The growing acceptance of real-world data (RWD) and real-world evidence (RWE) by both FDA and CMS represents an unprecedented opportunity for wound care product manufacturers to drastically reduce the cost and timeline of evidence generation for FDA approvals and coverage decisions compared to traditional randomized controlled trials (RCTs). FDA has defined their requirements for the use of RWD for primary evidence in regulatory decisions through recently finalized guidance. CMS has been working with FDA to establish CMS’ requirements for the use of RWD in coverage decision-making, resulting in a recently released proposed guidance for study protocols using RWD for national coverage determination (NCD) coverage with evidence development (CED). Although this guidance is not exhaustive and CMS’ requirements continue to evolve, both agencies are setting a high bar for scientific rigor and emphasize that the reliability of RWD is paramount.
Meeting these stringent data reliability requirements is not trivial however. Real-world patient data from sources like electronic health records (EHR) and claims is complex, messy, and highly varied across sources – orders of magnitude more so than RCT data – making it extremely challenging to derive credible and robust evidence for regulatory decision-making. Sponsors’ approaches to RWE have often fallen short of these regulator expectations, leading to a lack of consideration in both FDA submissions and CMS coverage decisions. For example, CMS’ recent local coverage determination (LCD) for skin substitutes for treatment of diabetic foot ulcers (DFU) and venous leg ulcers (VLU) notes that many published RWE studies for wound care products do not demonstrate adequate data reliability and have a high risk of bias, and as a result do not provide sufficient evidence of product efficacy to justify coverage.
Droice Labs has been addressing precisely these challenges with AI middleware technology specifically designed to generate reliable RWD at scale from diverse and messy RWD sources. Droice’s AI middlewares, Hawk and SuperLineage, have been discussed with FDA and meet FDA’s requirements for RWD reliability for use in regulatory decision-making, see this press release.
Droice has been generating regulatory RWD across diverse therapeutic areas in partnership with multiple big pharma and biotech companies. In wound care specifically, Droice Labs has deep domain expertise and is supporting several manufacturers with end-to-end evidence generation that meets FDA and CMS requirements at a fraction of the cost and time of traditional RCTs. For example, Droice’s AI middlewares are being used to rapidly aggregate and harmonize RWD from multiple sources into reliable study data to efficiently produce evidence for CMS for several on-market wound care products across multiple wound types. Droice AI middleware is also being used to generate standard of care external control arms to reduce required sample size in wound care RCTs for FDA submissions.