AI-Powered Clinical Trial Site Selection & Feasibility
Site selection decides whether a study recruits on time or spends months carrying under-performing locations. Sponsors and CROs already hold historical trial data, investigator profiles, patient population estimates, epidemiology, site performance metrics and operational information. Feasibility and selection still rely mostly on questionnaires, existing relationships and manual analysis.
AI-assisted site selection ranks candidate sites on their predicted ability to recruit, execute and complete a specific study, and pairs naturally with AI-assisted patient recruitment once sites are active.
The Problem: Site Selection Is Often Based On Incomplete Evidence
A well-known investigator or a large hospital does not automatically make a high-performing site.
Sites overestimate their eligible populations, compete with other studies for the same patients, run into coordinator capacity limits, take longer to activate than planned, or consistently recruit fewer patients than they projected.
Feasibility questionnaires are useful, but they capture self-reported estimates at a single point in time. For a global study evaluating hundreds of candidate sites, the real question is: which sites have the highest probability of delivering this particular trial?
Getting it wrong produces zero-enrolling sites, recruitment delays, additional activations, higher monitoring cost, protocol amendments and longer development timelines.
The Technology Solution
A site selection and feasibility platform builds a decision layer around study planning.
It combines historical site performance, investigator experience, therapeutic-area expertise, recruitment velocity, patient population estimates, competing clinical trials, start-up timelines, geography, operational capacity, epidemiology and other permitted internal and external datasets.
Models evaluate each candidate site against the specific requirements of the protocol and produce site-level feasibility and performance scores.
The flow: Protocol analysed → Target patient population identified → Potential sites discovered → Historical performance evaluated → Competing trials assessed → Sites ranked → Feasibility team reviews the recommendations.
Each ranking comes with its reasons. A site scores low because of three overlapping competing trials or a fourteen-month average activation time, not because of an opaque number.
Key Capabilities
- AI-driven protocol analysis
- Investigator and site discovery
- Eligible patient population estimation
- Historical recruitment performance analysis
- Site enrolment probability prediction
- Competing trial intelligence
- Investigator therapeutic-area mapping
- Site activation timeline prediction
- Geographic and demographic analysis
- Automated site feasibility scoring
- Explainable site recommendations
- Portfolio-level feasibility dashboards
Business Impact
Feasibility moves from relationship-driven site selection to evidence-driven site selection.
Sponsors identify promising sites earlier, reduce their dependence on re-selecting familiar investigators, forecast enrolment more accurately and see recruitment risk before activation rather than after. CROs evaluate much larger investigator networks without a proportional increase in feasibility workload.
At portfolio scale that means fewer non-performing sites, faster recruitment, better geographic diversification, fewer trial delays and more predictable development timelines.
Every completed study strengthens the model. Site and investigator performance from the last trial becomes the evidence base for the next one.
Build Clinical Trial Intelligence Platforms With Nirmitee
Nirmitee designs and engineers custom clinical trial feasibility and site intelligence platforms for pharma, biotech and CRO teams, using their own trial history alongside relevant external data. Our clinical and R&D AI work covers protocol intelligence, investigator discovery, predictive site ranking and recruitment analytics, and connects to the CTMS and site-scouting modules of Clinera where teams want a faster start.
Do not just find clinical trial sites. Predict which sites are most likely to deliver. Talk to our team about your next study's feasibility.
Frequently Asked Questions
- What data sources feed a site feasibility model?
Internal history first: site performance from the CTMS, activation timelines, enrolment versus projection, query and deviation rates. Then external sources such as public trial registries for competing studies and investigator experience, claims- or EHR-derived patient counts, epidemiology and publication data. The internal history usually carries the most predictive weight.
- How is this different from the feasibility questionnaires we already send?
Questionnaires capture what a site says about itself at one moment. The model uses what sites actually did in comparable protocols and updates as new studies complete. Questionnaires remain one input; they stop being the only one.
- Can it explain why a site ranked low?
Yes, at factor level. A typical explanation shows the estimated eligible population, the number of competing trials in the same indication, the site's historical activation time and its recruitment velocity in similar protocols, so the feasibility team can challenge or accept the ranking on evidence.
- Does it help discover new investigators or only rank the ones we know?
Both. Discovery draws on publication activity, registry records, referral patterns and patient-population data to surface investigators the sponsor has not worked with, which is also how sponsors improve geographic and demographic diversity across a study.
- How does it connect to our CTMS?
The platform reads historical site and investigator performance from the CTMS and writes ranked shortlists and feasibility scores back, so study start-up teams work from their existing system. Clinera's CTMS module, Veeva Vault CTMS and Medidata are typical integration targets.