About This Course
In an era of rapidly expanding health data, the ability to analyse and act on evidence is no longer optional — it is a core competency for every healthcare leader, administrator, and programme manager. From electronic health records and disease surveillance systems to patient satisfaction surveys and operational dashboards, health organisations generate vast amounts of data every day. Yet translating that data into clear, timely, and defensible decisions remains one of the most persistent challenges in healthcare management.
Healthcare Analytics for Evidence-Based Decisions Training is a focused five-day programme designed to build the skills necessary to harness health data for meaningful impact. Participants will gain a working understanding of health data types, analytical methods, and visualisation techniques, alongside the critical thinking frameworks required to interpret findings in context and communicate them persuasively to diverse stakeholders.
The programme goes beyond theory. Through real-world case studies drawn from hospital operations, public health programmes, and health system strengthening initiatives, participants will practise building and interpreting dashboards, conducting basic statistical analyses, and designing monitoring frameworks aligned to decision cycles. Special attention is given to data quality, ethical use of patient information, and the governance structures that support reliable evidence production.
Whether you are working in a ministry of health, a hospital management team, an NGO delivering health services, or a donor-funded health programme, this training will strengthen your capacity to ask the right questions of your data, identify gaps and trends, and lead evidence-informed change. By the end of the programme, participants will leave with practical skills, ready-to-use analytical tools, and the confidence to champion a culture of evidence in their organisations.
Who Should Attend
- Hospital administrators and health facility managers
- Public health officers and epidemiologists
- Health programme managers in government and NGOs
- Monitoring and evaluation officers working in health sectors
- Clinical informatics and health data officers
- Health policy analysts and planners
- Donor-funded project staff involved in health systems strengthening
- Medical officers seeking to integrate data analysis into clinical leadership
- Nursing officers and allied health professionals in supervisory roles
- Researchers and consultants working in health and development
Learning Objectives
- Define core concepts in healthcare analytics and explain their relevance to evidence-based decision-making
- Identify and classify key health data sources, including electronic health records, HMIS, and surveillance systems
- Apply descriptive and inferential statistical methods to analyse health datasets
- Design and interpret health dashboards and data visualisation outputs for decision support
- Assess data quality dimensions and implement strategies to improve data reliability
- Develop indicator frameworks aligned to health programme objectives and decision cycles
- Conduct root cause analysis of health system performance gaps using analytical evidence
- Apply ethical and governance principles to the collection, storage, and use of patient and population data
- Communicate analytical findings effectively to clinical, managerial, and policy audiences
- Link analytical outputs to strategic planning, resource allocation, and quality improvement initiatives
Course Modules
Module 1: Foundations of Healthcare Analytics
- Defining healthcare analytics and its role in evidence-based decision-making
- The evidence-to-action continuum in health systems
- Types of healthcare analytics: descriptive, diagnostic, predictive, and prescriptive
- Key actors and their data needs: clinicians, managers, planners, and policymakers
- Common barriers to analytics adoption in health organisations
- Overview of the analytics ecosystem in low- and middle-income country health systems
Module 2: Health Data Sources and Information Systems
- Types of health data: clinical, administrative, epidemiological, and financial
- Electronic health records and their data architecture
- Health Management Information Systems (HMIS) and district-level reporting
- Disease surveillance systems and their reporting pathways
- Community health data sources and household survey methodologies
- Interlinking data sources for a comprehensive health picture
Module 3: Data Quality in Health Settings
- Core dimensions of data quality: accuracy, completeness, timeliness, consistency, and integrity
- Common causes of poor data quality in health facilities
- Data quality assessment frameworks and audit tools
- Strategies for data cleaning and validation prior to analysis
- Building a data quality culture within health teams
- Routine Data Quality Assessment (RDQA) methods and their application
Module 4: Statistical Methods for Health Data Analysis
- Descriptive statistics: measures of central tendency, spread, and frequency distributions
- Health rates, ratios, and proportions in epidemiological analysis
- Cross-tabulation and stratification to explore health disparities
- Introduction to inferential statistics: confidence intervals and hypothesis testing concepts
- Correlation analysis and interpreting association in health data
- Common pitfalls in health data interpretation and how to avoid them
Module 5: Health Indicators and Performance Measurement
- Defining and selecting indicators: input, process, output, outcome, and impact
- Aligning indicators to programme logic models and results frameworks
- SMART indicator design: specificity, measurability, and data feasibility
- Baselines, targets, and benchmarking in health programmes
- National and global indicator frameworks: SDG health indicators and WHO core indicators
- Monitoring dashboards as tools for tracking indicator performance over time
Module 6: Data Visualisation for Health Decision Support
- Principles of effective health data visualisation
- Chooser guides: selecting the right chart type for the right message
- Building operational health dashboards in Excel and introductory BI tools
- Spatial and geographic data visualisation for disease mapping
- Designing visualisations for different audiences: clinical staff versus executives versus community
- Avoiding common visualisation errors and misleading graphical representations
Module 7: Analytical Methods for Health System Performance
- Identifying and interpreting health system bottlenecks through data
- Root cause analysis techniques: fishbone diagrams, five whys, and Pareto analysis
- Benchmarking facility and programme performance against peers and standards
