Health Records Officers
• Public Health Specialists
• Epidemiologists
• Data Analysts
• Biostatisticians
• Health Researchers
• Hospital Administrators
• Ministry of Health Officials
• Health Program Managers
• NGO Monitoring and Evaluation Staff
• Donor-funded Project Managers
• Clinical Data Managers
• Disease Surveillance Officers
• Digital Health Specialists
• Quality Assurance Officers
• Health Informatics Professionals
• Healthcare Consultants
• Professionals responsible for health information systems and data quality management.

Course Outline

Module 1: Foundations of Data Quality Assessment in Health

• Principles of data quality management
• WHO Data Quality Review Framework
• Data quality dimensions
• Health information systems overview
• Results-Based Management integration
• International best practices

Case Study: Conducting a national Data Quality Assessment for routine health information systems.

Module 2: Health Data Governance and Quality Standards

• Data governance principles
• Health data standards
• Metadata management
• Data ownership
• Organizational policies
• Regulatory compliance

Case Study: Developing a health data governance framework for a referral hospital.

Module 3: Data Quality Dimensions and Performance Indicators

• Accuracy assessment
• Completeness measurement
• Timeliness evaluation
• Consistency analysis
• Validity assessment
• Integrity monitoring

Case Study: Evaluating data quality indicators within an immunization reporting system.

Module 4: Data Verification and Validation Techniques

• Source document verification
• Data reconciliation
• Cross-validation methods
• Duplicate detection
• Error identification
• Automated validation rules

Case Study: Verifying maternal health service data across multiple reporting levels.

Module 5: Health Information Systems Assessment

• DHIS2 quality assessment
• Electronic Medical Records evaluation
• Laboratory information systems
• Disease surveillance systems
• Community health reporting
• Data interoperability

Case Study: Assessing data quality within a national disease surveillance information system.

Module 6: Data Collection Quality Assurance

• Standard operating procedures
• Digital data collection tools
• Enumerator supervision
• Field monitoring
• Quality control checklists
• Real-time validation

Case Study: Improving data quality during nationwide health facility assessments.

Module 7: Data Analysis and Root Cause Identification

• Data profiling
• Statistical quality analysis
• Trend analysis
• Root cause analysis
• Performance benchmarking
• Risk assessment

Case Study: Identifying causes of inconsistent tuberculosis treatment reporting.

Module 8: Data Quality Improvement Strategies

• Continuous quality improvement
• Corrective action planning
• Capacity building
• Workflow optimization
• Process redesign
• Performance monitoring

Case Study: Developing a data quality improvement strategy for district health facilities.

Module 9: Digital Technologies for Data Quality Management

• Power BI dashboards
• GIS quality monitoring
• SQL data validation
• Artificial Intelligence applications
• Automated reporting
• Business intelligence systems

Case Study: Implementing automated data quality dashboards for health program monitoring.

Module 10: Reporting and Communication of Data Quality Findings

• Data Quality Assessment reports
• Executive summaries
• Visualization techniques
• Stakeholder communication
• Donor reporting
• Quality improvement recommendations

Case Study: Preparing a national Data Quality Assessment report for Ministry of Health decision-makers.

Module 11: Organizational Data Quality Management Systems

• Data quality frameworks
• Institutional capacity strengthening
• Performance monitoring
• Internal audits
• Quality assurance committees
• Sustainability planning

Case Study: Establishing a hospital-wide data quality management system to improve reporting performance.

Module 12: Emerging Trends in Health Data Quality Management

• Artificial Intelligence in data quality
• Big data analytics
• Machine learning validation
• Cloud-based health information systems
• Predictive data quality monitoring
• Future innovations in digital health

Case Study: Using Artificial Intelligence to improve data quality monitoring within national Health Management Information Systems.

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, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of the 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 in-house and online training options customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of training 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.