From Priority Questions to Policy Action: Recommendations to Advance Women’s Health Innovation in Europe

How can priority questions on women’s health move beyond identification to shape European research agendas, funding instruments, and data infrastructures?

On 5 February, The Governance Lab and the Centre for European Policy Studies (CEPS) convened researchers, policymakers, technologists, funders, and advocates for a joint workshop focused on Building a Research Agenda for Women’s Health Innovation. The workshop marked the next step in the 100 Questions Initiative: shifting from identifying priorities to operationalising and institutionalising them within the European context.

Ten Takeaways for EU Stakeholders and Policymakers

1. Institutionalise Question-Driven Research in EU Women’s Health R&I. 

Question-driven science remains underutilised in EU research agenda-setting. Yet the questions institutions choose to pose determine what gets measured, financed, and developed. Questions that are specific and sensitive to women’s health and innovation must therefore be strategically embedded upstream within EU research and policy frameworks, not treated as a checkbox exercise or an afterthought.

This could be operationalised through existing instruments, such as Horizon Europe, by requiring proposals to include a clearly articulated question statement outlining the core research problem and its relevance. To support continuity, the EU could establish a publicly accessible and regularly updated European “Question Catalogue” for Women’s Health, to inform funding calls, policy development, and research design.

In parallel, EU bodies and Member States could convene recurring, time-bound Women’s Health Question Labs (QLabs) to:

• Scope priority problems,

• refine and prioritise research questions, and

• translate them into operational sub-questions linked to data, methods, partners, ethics, and expected impact.

Operational effect: Making agenda-setting explicit and reviewable would reduce unexamined priority capture, align research questions with data and impact pathways, and provide clearer signals to researchers and investors—ensuring data generation responds to defined societal needs rather than opportunity alone.

2. Position Women’s Health Innovation as a Driver of EU Competitiveness

Recognising and strategically investing in women’s health innovation presents a clear opportunity for the European Union to strengthen its global competitive advantage and demonstrate leadership in a critical and growing area of unmet need. By prioritising this field, the EU can consolidate its scientific expertise, enhance translational capacity, and position itself as a global leader in gender-responsive health research and innovation.

Despite over €2 billion invested across more than 1,000 projects under Horizon 2020 and Horizon Europe, significant gaps remain. Even in well-funded areas such as cardiovascular disease, sex- and gender-specific differences in diagnosis and treatment remain underexamined. Meanwhile, mental health and autoimmune diseases—both disproportionately affecting women—continue to receive comparatively limited investment.

Operational effect: Framing women’s health as a competitiveness priority would better align political, financial, and private-sector incentives, directing investment toward high-impact innovation while strengthening Europe’s global leadership.

3. Embed Women’s Health in EU Research Instruments and the Multiannual Financial Framework (MFF): 

Women’s health should be explicitly embedded as a priority across EU research and innovation instruments, including the forthcoming Multiannual Financial Framework (MFF). This would enable the coordinated development of dedicated funding calls and work programmes—designed in consultation with civil society and relevant stakeholders—to address persistent knowledge gaps and advance sex- and gender-responsive research.

Prioritisation must be matched with ring-fenced or clearly identifiable funding. Existing instruments such as Horizon Europe could integrate targeted calls in future work programmes, while the European Innovation Council could launch dedicated women’s health innovation challenges to mobilise the broader ecosystem.

Operational effect: Embedding women’s health across funding instruments would provide strategic clarity and long-term visibility, strengthen accountability, improve monitoring of investments and outcomes, and reduce fragmentation. Leveraging existing EU mechanisms through targeted calls and innovation challenges would better align financial incentives with women’s health priorities and accelerate translation into impact.

4. Establish a Women’s Health “Fair AI” Regulatory Standard. 

The EU should establish a Women’s Health “Fair AI” regulatory and funding standard for digital health tools. AI-enabled solutions supported through EU funding or public procurement should transparently report the sex and gender composition of training datasets, demonstrate performance across demographic groups, and clarify whether they address a recognised women’s health gap.

Operational effect: This would turn concerns about biased data into enforceable requirements, reducing systemic bias in algorithms and incentivising the development of representative, equitable tools—strengthening trust and the quality of AI-driven healthcare innovation.

5. Redesign Funding Conditions to Advance Women’s Health

EU health research funding should embed mandatory analytical and reporting requirements across all domains—basic, clinical, implementation, and social science—requiring the integration and reporting of sex- and gender-disaggregated data. Where such analysis is not feasible, applicants should provide a clear and justified explanation.

Operational effect: Making this a standard funding condition would drive a necessary cultural shift, reduce bias in research design and interpretation, and ensure future evidence more accurately reflects sex- and gender-specific health outcomes.

