Methodology¶
How the Framework Was Developed¶
The HDRL Framework was developed through a combination of systematic evidence synthesis, first-principles analysis, and multi-stakeholder validation. This page documents the approach, evidence base, and key design decisions.
Multi-Model AI Synthesis¶
A novel methodology was employed in which three frontier AI models (Claude, Gemini, and ChatGPT) independently conducted systematic reviews against identical research specifications. Outputs were then triangulated to assess confidence:
| Confidence Level | Definition | Count |
|---|---|---|
| High (concordance) | Identified by all three models | 24 frameworks |
| Medium (partial consensus) | Identified by two models | 18 frameworks |
| Verified unique | From single model, verified against primary sources | 14 frameworks |
This approach enabled comprehensive coverage whilst providing explicit confidence assessment and reducing the risk of any single model's biases shaping the framework.
Central finding
No existing framework comprehensively addresses health data research readiness. While mature frameworks exist for general data management, health information systems, and FAIR data practices, none integrates these dimensions for assessing readiness to participate in multi-site health data research infrastructure.
56-Framework Evidence Base¶
The systematic review identified 56 frameworks across seven domains:
| Domain | Frameworks | Key Exemplars |
|---|---|---|
| Data governance & management | 8 | CMMI-DMM, DAMA-DMBOK, EDM Council DCAM |
| Health-specific | 12 | OECD Health Data Governance, WHO SCORE, HIMSS EMRAM |
| Research infrastructure | 9 | EOSC Readiness, RDA FAIR DMM, NIH DMSP |
| Digital government | 7 | UN EGDI, World Bank GTMI, OECD DGI |
| Data quality & FAIR | 8 | RDA FAIR Data Maturity Model, WHO DQR, ISO 8000 |
| Workforce capacity | 6 | ESSENCE Framework (TDR/WHO), NHS NCF, WHO HWF Assessment |
| National strategies | 6 | Various national data strategies with maturity components |
Each framework was analysed across eleven extraction elements including purpose, domain structure, indicator design, measurement approach, level architecture, validation evidence, strengths, limitations, and relevance to health data research.
Three Research Tasks¶
Task 1: Systematic Framework Review¶
Identification and comparative analysis of existing maturity and readiness frameworks. This established the evidence base for domain selection, indicator design, and level architecture.
Task 2: Jurisdictional Evidence Review¶
Analysis of health data research ecosystems across:
- High-income exemplars: UK, Nordic countries, Singapore, Australia, Canada, Estonia
- Middle-income contexts: India, Jordan, Brazil
- Lower-resource settings: Various WHO member states
This ensured the framework reflects real-world variation rather than idealised models.
Task 3: First-Principles Derivation¶
Independent derivation — without reference to existing frameworks — of what "readiness for health data research" fundamentally requires:
- Necessary conditions: Data existence, accessibility, legal basis, ethical authorisation, minimum sustainability
- Enabling conditions: Standardised formats, linkage capability, trained workforce, governance mechanisms, public transparency
- Excellence conditions: Real-time availability, population-scale coverage, federated analysis, automated quality monitoring
Architecture Decisions¶
The framework architecture reflects strong consensus across source frameworks:
Five Maturity Levels¶
Level descriptors align with CMMI nomenclature: Initial (L1), Developing (L2), Defined (L3), Managed (L4), Optimising (L5). The use of "Optimising" rather than "Optimised" reflects maturity as a continuous process.
Eight Domains¶
High-relevance frameworks operate with 6–11 domains. HDRL's eight domains fall within this range, reflecting synthesis across the capability clusters identified through the evidence base and first-principles analysis.
Core/Enhancement Classification¶
Adapted from the RDA FAIR Data Maturity Model's priority tiers (essential, important, useful). HDRL uses Core (essential for baseline participation) and Enhancement (good practice for mature organisations).
Foundational Requirements¶
Five indicators that represent non-negotiable safety and governance prerequisites. These are drawn from the first-principles "necessary conditions" analysis and prevent the median-based scoring from hiding critical gaps.
Key Sources¶
| Source | Contribution to HDRL |
|---|---|
| RDA FAIR Data Maturity Model (2020) | Indicator classification and priority tiers |
| HIMSS EMRAM | Validation of staged maturity assessment in healthcare |
| EDM Council DCAM | Capability-based assessment and hierarchical indicator structure |
| ESSENCE Framework (TDR/WHO, 2016) | Multi-level assessment through System/Service/Both tags |
| OECD Health Data Governance (2016/2022) | Normative foundation for Domains C and E |
| Five Safes Framework (Ritchie, ONS) | Governance model operationalised across Domains C and H |
| Building Trusted Research Environments (UK HDRA, 2021) | TRE operational requirements informing Domain H |
| SATRE Specification (DARE UK, 2023) | TRE capability tiers informing H.1.1 |
| CMMI | Maturity level nomenclature and progression logic |