Transforming Effective Patient Recruitment within the Changing Healthcare Landscape

Over the past decade, clinical trial timelines have steadily lengthened, driven primarily by persistent patient recruitment and retention challenges. The root causes are twofold: increasingly complex trial protocols that heighten participant burden and a rapidly evolving health information environment that has transformed how people learn about and engage with health care. The cumulative effect has been striking — phase II and III protocols have seen a 39% increase in patient burden, and the average study-related clinical visit now lasts approximately two hours.

Compounding this problem is the fact that only about 5% of eligible patients ever enroll in clinical studies.^1,2 Recruitment efforts have long centered on a narrow and homogenous segment of the population — health-literate, insured individuals who live near major research centers. Urban patients remain three times more likely to participate in trials than rural ones, and participants are still predominantly higher-income, white, and male.^3,4 Less than 10% of Americans report having ever engaged with a clinical trial, and only around 15% of those participants come from minority populations.³ Even in oncology, one of the most actively researched fields, fewer than 15% of patients join clinical studies.⁵ Women, younger adults, people with disabilities, members of the LGBTQ community, and those in rural or low-income communities continue to be markedly underrepresented.

The barriers are both systemic and individual. On the systemic level, escalating protocol complexity — and the equally complex information used to describe trials — creates confusion from the very start. Trials often demand time and financial commitments that are difficult for many to manage. Awareness among health care providers remains uneven, and the investigator community still lacks comprehensive diversity and representation. At the individual level, many people simply don’t know that clinical trials could be an option for them. Cultural differences, language limitations, geographic distance from study sites, and enduring mistrust of the pharmaceutical industry further compound the challenge.

Together, these structural and personal obstacles have made patient recruitment and retention the single greatest impediment to bringing new therapies to market. Half of all clinical trial sites fail to meet their enrollment targets, and 86% of trials miss their overall recruitment goals.⁶ The cost is not only measured in time and money but also in delayed access to potentially life-changing treatments for the patients who need them most.

The growing difficulty of patient enrollment is also a reflection of how profoundly healthcare behavior has changed. Patients no longer rely solely on their physicians for information or guidance. They research their own symptoms, compare treatment options and make increasingly proactive decisions about their care: a shift fueled by the pervasive influence of digital technology in daily life.

Today’s patients live in a digital-first healthcare world. Many believe that digital health technologies have improved the quality and personalization of their care and are comfortable sharing health data with providers through secure digital platforms. The same systems that once simply supplemented clinical interactions — patient portals, mobile health apps, telehealth consultations, and wearable devices — are now fundamental to how people manage their well-being.

Across all demographics, but especially among younger generations, patients expect health care that mirrors the convenience and immediacy of the other digital experiences that define their lives. They expect seamless, tech-enabled interactions: real-time access to their medical records, direct communication with their care teams, and personalized recommendations that reflect their data and preferences. Increasingly, they also use online communities to seek peer advice, learn about treatment options, and share personal experiences.

These expectations now extend into the realm of clinical research. Patients accustomed to integrated, digital-first care anticipate the same level of accessibility, transparency, and personalization from clinical trial sponsors. They want engagement that fits naturally into the way they already navigate their health: frictionless, relevant, and available when and where they are most receptive.

The rise of digital health has produced a vast, continuously expanding reservoir of patient data — and with it, an unprecedented opportunity to understand patients more deeply and engage them more intelligently. Every interaction within the health care system now generates data: electronic health records (EHRs), insurance claims, laboratory results, prescriptions, clinical notes, wearable and app-based biometrics, and even participation in online health communities. When combined, these data streams create a detailed and dynamic portrait of where individuals are along their health care journeys, their potential eligibility for clinical studies, and the moments when they may be most open to engagement.

When properly integrated and analyzed, this wealth of real-world data reveals patterns that were previously underutilized for trial matching. Artificial intelligence (AI) and machine learning (ML) algorithms can parse both structured and unstructured information to refine the ideal patient profile, not only identifying who meets a study’s inclusion and exclusion criteria but also assessing who is most likely to express interest, adhere to protocols, and remain engaged.

AI is increasingly used to detect real-time patient “signals”: a new prescription written, a lab value outside the normal range, a recent imaging result, or a surge of activity within an online support group. These triggers can indicate when a patient may be most receptive to learning about a clinical trial. Likewise, AI-powered pre-screening tools can extract and interpret complex medical records to match patients with remarkable accuracy, streamline site workflows, and even maintain curated waiting lists of individuals who meet (or nearly meet) protocol criteria.

In this way, AI enables a more complete and connected understanding of both individual patients and broader populations. It links disparate data sets into coherent insights, rapidly organizing and analyzing information that would otherwise remain siloed. Importantly, AI is not the objective in itself but the means to a more human goal: making intelligent, empathetic connections with patients. By unlocking, analyzing, and activating complex data at scale, AI transforms how recruitment strategies are conceived and executed, helping sponsors meet patients exactly where they are in their digital health journeys.

