Clinical development now takes a median of 10 years from the start of Phase I to approval, according to the IQVIA Institute’s Global R&D Trends 2026 report. Every day of that timeline carries a price. A 2024 Tufts CSDD white paper estimates that a single day of delay costs sponsors about $800,000 in unrealized drug sales, on top of direct trial costs that average roughly $40,000 per day across Phase II and III. Cutting even a few weeks from a program protects revenue and preserves more of the exclusivity window sponsors depend on to recoup their R&D investment.
Much of that delay traces back to data access. Sponsors need patient data from many institutions to design feasible protocols, find eligible patients, and build comparator cohorts, yet hospitals rarely let that data leave their firewalls. A sponsor's own data rarely sits in one place either, since clinical and real-world data assets tend to be scattered across functions and systems.
Federated computing resolves the conflict by bringing code to the data. This guide explains how harmonization, federated analytics, federated learning, and AI agents each fit into the trial lifecycle, and how sponsors can run them on the Rhino Federated Computing Platform (Rhino FCP) without moving raw patient data. Rhino works with 9 of the top 20 pharmaceutical companies, including Eli Lilly, and provides the infrastructure behind the FAITE Consortium of whose members include AbbVie, Amgen, AstraZeneca, Johnson & Johnson, and UCB.
Why Are Clinical Trials So Expensive and Time-Consuming?
Trial costs scale with time. Every month spent recruiting patients, monitoring sites, and managing data adds direct costs and pushes back the day a therapy reaches the market. Those costs climb in late-stage work, reaching nearly $56,000 per day in Phase III, according to the same Tufts white paper.
Protocol amendments show how quickly those months add up. Three out of four protocols now require at least one substantial amendment, averaging 3.3 amendments each, according to a 2024 Tufts CSDD study published in Therapeutic Innovation & Regulatory Science. A single Phase III amendment can add nearly three months and up to $1 million in unplanned direct costs. Tufts research has also found that recruitment difficulties rank among the top causes of amendments and that many changes involve patient eligibility criteria. Both problems point to the same gap, since sponsors design trials with limited visibility into the patients each site actually treats.
How Does Federated Harmonization Get Data Ready for Trials?
Every use case in this guide depends on data that looks the same at every site. Hospitals record diagnoses, labs, and medications in different systems, codes, and formats, so each site's records need to be mapped to a shared standard before a model or query can run across ten of them. Real-world evidence work typically runs on OMOP, data exchange on FHIR, and regulatory submissions on CDISC SDTM and ADaM.
Real-world evidence platforms have traditionally handled this by pooling data and harmonizing it centrally. Rhino Data Activation, built on the Rhino FCP, takes the opposite approach. The Rhino Data Harmonization Engine maps each site's data to standards such as OMOP and FHIR inside that institution, so standardized data stays where it started and each site keeps control of it.
The speed difference shows up in practice. A health system ranked among Newsweek's top 10 worldwide harmonized more than 600 million records on the Rhino FCP and cut the time to map each FHIR profile from two to three weeks down to one to two days. For trial sponsors, faster harmonization means a site becomes usable for feasibility, screening, and analysis sooner.
Trial work also raises the bar on traceability. Any pipeline that feeds a regulatory submission needs audit trails and data provenance that meet Good Clinical Practice (GCP) and 21 CFR Part 11 expectations, so logging every transformation at the source matters as much as the mapping itself.
How Does a Federated Approach Apply to Clinical Trials?
Once data is harmonized, federated approaches apply across the trial lifecycle. Rhino Federated Intelligence, built on the Rhino FCP, supports the multi-site work below. A sponsor, or the CRO managing its feasibility and site work, runs each analysis while every hospital decides what code runs on its data and what leaves its environment.
- Protocol Feasibility. A sponsor or its CRO tests draft eligibility criteria against real patient populations at candidate hospitals before finalizing a protocol. Each site returns only counts or aggregates, which shows how many patients it could enroll without exposing patient-level records.
- Patient Screening. Eligibility models run on each site's EHR and clinical data, and the site's own study team reviews any patient-level matches and decides whether to contact those patients. The sponsor sees only counts and aggregate results.
- Broader Recruitment. Predictive models running at participating sites can flag likely candidates early and give clinical operations teams time to act. Because the platform places a light footprint on each hospital, sponsors can also extend feasibility and pre-screening to community and regional health systems that rarely host trials. Those hospitals can refer eligible patients to a nearby active site, and the data they surface helps sponsors decide where to open new sites or add decentralized trial elements.
- External Control Arms. Sponsors can identify and characterize comparator cohorts from real-world data across multiple hospitals while each site keeps control of its records. FDA's draft guidance on externally controlled trials describes how EHR and other real-world data can serve as a control arm and what sponsors must address to make those comparisons credible.
Recruitment reach matters more than it looks. Trial sites cluster in urban centers, yet about one in five people in the US lived in a rural area as of the 2020 Census. Access appears to be the real barrier, since a SWOG analysis of nearly 37,000 cancer trial participants found that rural and urban patients had similar survival outcomes once enrolled. Reaching those patients also supports representative enrollment, which Congress wrote into law through Diversity Action Plan requirements for Phase III and other pivotal trials. Those requirements take effect once FDA finalizes its guidance.
All four use cases target a leading cause of trial delays, which is patient data that sponsors need but can't move.
What About the Data Sponsors Already Hold?
Hospital data is only half the problem. Inside most sponsors, the data that could sharpen a protocol is spread across clinical development, RWE, translational research, and regional affiliates, held in separate systems and often under different rules. Historical trial data may carry consent limits, licensed real-world data usually comes with contract terms that bar copying it or combining it with other datasets, and data held in the EU or other regions may not be allowed to leave its jurisdiction.
