AI + Human Expertise: Women’s Health Profit Equation

Women's health companies face a critical challenge: 71% of femtech startups fail to reach Series A, forced into unsustainable direct-to-consumer models because they lack reimbursement pathways. While AI promises to identify patterns in population health data and generate evidence at scale, pattern recognition alone doesn't secure payer approval. The real profit equation requires combining AI's analytical capabilities with Health Economics and Outcomes Research (HEOR) expertise to build the pharmacoeconomic cases that win reimbursement—whether for pharmaceuticals, diagnostics, digital therapeutics, or care delivery models.

This profitability equation becomes particularly powerful in women's health, where systemic evidence gaps create opportunity. With only 1% of healthcare R&D funding directed toward female-specific conditions and minimal comparative effectiveness data, AI can help generate the population health insights and real-world evidence that never existed—but only if translated into budget impact models, cost-effectiveness analyses, and payer value demonstrations that secure formulary placement, CPT codes, and health plan contracts.

Join us to discuss:

• How can women's health companies use AI to generate population health evidence and real-world data for historically underserved conditions, while ensuring HEOR expertise translates findings into reimbursement pathways that payers will actually fund across pharmaceuticals, diagnostics, and digital health?
• What HEOR capabilities must women's health startups build from day one—budget impact models, cost-effectiveness analyses, outcomes-based evidence—to avoid the direct-to-consumer trap and achieve sustainable payer reimbursement?
• How should investors evaluate women's health companies differently, prioritizing reimbursement strategy, pharmacoeconomic ROI, and payer evidence requirements rather than user engagement metrics?