- Trend analysis and forecasting for health service planning
- Efficiency analysis: linking inputs to outputs in health service delivery
- Using analytics to support quality improvement cycles (PDSA and similar frameworks)
Module 8: Ethics, Governance, and Data Protection in Healthcare
- Ethical principles governing health data collection and use
- Patient confidentiality, informed consent, and anonymisation requirements
- Data governance frameworks and organisational data management policies
- Legal and regulatory landscape for health data: national laws and international standards
- Data sharing agreements and responsible secondary use of health data
- Building institutional accountability for ethical data practices
Module 9: Communicating Evidence to Decision-Makers
- Structuring analytical findings for different stakeholder audiences
- Writing concise and persuasive health evidence briefs
- Designing executive summaries and one-page policy digests
- Presenting data in meetings, reviews, and strategic planning sessions
- Handling uncertainty and conflicting data when presenting to decision-makers
- Tactics for influencing health policy with evidence without overstepping analytical roles
Module 10: Embedding Analytics in Organisational Strategy and Planning
- Integrating analytics outputs into strategic planning and budget cycles
- Building an analytics-ready organisation: people, processes, and infrastructure
- Designing analytical workflows and governance committees within health organisations
- Linking evidence to resource allocation, procurement, and workforce planning decisions
- Developing a health analytics action plan for the participant's own organisation
- Sustaining an evidence culture: leadership commitment, learning loops, and continuous improvement
Requirements & Prerequisites
Participants should have at least two years of professional experience in a health, public health, or health programme management role. A basic familiarity with health information systems or routine health data reporting is recommended. No advanced statistical background is required, though comfort working with spreadsheets or simple data tables will be beneficial.
Training Methodology
- Guided analysis of real anonymised health datasets from hospital, district health, and programme contexts
- Interactive dashboard-building exercises using accessible tools such as Excel and introductory BI platforms
- Case study discussions drawn from East African and global health system experiences
- Small-group indicator design workshops tied to participants' actual programme contexts
- Data quality audit simulations where participants assess and score sample datasets against standard dimensions
- Peer critique sessions in which participants present analytical findings and receive structured feedback
- Facilitated debate on ethical dilemmas in health data use, privacy, and community consent
- Practical stakeholder communication exercises including the drafting of evidence briefs and executive summaries
Organisational Benefits
- Improved quality and reliability of health data used for internal and external reporting
- Stronger capacity to monitor programme performance and respond promptly to emerging trends
- Evidence-based resource allocation that improves efficiency and reduces waste
- Enhanced accountability to funders, regulators, and communities through transparent data use
- Better alignment between health system data and strategic planning processes
- Reduced dependence on external consultants for routine data analysis and interpretation
- A workforce equipped to sustain quality improvement cycles grounded in evidence
- Improved compliance with national health information system standards and donor reporting requirements
- Strengthened organisational culture of learning and continuous improvement
- Greater credibility with partners, government counterparts, and oversight bodies
Where Is Healthcare Analytics for Evidence-Based Decisions Training Offered?
This course runs in-person across: Pretoria, Arusha, Addis Ababa, Nakuru, Zanzibar, Abuja, Nairobi, Dubai, Kisumu, Riyadh, Lagos, Kigali, Kampala, Dar es Salaam, Mombasa, Naivasha, Johannesburg, Accra, Cape Town, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
24 Oct 2026 – 15 Nov 2026 |
KES 80,000 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,300 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,100 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,400 |
| Live Virtual |
Online |
26 Oct 2026 – 30 Oct 2026 |
KES 80,000 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,800 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 115,000 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,100 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Zanzibar, Tanzania |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,500 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Nakuru, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 120,000 |
+237 more dates — view live schedule →
Frequently Asked Questions
Do I need a background in statistics or data science to attend?
No advanced statistical background is required. The programme is designed for health professionals and managers who work with data in their roles but are not necessarily trained analysts. Comfort with basic spreadsheets is helpful, and all analytical concepts are introduced progressively from the ground up.
What software or tools will be used during the training?
The course primarily uses Microsoft Excel for data analysis and visualisation exercises, making it accessible to participants without specialised software licences. Introductory exposure to business intelligence dashboarding concepts is also provided, and facilitators will demonstrate additional tools where relevant.
Will I receive a certificate upon completion?
Yes. Participants who attend the full five-day programme and complete the practical exercises will receive a Certificate of Completion from TrainingComplex, recognised for continuing professional development purposes.
Is this course relevant for participants from NGOs and donor-funded programmes, not just government health facilities?
Absolutely. The programme content is designed to be applicable across government health systems, hospital management, NGO-delivered health programmes, and donor-funded projects alike. Case studies and exercises draw on diverse health contexts to ensure relevance for all participants.
Can my organisation send a team to attend together?
Yes, and it is highly encouraged. Sending a team of two or more colleagues from the same organisation allows participants to work on shared datasets and co-develop an analytics action plan relevant to your specific context. Group enrolment discounts may also be available — contact TrainingComplex for details.