Rhino Data Activation harmonizes these assets where they sit, mapping each source to shared standards without building another central copy. Private Agentic AI then lets teams put AI to work on the data they can’t move, or can’t expose. Agents and models query across functions, regions, and licensed sources under governance that each data owner controls, so a feasibility question that once took weeks of data requests can draw on everything the organization already holds.
How Can AI Agents Work With Trial Data Without Exposing It?
AI agents can already turn a draft list of inclusion and exclusion criteria into structured queries in minutes. The harder problem is letting them run those queries against sensitive patient data that the organization can't move or expose.
Private Agentic AI, built on the Rhino FCP, gives sponsors and CROs a governed way to do that. An agent, whether built in-house or brought from a third-party vendor, connects through Rhino's MCP integration and skills and runs its queries inside each site's environment. The agent gets back the counts and aggregate results it needs, and the underlying records never leave the site. Because the Rhino FCP works across agent frameworks and tech stacks, teams can keep using the tools they already have.
How Does Federated Data Access Protect Patient Data While Enabling Larger Datasets?
Federated learning keeps patient data inside each institution. Models travel to the data, train locally, and send back only model updates or aggregate results. Raw records never leave the hospital, so no central copy of raw records exists to breach.
That design also shrinks the ask for hospital information security teams, who review software running inside their own environment under their own access controls rather than a request to export patient records. A smaller ask makes it easier to bring more sites into a study, and a larger site pool gives models broader and more representative patient populations to learn from, all without a central repository for anyone to build, secure, and maintain.
What Questions Do Sponsors Ask Before Adopting Federated Data Access?
The first question is usually how this fits alongside the cohort-discovery and real-world data tools sponsors already use. Research networks and EHR-based datasets help sponsors size patient populations across participating institutions, and real-world data providers offer curated datasets and feasibility services. Rhino complements these tools by running a sponsor’s own code, models, and agents on data at the institutions it works with, under each site’s control. Rhino Data Activation harmonizes that data at the source, so the same governed setup carries from feasibility to screening to analysis, study after study.
One distinction matters here: Rhino is not a data network or a data broker. We don’t sell access to patient data or pool it into a shared dataset. The Rhino FCP is infrastructure that sponsors and their research partners deploy, so a sponsor runs studies with the hospitals it already works with, or brings new ones on, and each institution installs the platform under its own governance. Many of the hospitals sponsors want most already run Rhino, which shortens that setup.
The next questions depend on who's asking.
- IT and Data Leaders. Each study runs without a central repository to build, secure, and maintain, and one governed platform serves every study and site that follows. Security reviews also move faster when no patient data leaves the hospital.
- AI Leaders. Agents and models can work on data the organization can't move or expose, with governance built into the platform instead of renegotiated for every project.
- Clinical Development, Operations, RWE, and Feasibility Leaders. Testing eligibility criteria against real site populations reduces the risk of eligibility-driven amendments, and a broader site pool supports faster, more representative enrollment.
For a sense of scale, consider a single avoided Phase III amendment. Based on the figures above, that one change would save up to $1 million in direct costs and close to three months of schedule, and each of those days carries the revenue cost of delay described at the top of this guide. The example is illustrative and doesn't reflect a specific Rhino deployment.
Sponsors who settle these questions early spend less time negotiating data access and more time running their trials.
Bring Your Next Trial to the Data
The sponsors that move fastest in the coming years will be the ones that solve data access first. Feasibility, recruitment, and comparator cohorts all depend on patient data that hospitals can't share, and the Rhino FCP lets sponsors bring code to that data where it already lives.
The Rhino FCP runs in 14 of the top 20 Newsweek Best Smart Hospitals, so many of the research sites sponsors want most already know how federated projects work.
If you lead clinical development, feasibility, or RWE, bring us a protocol you’re designing now. In a working session, we’ll identify which eligibility criteria can be tested against real site populations, which of your sites could take part, and what it would take to get them running. Book a protocol working session.
If you lead IT, data, or AI, request a demo to see how the Rhino FCP fits your stack and governance model.
FAQs
How Long Do Clinical Trials Take?
Clinical development takes about ten years at the median, measured from the start of Phase I to approval, according to IQVIA, and timelines vary widely by therapeutic area. Patient enrollment drives much of that time. IQVIA identifies enrollment duration as the largest opportunity to shorten trial cycle times.
How Much Does a Clinical Trial Delay Cost?
A single day of delay costs a sponsor roughly $800,000 in unrealized drug sales, according to a 2024 Tufts CSDD white paper. Direct trial costs add about $40,000 per day across Phase II and III, and Phase III alone runs close to $56,000 per day.
How Does Federated Learning Speed Up Patient Recruitment?
Federated learning runs eligibility and prediction models on each hospital's own EHR data. The site's study team reviews any patient-level matches and decides whether to contact patients, while the sponsor or its CRO sees only counts and aggregates. Keeping records in place lowers the barrier for hospitals to join, so a trial can draw from more sites, including community hospitals that can refer patients to active sites.
Is Federated Learning HIPAA and GDPR Compliant?
Federated learning keeps raw patient data inside the institution that holds it, and only model updates or aggregate results leave the site. That architecture removes the biggest compliance hurdle in multi-site research, which is moving identifiable data across organizations or borders. Compliance still depends on how each deployment is governed, so sponsors should confirm their platform supports access controls, privacy-enhancing technologies, and audit trails that meet GCP and 21 CFR Part 11 expectations.
