Choosing Trial Sites Beyond the Feasibility Survey

Traditional trial feasibility relies heavily on site surveys: questionnaires where principal investigators estimate their available patient pools, startup timelines, and competing studies. In practice, these self-reported figures are notoriously optimiztic. Clinical operations teams frequently face low-enrolling sites, extended timelines, and costly protocol amendments.

Machine learning methods now promise to replace or augment static questionnaires by analyzing historical operational performance, electronic health records (EHRs), and real-world claims data. However, moving from optimiztic investigator estimates to machine learning predictions introduces a distinct set of methodological and operational risks that clinical trial leaders must navigate.

Predictive Feasibility: Enrolment, Complexity, and Amendments

Modern feasibility models tackle three distinct operational problems: forecasting recruitment trajectories, quantifying protocol burden, and identifying amendment risks before study launch.

Enrolment forecasting has shifted from static monthly averages to probabilistic models. Rather than assuming a constant accrual rate per site per month, advanced models treat recruitment as a time-varying process affected by competitive trial density, local seasonal fluctuations, and site-specific startup friction. These algorithms estimate site-level enrolment curves, allowing study teams to model realistic ramp-up periods rather than flat targets.

Protocol complexity scoring relies on natural language processing to dissect draft protocols. These tools parse inclusion and exclusion criteria, mapping required diagnostic procedures and visit cadences against standard care workflows. By scoring protocol burden against historical industry benchmarks, clinical operations teams can pinpoint operational bottlenecks, such as overly narrow laboratory thresholds or burdensome imaging schedules, while the protocol is still draft.

Predictive amendment models build on complexity scoring by correlating protocol features with historical protocol revisions across therapeutic areas. When an algorithm flags that a specific combination of biomarker screening and visit frequency historically leads to a protocol amendment, study leads can evaluate whether relaxing non-critical criteria preserves statistical power while preventing costly mid-trial adjustments.

Data Sources and Structural Biases

The utility of any feasibility algorithm depends on its underlying data substrate, which typically combines commercial claims data, EHR networks, and historical clinical trial registries. Each source introduces specific systematic biases that teams must account for during evaluation.

Claims data offer broad geographic coverage but lack granular clinical detail. They reflect billing events rather than true disease state, longitudinal symptom severity, or specific biomarker statuses. This often leads models trained on claims to over-estimate the true pool of protocol-eligible patients at a given location.

EHR networks provide rich clinical context, including laboratory values and diagnostic imaging reports, but suffer from significant data missingness and regional fragmentation. Patients who receive care across multiple unlinked health systems appear lost to follow-up or incompletely characterized, which can distort algorithmic assessments of patient prevalence.

Historical trial registries reflect past operational decisions rather than total epidemiological potential. Relying heavily on historical trial performance creates a self-reinforcing feedback loop. Sites that were selected previously receive more trial opportunities, building stronger operational metrics, while capable community centres remain invisible to predictive models regardless of their true patient density.

Equity, Access, and the Diversity Paradox

Unchecked feasibility algorithms tend to concentrate site selection into a narrow cluster of high-volume academic medical centres. Because historical trial data reflects past under-representation of minority and socio-economically disadvantaged populations, models trained on this data naturally reproduce those disparities.

Achieving meaningful demographic diversity requires explicit algorithmic constraints rather than naive site ranking. Teams evaluating site selection models should ensure that tools allow for multi-objective optimization, balancing historical operational velocity against community reach and demographic alignment with target disease epidemiology.

Expanding trial access beyond conventional research hubs also requires assessing a site's infrastructure support rather than just its historical trial volume. Machine learning can help identify high-potential community health centres by matching local disease prevalence from claims data with regional investigator density. However, clinical operations teams must still account for the necessary site enablement resources, such as dedicated research coordinators and specialized equipment.

Evaluating Vendor Models Honestly

The commercial landscape for trial feasibility software contains significant marketing exaggeration. Operating leaders evaluating vendor solutions should apply rigorous validation checks:

• Baseline Comparisons: Require vendors to demonstrate performance gains against a simple baseline, such as historical site-level medians within the same therapeutic area, rather than comparing against unadjusted investigator survey estimates.
• Temporal Leakage Checks: Interrogate the model for data leakage. A common flaw in trial modeling occurs when post-hoc trial data, such as total actual trial duration or global completion dates, is exposed to the model during historical training, artificially inflating its predictive accuracy.
• Out-of-Distribution Validation: Request validation metrics on therapeutic areas, protocol phases, or geographic regions that were completely held out from the training set. A model trained primarily on oncology trials in North America will often fail when deployed on rare disease protocols in Western Europe.
• Training Coverage: Confirm that the training data covers the specific patient populations and care settings relevant to your upcoming trial portfolio.

Operational Integration and Governance

Implementing predictive feasibility tools requires a clear understanding of their place within clinical governance frameworks. Machine learning outputs do not replace site qualification visits or principal investigator engagement; they serve as decision-support signals to refine initial site lists and protocol design.

Teams must frame algorithmic site scoring as an advisory input subject to internal standard operating procedures, quality unit oversight, and expert clinical judgment. Any software tool used to support site selection or enrolment forecasting may support, but does not satisfy, regulatory compliance, GxP validation, or audit-readiness requirements.

Ultimately, predictive feasibility tools are most effective when used to replace vague questionnaires with quantitative, data-driven discussions between sponsors, CROs, and clinical sites. By pairing algorithmic predictions with thoughtful human oversight, trial sponsors can build resilient site networks, improve participant access, and reduce avoidable protocol revisions.

How it works in practice

Feasibility models are ordinary tabular prediction problems dressed in operational language. The mechanics are simple; the data quality and the bias questions are not.

• What data goes in. Historical trial operational data: site-level enrolment by study and indication, startup and activation timelines, screen-failure and dropout rates, protocol complexity features, investigator history, and where permitted, claims or registry data describing the patient population around a site.
• What the model predicts or ranks. Expected enrolment rate and time-to-first-patient for a candidate site under a specific protocol, and from those, a ranked site list for a given country and target.
• Technique or model family. Gradient-boosted trees or regularized regression on tabular features, sometimes with survival models for time-to-activation, plus simple simulation on top to turn per-site predictions into an enrolment curve for the study as a whole.
• How it is trained or validated. Held out by study and by time period — a model tested on studies it partly trained on will look excellent and fail in the field. Performance is checked separately for new sites with no history and for sites serving under-represented populations, where the data is thinnest.
• What the team does with the output. The ranking informs country allocation and the site list, and the simulated curve sets a defensible enrolment assumption. Teams that use it well deliberately hold slots for sites the model scores poorly because they lack history rather than capability.
• A representative example. Input: a candidate site with three prior studies in a related indication and a protocol with a demanding inclusion criterion. Output: predicted 1.4 patients per month with a wide interval, versus a network median of 0.9. Operations activates it early and revisits the assumption after the first two months of actual enrolment.

Sources

1. ICH E6(R3) Good Clinical Practice — International Council for Harmonization.
2. Enhancing the Diversity of Clinical Trial Populations — U.S. Food and Drug Administration.
3. ClinicalTrials.gov — U.S. National Library of Medicine.