6. Establish EU-Wide KPIs for Women’s Health

The EU should establish policy-relevant key performance indicators (KPIs) to monitor and advance progress in women’s health. Drawing on priority questions, these indicators should secure cross-sectoral buy-in—across health, economic, social, and resilience agendas—and be applied consistently across funding and evaluation frameworks.

KPIs could include measurable reductions in the healthy life-years and quality-of-life gaps for women, alongside other sex- and gender-sensitive outcomes. Linking these indicators to EU funding calls and monitoring systems would strengthen accountability and long-term progress. Framing women’s health as societal infrastructure—integral to competitiveness, productivity, and resilience—would reinforce political and strategic alignment.

Operational effect: This would shift evaluation from “counting initiatives” to measuring real change over time, enabling longitudinal monitoring and clearer links between research investment and societal outcomes.

7. Align Investment with Recognised Gaps and Shared Metrics

Investment should align with clearly defined gaps and shared performance metrics. As noted by Deepali from UNFPA, “capital follows clarity”: outcome-oriented indicators reduce fragmentation and strengthen the investability of women’s health innovation. Establishing common metrics across research, policy, and financing frameworks would increase predictability for funders and innovators.

Operational effect: Clearer metrics would send stronger signals to investors, policymakers, and researchers, directing capital toward demonstrable impact areas.

8. Address Structural Data Gaps Across the Female Life Course

Persistent data gaps remain across under-researched conditions, key life-course transitions such as puberty and menopause, and the interaction between social determinants and health outcomes. A coordinated EU data strategy is needed to ensure consistent collection, harmonisation, and analysis of life-course and sex-disaggregated data.

Operational effect: This would strengthen transparency and comparability across Member States, improve accountability, and ensure that expanded data access translates into measurable progress aligned with women’s health priorities.

9. Introduce Transparency Requirements for Health Data Access Bodies (HDABs)

Health Data Access Bodies (HDABs) should publish, using a standardised EU template:
(i) all approved data access requests by topic,
(ii) the datasets utilised,
(iii) links to resulting outputs, and
(iv) an indicator reflecting the proportion of approvals addressing women’s health questions, including specification of thematic focus.

Operational effect: This would create a measurable feedback loop to assess whether data access mechanisms are advancing women’s health research. Greater transparency and comparability across Member States would strengthen accountability and ensure expanded data access translates into tangible progress.

10. Leverage the European Health Data Space (EHDS)

The EHDS should be strategically leveraged to advance women’s health priorities. Its metadata standards, interoperability requirements, and data catalogues should align with clearly defined policy and research questions, particularly in under-researched conditions and key life-course transitions.

As part of the roll-out, Member States should prioritise women’s health-relevant datasets—across both reproductive and non-reproductive domains—ensure standardised recording for interoperability, systematically tag datasets in a discoverable EU-level catalogue, and strengthen secure cross-border secondary use.

Operational effect: This would transform the EHDS into a practical accelerator for women’s health research, enabling fragmented national datasets to be pooled and analysed at scale, and converting dispersed data into a coherent European asset for evidence-based innovation and policy.

11. Require Sex- and Gender-Disaggregated Data

The consistent collection and reporting of sex- and age-disaggregated data should be embedded as a core requirement to reduce bias in AI applications, diagnostic tools, and health system analytics. By aligning cross-border data access with policy priorities, the EHDS can ensure interoperability advances not only data availability, but also relevance and equity in health research and innovation.

Operational effect: Making representativeness a structural requirement would disrupt persistent male-default assumptions in biomedical research and clinical tools. It would improve comparability across Member States, strengthen regulatory assessment and meta-analyses, and enhance the reliability of AI-driven and data-enabled health solutions—contributing to a more accurate and inclusive evidence base.

12. Partnerships and Synergies are Required to Advance Women's Health: 

Advancing women’s health requires coordinated partnerships across public funders, industry, AI developers, policymakers, academia, and civil society. Progress cannot be achieved through isolated action; it demands shared responsibility across the innovation ecosystem.

Strategic collaboration is particularly critical at the intersection of AI and women’s health. Engagement between technology developers, health experts, and patient advocates is essential to address data limitations, embedded bias, and gaps in model training that risk perpetuating inaccuracies in diagnostics and treatment tools.

Operational effect: Structured cross-sector collaboration would strengthen scientific rigour and societal relevance, ensuring women’s health innovation is equitable, evidence-based, and future-oriented.

Why This Matters Now

Europe is investing heavily in digital infrastructure, AI systems, and cross-border health data interoperability. Without deliberate priority-setting, these investments risk reproducing existing inequities.

By institutionalising high-quality questions, and developing a methodology to operationalize them, EU policymakers can ensure that women’s health innovation is guided by equity, clarity, and measurable impact.

The task is not simply to answer the Top 10 questions, but to embed them in how Europe governs research, data, and investment.