Seen builds AI payment infrastructure to address the systemic underpayment and undervaluation of women's health services. The company tackles the financial and data gaps that lead to mispriced procedures, inadequate billing codes, and invisible patient populations. Their platform aims to unlock the stranded $1 trillion market opportunity by enabling evidence-based payment models.
Funding
Funding not disclosed
Founders
Product
Problem
Women's health services are chronically underpaid and undervalued in the U.S. healthcare system, with male-specific procedures receiving 25-75% higher facility payments than comparable female procedures. Additionally, 80% of urogynecologic procedures lack adequate billing codes, and a pervasive data gap leaves many women undiagnosed and invisible in medical and billing records, undermining care quality and innovation.
Solution
Seen provides AI-powered payment infrastructure designed specifically to correct financial and data inequities in women's healthcare. The platform uses evidence-based payment analytics to identify mispriced rates and benefits, rebuild deficient billing code structures, and surface undervalued services. By digitizing and enriching incomplete datasets, Seen helps providers achieve fair reimbursement while enabling health plans to accurately prioritize women's health services. The solution addresses the root causes of financial losses, deprioritized services, and weak data that perpetuate poor outcomes for women.
Target Audience
Primary customers are healthcare providers, health systems, and payers seeking to correct reimbursement inequities and unlock the under-monetized women's health market, along with digital health companies needing accurate sex-specific data for AI models.
Features
- AI-driven payment analytics that benchmark and correct gender-based reimbursement disparities across 300+ paired procedure categories
- Billing code modernization tools that address the 80% of urogynecologic procedures lacking adequate or specific codes
- Data enrichment pipeline that surfaces undiagnosed patient populations (80% of women with diagnosable conditions) from existing medical and billing records
- Evidence-based pricing models that enable providers and payers to properly value female-specific procedures and services
- Workflow integrations designed to close the AI invisibility cycle by feeding complete, sex-specific data into healthcare AI systems