Womens Health Investment Outlook 2026

Appendix A: Methodology To build a market-driven view of the women’s health innovation landscape for the Women’s Health Investment Index, a combination of structured data aggregation, natural language processing (NLP), generative AI tagging and expert consultation was used. This approach integrates quantitative rigour with qualitative validation, producing an evidence- based map of where capital is flowing – and where opportunity remains. Data sources and inclusion criteria A keyword-based search strategy was developed to capture company descriptions that explicitly referenced women’s health. Data was aggregated from PitchBook, CapitalIQ and Crunchbase, then processed through Quid, an NLP platform, to ensure consistency and depth of coverage. Search strategy The dataset includes funding events from 3 September 2020 to 3 September 2025. Keywords captured women’s health terms (e.g. woman* OR women*121 OR female AND disorder OR therapeutic OR therapy OR diagnosis OR cardiovascular, etc.), condition-specific terms (e.g. menstrual OR postpartum OR maternal OR breast cancer OR PCOS, etc.) and additional relevant keywords (e.g. femtech). The full search strategy is available by request. With the data pull spanning September 2020 to September 2025, only full years were included (2021–2024) in the data visualizations, where the funding is shown across years. Scope of financing events Companies were included if they had at least one financing or transaction event – defined as M&A, minority stake, private investment or public offering – between 2020 and 2025. The analysis focuses on external capital flows and therefore excludes internal R&D budgets, philanthropic grants and government funding, which operate through different channels. Inclusion and exclusion criteria Included were women’s health-specific companies, generalist organizations with material women’s health-specific products or clinical assets, and enabling categories configured for women’s health, such as diagnostics, devices and digital tools. Excluded were entities without a differentiated women’s health offering, including general hospitals, broad telehealth providers and large pharma and biotech players without women-focused assets. Consumer categories such as beauty, clothing or generic wellness and fitness were also excluded. Clustering and classification Market-driven clustering was conducted using Quid’s NLP algorithm, which organizes companies by linguistic similarity in their descriptions. This revealed natural clusters across both functional themes (such as diagnostics, navigation and benefits) and therapeutic areas (including oncology, reproductive health and maternal health). To refine accuracy, clusters were manually reviewed. Overly broad clusters (e.g. generic “digital health”) were subdivided, while very small ones were merged where fragmentation obscured meaningful patterns. A complementary generative AI-based tagging system, built in Python, classified companies into 16 predefined therapeutic areas and seven predetermined industry areas. This enabled cross- cluster comparisons and alignment with established healthcare categories, linking investment activity directly to disease burden and unmet need. Women’s Health Investment Outlook 35