Primary audiences include:
- Healthcare operational leaders
- Service managers
- Clinicians
- Public health teams
- BI and analytics teams
- Integrated Care Board colleagues
- NHS transformation teams
- Informatics trainees and early-career analysts
Project - Healthcare Decision Intelligence Series
Using Data, Evidence, Evaluation and AI to Make Better Healthcare Decisions
Timeline: May 2026 to November 2026
Author and Contributors
Contributors & Reviewers
Nick Swart
Director of Health Economics
SWART Evaluation Limited
Sam Williamson
Deputy Director of Health Economics and Evaluation
NHS Central East ICB
Audience: Analysts and Decision Makers in healthcare systems
Endorsed by: 
Project Overview
The Healthcare Decision Intelligence Series is a practical, NHS-focused educational resource designed to improve understanding, interpretation and use of healthcare analytics across healthcare systems.
The series aims to bridge the gap between:
healthcare analysts and decision-makers, operational leaders, clinicians and service managers.
Rather than focusing purely on statistical methods or software training, the series focuses on:
- analytical reasoning
- interpretation
- operational context
- fair comparison
- healthcare systems thinking
- communicating uncertainty and complexity appropriately.
The resource will be developed as an openly accessible digital book and supporting learning materials using GitHub Pages and Quarto.
Why This Project Matters
Healthcare organisations increasingly rely on data to:
- prioritise resources
- benchmark services
- monitor inequalities
- redesign pathways
- support operational and strategic decisions.
However:
- healthcare metrics are often misunderstood or oversimplified
- comparisons between organisations are not always fair
- dashboards can unintentionally hide operational reality
- analytical outputs may lack sufficient context or interpretation.
This project aims to improve:
- healthcare decision intelligence
- analytical questioning
- interpretation of metrics
- understanding of variation and fairness
- communication between analysts and decision-makers.
The long-term goal is to support:
better questions, better interpretation and better decision-making across healthcare systems.
Project Objectives
The project aims to:
- Develop a structured NHS-focused healthcare data literacy series
- Improve understanding of common healthcare analytics concepts and pitfalls
- Support fairer interpretation of healthcare metrics and benchmarking
- Help decision-makers understand operational context behind the numbers
- Improve communication between analysts and healthcare leaders
- Create practical examples using realistic NHS operational scenarios
- Provide openly accessible educational resources for healthcare systems
- Support analytical maturity across NHS organisations
Core Principles
The series will aim to be:
- Operationally grounded
- Accessible to non-statisticians
- NHS-relevant
- Visually engaging
- Conceptually rigorous
- Openly accessible
- Practical rather than academic
- Focused on interpretation and reasoning rather than software training
Modules:
Part 1 — How do I interpret healthcare data correctly?
- Counts vs Rates
- What number should I be comparing?
- Mean vs Median
- What is the best way to summarise the data?
- Correlation vs Causation
- Does this relationship actually imply cause and effect?
- Variation & Distributions
- Is this difference meaningful or simply normal variation?
- Standardisation & Fair Comparison
- Are these populations really comparable?
- How to Read a Dashboard
- What story is the dashboard actually telling me?
- Theory of Change & Logic Models
- Connecting Interventions to Outcomes
- Evaluating Interventions & Schemes (Thinking About Evidence)
- How should I think about whether something worked?
- Evaluating Interventions in Practice (Methods)
- How do I evaluate it properly?
- Risk, Probability & Uncertainty
- How certain can I be about this evidence?
- Common Analytical Traps & Data Fallacies
- What mistakes could lead me to the wrong conclusion?
Part 2 — How do we understand, improve and evaluate complex healthcare systems?
- Demand, Capacity & Operational Flow ⭐
- Why are patients waiting, and where is the bottleneck?
- Data Quality, Assurance & Trustworthy Data
- Can we trust the data before making decisions?
- Designing Better Healthcare Pathways ⭐
- How do we redesign, implement and evaluate better pathways?
- Health Economics & Value in Healthcare Decision-Making ⭐
- Is the improvement worth the investment?
- Methods for Applying Health Economics to Healthcare Decisions
- Comparing options, costs, outcomes and value
- Evaluating Multiple Interventions in Complex Healthcare Systems ⭐
- Understanding What Made the Difference
- Population Health Management & Population Segmentation
- Who should we intervene with, and why?