Data alone hold little value, and AI or ML applied without purpose produces only noise. The true power lies in using these tools together to identify not just who qualifies for a clinical study, but who is likely to participate and remain engaged. Effective recruitment begins with understanding the full ecosystem of influence: patients themselves, the caregivers who support them, and the health care professionals (HCPs) who guide their decisions.

A nuanced understanding of patients and their real-world experiences is the foundation for meaningful engagement. Knowing where individuals are on their healthcare journeys — the challenges they face, the channels they trust, and the ways they prefer to receive information — allows sponsors to design outreach that feels authentic rather than transactional. By combining real-world data with insights into lived experience, sponsors can match the right message to the right audience through the right channel and at the right moment. This patient-first framework enables both personalization and scalability, allowing engagement strategies to be tailored by disease area, geography, or study design while maintaining a consistent philosophy. When recruitment mirrors the patient’s own journey, participation feels like a natural extension of their care rather than a departure from it.

Building on this understanding of the challenges and opportunities that shape patient engagement, the PPD™ clinical research business of Thermo Fisher Scientific leverages a patient-first recruitment and retention model that unites global reach and pinpoint precision and enables sponsors to connect with the right participants. By combining diverse data sources with advanced analytics, our team identifies the digital and real-world touchpoints where trial opportunities naturally intersect with patients’ existing healthcare experiences. This integrated view allows teams to design outreach that feels organic within the patient journey rather than external to it.

Our approach is built on three complementary pillars: point of care data-driven targeting, community networks and registries, and adaptive digital outreach.

We leverage a wide spectrum of real-world data — EHRs, claims, pharmacy and laboratory results, genetic-testing networks, HCP referrals, and patient-submitted biometrics — to create a holistic understanding of potential participants. ML-enabled modeling can simulate how inclusion and exclusion criteria affect the eligible patient funnel, estimate likely adherence patterns, and identify subgroups that may require tailored interventions to stay engaged. Patient pathways are customized according to their healthcare touchpoints for optimal conversions. By integrating these insights across omnichannel point-of-care platforms, we can reach patients directly within their existing care environments — through their physicians, pharmacies, diagnostic networks, or digital health tools — ensuring that trial opportunities are presented naturally within the context of their ongoing health care journeys.

Through HealthUnlocked™, our leading health care online community, we engage over 2 million members across 400+ advocacy and condition-specific groups. Our global research registry, encompassing 10 million pre-consented individuals spanning 200+ indications in 41 countries, provides immediate access to patients who are informed, motivated and ready to participate. These networks transform passive awareness into active interest, building trust through peer connection and relevant information.

Our digital marketing teams leverage AI-driven insights to locate and engage ideal patient profiles more effectively than ever before. By combining creative assets from our in-house agency with predictive modeling, social listening, and advanced audience analytics, we identify when and where patients are most receptive to learning about clinical research. Whether engaging healthy volunteers through interest-based communities or reaching individuals who are actively researching symptoms or diagnoses, our outreach is guided by data and refined by machine learning.

Content is distributed through 25+ organic social media channels with a combined following of more than 200,000, ensuring broad reach and authentic engagement. We also integrate proprietary and third-party data, including claims, EHR, and behavioral insights, into leading digital platforms to expand visibility and pinpoint specific patient populations with exceptional accuracy. These omnichannel campaigns evolve in real time, with analytics continuously optimizing tone, timing, and resonance while maintaining HIPAA-compliant, privacy-protected interactions.

Together, these capabilities allow us to connect patients across every stage of their digital healthcare with relevant opportunities in ways that feel timely, trusted, and personal. By aligning technology, data, and empathy, we accelerate enrollment, reduce site burden, and help promising therapies move from development to delivery faster and more efficiently.

To ensure that engagement translates into high-quality enrollment, we support each stage of the patient journey through a multi-layered screening and handoff process.

• Self-service pre-screening questionnaires that efficiently filter out ineligible candidates.

• Patient navigator assisted virtual pre-screening further validate harder-to-understand medical eligibility.

• Licensed nurses review medical histories for protocol alignment.

• Personalized follow-up, including reminders and check-ins that guide each participant through every milestone.

Our SMART Screening solution elevates this process further. Proprietary AI algorithms prioritize high-probability candidates by weighing demographics, biometrics, and clinical records. Integration with EHRs enables automated population of critical data fields, reducing manual error. Patients can upload wearable or lab data directly, enhancing precision in eligibility determinations. The system’s continuous learning models refine matching criteria over time, improving referral quality with each study.

