Beyond Historical Assumptions
Clinical trial recruitment failures have persisted for decades, yet most sponsors still plan studies using the same basic approach: look at historical recruitment rates, identify sites with patient volume, and hope past performance predicts future success. This approach overlooks a fundamental reality: the clinical landscape is not static, and historical data alone cannot account for how patient care has evolved since those reference trials were conducted.
Patient Journey Intelligence allows for a different, more predictive approach to trial planning. Rather than relying on decontextualized historical metrics, it maps the country-specific pathways patients actually follow from diagnosis through treatment. This mapping reveals operational friction points, access barriers, and eligibility mismatches that conventional feasibility studies consistently miss.
The consequences of ignoring these realities are substantial. According to data from Tufts CSDD, up to 43% of protocol amendments are avoidable, many linked to eligibility criteria oversights that could have been identified before the first patient was ever screened. When teams integrate patient journey insights early in protocol design, they move from reactive problem-solving to proactive risk prevention.
The Standard of Care Drift Problem
One of the most common disconnects between traditional feasibility and real-world recruitment success is what we call "The Standard of Care Drift." Sponsors routinely rely on historical recruitment rates to predict future performance without accounting for how the treatment landscape has evolved since those reference trials ran.
Consider a past trial that recruited quickly because a specific therapy was not yet reimbursed in that country. If that therapy has since become the standard of care, the historical recruitment rate becomes meaningless. The patient population that considered the original trial attractive has fundamentally changed.
Patient Journey Intelligence addresses this by continuously monitoring the dynamic standard of care landscape. It tracks not just which drugs are approved, but which are actually prescribed, reimbursed, and accessible to patients in each target country.
Approved Does Not Mean Accessible
A significant gap exists between a drug being legally approved and a patient being able to access it. Feasibility teams frequently assume that an "Approved" status means the drug is available as a comparator. However, the pathway from approval to patient access involves several gatekeeping steps, each of which can narrow availability.
For example, a drug may be approved for lung cancer generally, but the health technology assessment (HTA) might be favorable only for non-small cell lung cancer (NSCLC). Then payers may determine that existing options are "good enough" and that reimbursing the drug for all NSCLC patients is too costly. Reimbursement gets limited further, perhaps to metastatic NSCLC only. Even if doctors believe the drug is right for NSCLC patients more broadly, prescribing outside reimbursement guidelines means out-of-pocket payment. For many therapies, that is simply not realistic.
If a protocol requires patients who have failed a specific line of therapy that is approved but not reimbursed in a target country, the patient pool effectively does not exist. Patient Journey Intelligence reveals these gaps before site selection begins.
Uncovering Operational Choke Points
Conventional feasibility studies often identify sites with high patient volume but fail to map the pathways that actually bring patients to those sites. Patient Journey Intelligence looks beyond prevalence to identify operational choke points that determine whether eligible patients can realistically be recruited.
In some countries, patients with certain conditions are managed primarily by general practitioners rather than specialists. Selecting specialist sites in these regions without a clear referral strategy guarantees recruitment failure. The patients exist, but they are not where the trial expects them to be. Patient Journey Intelligence maps referral patterns and identifies who actually treats the patient at each stage of their care journey.
Teams must also consider the operational window between diagnosis and treatment initiation. If the time from diagnosis to treatment start is too short (for example, less than three weeks), there may be no practical opportunity to consent a patient for a trial before they begin standard care. Patient Journey Intelligence compares these timelines across countries, revealing where recruitment windows align with operational reality and where they do not.
Designing for Real Patients, Not Theoretical Ones
Perhaps the most persistent challenge in trial design is what we call the "Unicorn Patient" problem: sponsors designing protocols for a theoretical, "perfect patient" who fits a specific scientific profile while ignoring the messy reality of actual clinical presentation.
Protocols often require patients to have failed specific therapies. For instance, a trial might target third-line pancreatic cancer patients who have failed pembrolizumab. Feasibility studies typically look at disease prevalence, but Patient Journey Intelligence looks at the specific treatment history and sequencing required for eligibility. The critical question becomes: in which countries is pembrolizumab actually the first- or second-line standard of care? Only in those countries will a pool of eligible "failure" patients exist.
