HHS Launches SURPASS and New Efforts to Accelerate Faster, Smarter Clinical Trials
Short summary
The U.S. Department of Health and Human Services announced SURPASS, a new program designed to cut clinical trial timelines, reduce costs, and shrink patient burden by using computational models, real-time data analysis, and automation. It's paired with three complementary projects to fix infrastructure problems like site capacity and fragmented data access.
- Current clinical trials take over a decade, cost up to $2 billion, and fail more than 90% of the time — SURPASS aims to compress timelines and lower costs through adaptive trial designs and continuous data analysis
- The program includes three technical areas: a "phaseless design engine" using digital twins to simulate outcomes before launch, a "continuous inference engine" for real-time trial adjustments, and automation tools to speed site activation and data handling
- Three companion projects tackle ecosystem barriers: STACK expands trial sites through AI-driven site activation, COMMONS builds national data infrastructure with privacy protections, and CINCH lets patients contribute and access their own real-world data
Full summary
HHS launched SURPASS, a major clinical trial modernization program designed to cut development timelines, reduce costs, and shrink patient burden. The current system is broken: drug development takes over a decade, costs $2 billion on average, and fails more than 90% of the time. SURPASS tackles this through three technical approaches—building a "phaseless design engine" that uses digital twins and predictive models to simulate outcomes before trials start, developing a "continuous inference engine" for real-time data analysis and faster decision-making, and creating an "agentic operations layer" to automate startup and operational tasks. The goal is to generate rigorous evidence with fewer participants and faster turnaround.
Three companion projects address the infrastructure gaps that SURPASS alone can't fix. STACK uses AI to activate clinical sites faster and convert research-naive locations into trial-capable facilities, expanding where trials can run. COMMONS builds a nationwide data infrastructure with privacy-by-design consent mechanisms, giving researchers access to regulatory-grade data needed for enrollment and predictive modeling. CINCH focuses on patients directly—letting them contribute real-world data, track their own care, and get faster connections to relevant trials. Together, these four initiatives target the operational chokepoints that currently slow development: fragmented data, limited site capacity, outdated trial designs, and patient navigation barriers.
The programs are designed to generate publicly available tools, standards, and frameworks that the entire U.S. clinical trial ecosystem can adopt. Rather than just fixing individual trials, ARPA-H is trying to build permanent infrastructure that sponsors, sites, regulators, and tech developers can use to run faster, more adaptive studies across many diseases. This sits within the broader Trump administration goal of reducing unnecessary delays in drug development and keeping U.S. biotechnology competitive globally.