- Testing Decisions Before We Make Them ⭐
- Using scenarios and simulation to explore possible consequences
- Bringing Evidence Together ⭐
- How do we combine different forms of evidence into a coherent picture?
- From Evidence to Decision ⭐⭐
- Making transparent decisions when there are trade-offs and uncertainty
The project will include:
- Publicly accessible Quarto/GitHub Pages digital book
- Educational modules with practical NHS examples
- Visual infographics and diagrams
- Operational interpretation examples
- Decision-maker reflection questions
- Worked healthcare scenarios
- Supporting presentation materials
- Executive summaries and quick-reference guides
- Optional downloadable PDF/Word versions
What Is Out Of Scope
The project will not focus on:
- Software-specific technical training
- Coding tutorials
- Advanced statistical derivations
- NHS data governance policy documentation
- Building analytical platforms or infrastructure
- Formal accreditation or certification
- Organisation-specific implementation programmes
Target Audience
Secondary audiences may include:
- Local authorities
- Healthcare consultancies
- Academic/public health programmes
- Wider public sector analytical communities
Delivery Approach
Platform
GitHub Pages Quarto Book framework PDF and Word export options
Style
Visual and example-led Operationally realistic Plain English where possible Progressive conceptual layering
Project Phases
Phase 1 — Foundation Development
Months 1–2
- Establish project structure
- Develop initial visual identity and formatting
- Create foundational modules
- Build GitHub/Quarto publishing workflow
Phase 2 — Core Content Development
Months 3–5
- Develop additional modules
- Expand operational examples and visual content
- Gather peer feedback from NHS analytical community
- Refine educational structure and readability
Phase 3 — External Review & Refinement
Months 5–6
Seek structured feedback from: - NHS CDAO network - AphA members - healthcare analysts - operational leaders and strategic commissioners - Improve content based on feedback - Refine visuals and accessibility
Phase 4 — Publication & Dissemination
Month 6 onwards
- Public launch of the series
- Social and professional dissemination
- Presentation sessions/webinars
- Ongoing iterative development
- Success Measures
Success indicators may include:
- Positive feedback from NHS analysts and operational leaders
- Adoption within NHS analytical communities
- Use in analyst onboarding or data literacy initiatives
- Engagement through GitHub Pages analytics
- Requests for reuse/adaptation across organisations
- AphA and/or NHS analytical community endorsement
- Evidence of improved analytical questioning and interpretation
Key Assumptions
- Contributors can support development alongside operational roles
- NHS analytical communities will engage constructively with feedback
- Open-access publication is supported
- The series will evolve iteratively over time
Long-Term Vision
The long-term ambition is to create:
- a widely used NHS healthcare data literacy resource
- a practical guide to analytical interpretation in healthcare
- an openly accessible educational reference for healthcare analytics and operational decision-making.
The series may later evolve into:
- a formally published digital book
- workshop/training materials
- conference presentations
- broader healthcare analytical capability resources
Guiding Philosophy
“Good healthcare analytics is not just about producing numbers. It is about helping people interpret, question and use those numbers responsibly within complex healthcare systems.”
Sign-Off:
Name: Chandan Kaur
A passionate advocate of decision intelligence and decision analytics in healthcare
Beyond the Series – Advanced Analytics, AI & the Future of Healthcare Decision-Making
Parts 1 and 2 of Healthcare Decision Intelligence provide a freely available practical foundation for using data, evidence, evaluation, health economics, population insight and analytical methods to support better healthcare decisions.
The next stage will explore how advanced analytics and artificial intelligence are changing healthcare decision-making.
Rather than extending the online series, this material will be developed as a separate published book.
The book will build on the principles introduced in Healthcare Decision Intelligence and explore how healthcare organisations can use advanced analytics and AI responsibly, transparently and effectively — while recognising that technology should support, rather than replace, professional judgement and human decision-making.
Planned themes include:
- Predictive Modelling & Risk Stratification
- Explainable AI in Healthcare
- From Business Intelligence to Augmented Analytics
- AI Assistants and Agentic AI
- Evaluating AI-Supported Decision-Making
- Responsible AI & AI Governance
- Bias, Fairness & Health Inequalities
- Implementing and Scaling AI in Healthcare Organisations
- The Changing Role of Analysts and Decision-Makers
- The Future of Healthcare Decision Intelligence
Further details about the book will be announced here in due course.