Similarly, as access to biologics increases globally, finding "biologic-naive" patients becomes harder. Patient Journey Intelligence maps which countries have lower access to these drugs, identifying them as prime targets for trials requiring naive populations.
Patient Burden and Protocol Fit
Conventional feasibility focuses on identifying patients who meet inclusion criteria. Patient Journey Intelligence goes further by checking whether the protocol fits the patient's life.
A large sponsor company shared a revealing case about hemophilia trials. Prior to 2020, the burden of disease was high, making gene therapy trials attractive to patients. Once a new drug arrived that was easier to administer, patients felt their condition was "good enough" and lost the urgency to participate in what they viewed as risky or burdensome gene therapy trials. The sponsor had not accounted for this shift in patient perspective when planning recruitment.
Patient Journey Intelligence also aims to align trial visits with the existing standard of care rhythm, reducing dropout risk by designing protocols that fit within patients' established care patterns rather than imposing conflicting schedules.
Breaking Down Data Silos
One of the surprising barriers to effective, patient-centric trial planning is internal rather than external. Valuable patient journey data often sits with commercial or market access teams preparing for product launch, while clinical operations teams plan trials without access to this intelligence.
A large sponsor company described how patient journey mapping is typically done by commercial teams for launch readiness, but clinical teams rarely see this data in its full form to optimize recruitment or stress-test protocols. Patient Journey Intelligence makes this data actionable for trial planning, translating market access insights into recruitment strategy and protocol design improvements.
Predicting Country-Level Risk
In global trials, aggregate performance can mask significant country-level failures. One country's recruitment success often compensates for underperformance elsewhere, obscuring systemic issues until timelines have already slipped.
Patient Journey Intelligence enables sponsors to predict country-level enrollment risks before site activation by revealing operational friction points made invisible by aggregated historical data.
In countries with multiple approved therapies, patients often prefer standard care over trial participation. Conversely, in countries where patients lack access to standard treatments, clinical trials become more attractive—a factor conventional feasibility often misses.
Historical recruitment rates become misleading without knowing the specific standard of care context at the time those trials ran. A country may have performed well historically because the standard of care was limited, creating patient demand for trial participation. If treatment access has since improved, that same country may now struggle with recruitment despite strong historical performance.
The Case for Early Integration
The timing of when patient journey insights enter the trial planning process determines their impact. Introducing these insights early, ideally during the pre-clinical to clinical transition or program planning stage, allows sponsors to avoid costly amendments and optimize entire programs.
Without real-world patient journey data, teams risk creating protocols that lead to high screen failure rates, recruitment challenges, and retention issues. One case involved a protocol that experienced high screen failure rates, forcing six to seven amendments because the eligibility criteria were scientifically sound but operationally impossible to find in a single patient. Each amendment costs time, money, and credibility with sites and patients.
Early patient journey mapping prevents this cascade by identifying operational disconnects before they become protocol requirements.
Conclusion: From Aspiration to Execution
Traditional feasibility approaches miss critical signals that determine trial success. They tell sponsors where patients were, not where patients are or whether the trial can realistically reach them. Patient Journey Intelligence reveals those signals before sites activate, enrollment begins, and costly amendments become necessary.
The value extends beyond risk mitigation. Patient Journey Intelligence makes patient-centric trial design operationally possible. It grounds aspirational patient centricity in the real-world experiences of patients navigating their care pathways, answering the questions that matter most: Where do patients receive care? Who manages their condition? What treatments can they actually access? How does the trial fit within their existing routines?
When sponsors integrate these insights early, they design protocols that work for the patients they actually need to recruit, not the theoretical patients they wish existed. They select countries where those patients can realistically participate and create visit schedules that respect patient burden rather than ignore it.
The industry has embraced patient centricity as a value. Patient Journey Intelligence provides the means to practice it now, in real world scenarios. For sponsors committed to designing trials that honor both scientific integrity and patient experience, this shift from reactive to proactive planning is no longer optional. It is essential.
Maya Zlatanova