The AI Infrastructure Behind Modern Clinical Trials

AI Mini Report Blog

Clinical trial planning remains one of the most fragmented and manual processes in drug development. Sponsors often move between disconnected systems, static datasets, spreadsheets, and email threads to identify sites, assess feasibility, and validate assumptions. Even as artificial intelligence becomes a priority across the industry, many platforms treat AI as an add-on feature, restrict it, license it separately, or limit how sponsors can use the underlying data.

H1 takes a fundamentally different approach. AI is not layered on top of the platform; it is built into the core engine that powers feasibility, search, and data access. And just as importantly, H1’s data is structured to support both embedded intelligence within the platform and sponsors’ own internal AI initiatives.

This embedded, open model creates a new standard for how sponsors identify sites, evaluate performance, and design smarter clinical trial strategies.

AI‑Driven, End‑to‑End Feasibility

H1’s Site Network capability applies AI across the site selection workflow to streamline and remove barriers that have historically slowed study start-up. It analyzes a sponsor’s protocol criteria and relevant historical data to generate a targeted shortlist of sites, then manages feasibility questionnaire distribution and response collection within the same platform, keeping identification, outreach, and evaluation in one continuous workflow.

  • Protocol‑aware site recommendations: AI models interpret a sponsor’s protocol (including key eligibility criteria and target populations) and match it to data within H1’s Site Universe and surface sites that are already seeing relevant patients and running similar trials. This eliminates the need for manual filtering and time-intensive data review to identify qualified sites.
  • Centralized site feasibility data: H1 consolidates site-reported capabilities and prior feasibility responses into one structured, continuously updated profile for each site.
  • Direct sponsor–site connection: Once candidate sites are selected, the platform pre-populates existing site data, sends feasibility questionnaires, standardizes responses, and captures all information directly within H1, eliminating the need to rely on spreadsheets, email chains, or other disconnected tools.

H1’s Site Network is the only solution in the industry that tackles sponsor and site challenges head-on, saving time, money and effort.

AI‑Enhanced Search and Discovery

H1 also embeds AI into its search and navigation experience, so that all users, from novices to experts, can quickly move from broad questions to precise answers.​

  • Natural‑language and intent‑based search: Users can search in the way they think about studies (e.g., by indication, line of therapy, prior trial experience), and AI interprets the intent, rather than manually building complex filters or queries.​
  • Guided workflows, not just “search bars”: AI‑powered filters and recommendations are woven into workflows like shortlisting sites, evaluating experience, and assessing diversity potential, supporting consistent decisions across teams.​

Because these capabilities are embedded in the user experience, sponsors do not need separate tooling or internal data‑science resources to get insights from the platform.

Open Data for Internal AI/ML and Data Lakes

The third way H1 differentiates its AI strategy is by enabling sponsors to use H1 data within their own data lakes and internal AI/ML programs, rather than locking data inside a closed system or charging extra for access.​

  • Rich, AI‑ready data sets: H1’s data model can include clinical trial activity, site and PI profiles, patient representation data, real‑world claims, and performance metrics, all linked at the entity level. These interconnected data streams are well‑suited for building internal models, enrollment‑risk forecasts, and portfolio‑planning tools.​
  • Integration into data lakes and analytics stacks: Sponsors can bring H1 data into their own environments, such as enterprise data lakes or analytics platforms, and combine it with operational data to power custom AI/ML use cases.​
  • Supportive, not restrictive, licensing: The philosophy is that H1’s data should foster and encourage AI, both in‑platform and in partnership with customers, instead of imposing contractual limits or add‑on fees when sponsors want to run their own models.

Together, these three pillars help sponsors move from static lists and manual feasibility surveys to continuously optimized, data‑driven trial strategies powered by both H1’s embedded intelligence and their own internal AI initiatives.

This is only the beginning of H1’s AI roadmap, with new capabilities planned to further streamline feasibility, deepen site and PI insights, and enhance predictive decision-making over time. Through a deep, personalized partnership model, H1 co‑develops tools and workflows with sponsors so that future innovations directly address real‑world clinical and operational challenges.

To learn more about H1’s AI capabilities to speed up trial planning and feasibility, request a demo today.