Why 40% of Trials Miss Enrollment Targets
Clinical trial enrollment failure is the most expensive problem in drug development that most organizations treat as unavoidable. Forty percent of trials fail to meet their original enrollment targets. The average trial extends its enrollment period by 30-40%. Every month of extended enrollment adds $500K-$2M in operating costs and defers the data readout that investors and patients are waiting for.
This is not a random problem. Enrollment failures have structural causes that are addressable — and increasingly addressable with AI.
The five causes of enrollment failure
1. Protocol over-restriction. The most common cause. Eligibility criteria written to maximize scientific purity exclude 80-90% of the eligible population. Many exclusion criteria are inherited from prior trials without analysis of whether they're necessary for safety or data quality.
2. Site selection based on relationships rather than data. Sites are often selected because of existing investigator relationships or geography rather than actual enrollment performance. A site that enrolled 3 patients in the last relevant trial is not a good site for your next trial, regardless of the PI's reputation.
3. Patient identification failures. Eligible patients exist in the healthcare system but are never referred or identified. Primary care physicians don't think of trials. Patients who would qualify are never asked. The connection between the eligible population and the trial doesn't happen.
4. Screen failure rates. Even when patients are referred, high screen failure rates waste site resources and slow enrollment. If 60% of screened patients fail on a single eligibility criterion, that criterion needs analysis — either it's excluding patients who don't need to be excluded, or your referral targeting is wrong.
5. Site activation delays. The average site takes 3-4 months from selection to first patient enrolled. Protocol-to-activation delay can exceed 6 months at academic sites. This delays the enrollment clock before it starts.
What's addressable
Causes 1, 2, 3, and 4 are highly addressable with AI. Protocol design analysis, site selection intelligence, patient identification at population scale, and screen failure analytics all benefit directly from AI tools that exist today. Cause 5 (regulatory/institutional activation delays) is harder to compress and mostly lives in the coordination category.