Project - Healthcare Decision Intelligence Series

Using Data, Evidence, Evaluation and AI to Make Better Healthcare Decisions

Timeline: May 2026 to December 2026

Author: Chandan Kaur

Audience: Analysts and Decision Makers in healthcare systems

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
Understanding What Made the Difference ⭐
- Evaluating Multiple Interventions in Complex Healthcare Systems
Population Health Management & Population Segmentation
- Who should we intervene with, and why?
Simulation & Scenario Modelling
- What is likely to happen before we make changes?
Bringing Evidence Together ⭐
- How do we combine different forms of evidence into a coherent picture?
Making Better Healthcare Decisions ⭐
- How do we turn evidence into robust, transparent decisions when there are trade-offs, competing priorities and uncertainty?

Part 3 – Advanced Analytics, AI & the Future of Healthcare Decision-Making (to be confirmed)

Predictive Modelling & Risk Stratification
Explainable AI in Healthcare
From Business Intelligence to Augmented Analytics
Responsible AI & AI Governance
Additional topics under consideration

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

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

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

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