A genetically-defined trial can be designed correctly and still stall. The protocol names the right molecular subtype, the endpoints are sound, and the therapeutic hypothesis holds. Then months pass, screen-failure rates climb, and enrollment slips behind plan. This is the precision medicine execution deficit: the distance between an elegant protocol and a randomized patient, where most of the loss actually happens.
When teams review what went wrong, the explanation often turns inward. The biology was harder than expected, or the eligibility criteria were too narrow. That account is usually incomplete. A large share of screen failure and enrollment delay in precision trials is procedural rather than biological. Procedural losses are measurable and fixable. In practice they are rarely measured with rigor, and almost never owned by a single party from first contact to randomization.
Two roles carry the consequences. Clinical Operations leaders answer for enrollment speed, timeline adherence, and screen-failure rates. Medical and scientific leaders answer for eligibility accuracy and data quality. Both inherit the same funnel, and both are held to outcomes that a fragmented delivery model quietly undermines. Naming the procedural share of the loss is the first step toward assigning it.
In a genetically defined trial, matching an eligible patient to the molecular criteria is only the first hurdle. Read quickly, a low enrollment yield looks like a hard ceiling set by biology, as though the eligible population is simply too small to fill the study. The funnel tells a different story.
Precision oncology offers the clearest published view of that funnel, and the pattern it reveals holds across genetically defined studies. A study of the NCI-MATCH trial reported that 37.6% of screened patients carried an actionable genetic alteration, while only 17.8% were assigned to a treatment arm. The distance between a matching mutation and an enrolled patient accounts for more than half of the eligible population. Those patients qualified on the genetics and were still lost.
They were not lost because of the science. Consent timing, confirmatory testing, sample logistics, and results arriving after a clinical window had closed each removed patients who met the molecular criteria. Screen failure in genetically defined trials is an operational measure as much as a clinical one.
Enrollment delay follows the same pattern. A large analysis of clinical trials found that roughly one in five trials completed enrollment on time, with a median delay of 12.2 months. Delay of that scale rarely traces to one decision. It accumulates across handoffs, and each handoff drops a fraction of the eligible population before the next step begins.
That same study found that fewer than 46% of trials met their planned enrollment target, which shows how widely the shortfall spreads across studies. Enrollment gaps of that scale build up across the handoffs between recruitment, testing, and enrollment, with each transfer dropping a share of the eligible population. In a genetically defined study, an under-enrolling site is often one that cannot reliably find, test, and confirm the right patients inside the protocol's window.
The structural cause is ownership. Recruitment, pre-screening, genetic testing, consent, and enrollment usually sit with different vendors and internal teams. No single party owns the patient across the full path, so no single party is accountable for the losses between steps. Enrollment planning that accounts for genetics treats these handoffs as design decisions rather than afterthoughts, and that reframing is where the deficit starts to close.
Some of the deficit sits upstream of the trial entirely. Under-diagnosis is an invisible screen failure: patients who would qualify are never identified as candidates. In rare disease, EURORDIS survey data reports an average time to diagnosis of 4.7 years, with 25% of patients waiting more than five years and 73% misdiagnosed at least once. A patient who has no diagnosis cannot screen fail, because they never reach the funnel.
Where diagnosis happens is also shifting, and recruitment models built on old assumptions miss patients. A study of nephrologists found that 72% report using genetic tests in their practice, while genetic testing was ordered for only 3.8% of their patient population. The specialists now identifying genetic disease are not always the ones legacy recruitment plans target. Treating genetic testing as an enrollment criterion changes who a sponsor should reach, and when.
Closing the deficit starts with measuring it. A precision trial funnel is easier to fix when the numbers are separated rather than pooled into one rate:
A funnel measured this way converts a vague timeline risk into a set of specific, ownable failures. Once a loss has an owner and a number attached to it, it becomes a problem an operations team can solve rather than absorb. Pooled metrics hide that structure, which is why so much of the deficit stays invisible until a timeline has already slipped.
To ensure sponsors have access to real-time metrics that can be used for rapid decision-making, Sano measures conversion rates and drop-off at every step in the funnel. Learn more about conversion patterns and lessons in this report.
The execution deficit describes a set of measurable, procedural losses that a coordinated system can recover. Sano Genetics was built to close the intervals where eligible patients disappear, unifying participant recruitment, genetic testing, and long-term engagement so a single system carries the patient from first contact to enrollment. When one platform holds that path, screen failure and enrollment delay become visible, attributable, and addressable.
Sponsors keep the resulting data as a durable asset rather than losing it between programs. The next trial does not start from zero, and the same losses are not paid for twice. The path to the patient can be the weakest link, but that path can be measured and owned.
To discuss where your program loses eligible patients, get in touch.