Advanced site-facing technologies close the loop. Automated handoff triggers alert sites as soon as candidates are fully screened. Site dashboards provide real-time visibility into referral status, patient communications, and next steps. Bi-directional data flow ensures that electronic data capture (EDC) and clinical trial management systems (CTMS) remain synchronized, eliminating redundant entry and minimizing administrative burden.

Through this integration of human insight and digital precision, we execute recruitment strategies that are both scalable and deeply personal. Each engagement feels relevant, timely and aligned with the patient’s own healthcare experience, ensuring that the path to trial participation fits naturally within their broader journey.

At our core is the principle of meeting patients where they already are: online, informed, and engaged in their own care. To achieve this, we are the only contract research organization (CRO) offering an integrated, large-scale online community platform designed specifically for healthcare and research engagement.

HealthUnlocked, the largest dedicated social network for patients, now connects more than 2 million members across hundreds of condition-specific communities. The platform is built around trust: it provides a safe environment where patients can find expert-vetted health information, share personal experiences, and receive support from peers navigating similar diagnoses. Because participation is completely free, HealthUnlocked offers an uncluttered, credible space where learning, connection, and research awareness naturally intersect.

Trial opportunities are not pushed through conventional marketing channels but integrated seamlessly into relevant conversations and educational content. Patients encounter them within the flow of their daily interactions, as part of the same trusted environment where they already seek advice and share insights. This authenticity is central to building awareness that leads to meaningful participation.

Behind the scenes, real-time analytics dashboards track sentiment, trending topics, and engagement patterns, helping both sponsors and patient organizations understand what matters most to their audiences. This continuous feedback loop allows us to refine outreach strategies and better align study opportunities with patient interests and needs.

HealthUnlocked has transformed what was once passive recruitment into an active, patient-centric ecosystem that fosters trust, empowers education, and creates a direct bridge between patient communities and the clinical research enterprise.

Traditional patient recruitment often depends on broad outreach and basic prescreening checklists to identify eligible participants. While this approach can generate volume, it frequently sacrifices precision. Many motivated patients are overlooked, while sites are inundated with ineligible referrals that slow down screening and increase workload. The result is inefficiency on both sides: patients lose momentum, and sites lose valuable time.

AI fundamentally changes that equation. With appropriate patient consent, AI algorithms can extract and interpret data from EHRs, diagnostic results, lab values, medication histories, and treatment timelines to assess study eligibility with far greater accuracy. By analyzing both structured and unstructured information, AI determines not just whether a patient technically qualifies but whether participation aligns with their real-world medical journey. This precision reduces friction between patients and sites and ensures that individuals who express interest are connected to trials where they are most likely to succeed.

In collaboration with a leading AI software organization, we developed Smart Search and Match, a large language model (LLM)-driven tool to query potential candidates in our proprietary patient registry and database against protocol inclusion and exclusion criteria, assigning confidence scores for each relevant metric.

This process enables far more accurate and efficient prescreening. Eligible and motivated patients can be prioritized based on their care patterns, prior behaviors, and expressed interest, providing sites with a higher-quality starting pool and reducing screen failure rates. For patients, the experience feels streamlined and personalized: instead of encountering dead ends or repetitive questionnaires, they are guided toward opportunities that genuinely fit their needs.

Beyond recruitment, we also apply AI to feasibility and protocol design, using predictive modeling to simulate enrollment scenarios, identify inclusive site geographies, and streamline protocol development. These capabilities not only accelerate the start-up phase but also enhance representativeness and trial performance from the outset.

Through such applications, AI serves its intended purpose — not as a replacement for human judgment but as an enabler of smarter, faster, and more equitable patient connections.

By aligning every recruitment strategy with the individual needs, preferences, and behaviors of patients, we create an experience that feels like a natural extension of their personal healthcare journeys. Yet this work is far from finished. The landscape of patient engagement is changing rapidly, and each advancement brings new opportunities to make participation in clinical research more inclusive, accessible, and intuitive.

Our patient-first framework provides the foundation for targeted recruitment and engagement, but it is intentionally built to evolve. We continuously test, measure and refine our methods, integrating emerging technologies, channels, and data sources to better reach and support patients wherever they are. AI, ML and other advanced tools are not simply adopted for novelty; they are applied purposefully to extend access, improve matching accuracy, and strengthen relationships among patients, sites, and sponsors. Additional opportunities lie in dialing up the framework for early-phase studies and dialing down to support all-virtual postapproval observational studies.

Every innovation that we leverage serves to deepen understanding, remove barriers, and connect individuals to the right opportunities at the right time. Through this ongoing evolution, we continue to transform recruitment from a procedural step into a patient-centered experience that accelerates discovery and delivers better outcomes for all. As patient expectations evolve, so too must clinical research, and for us, innovation remains inseparable from empathy.

1. Henderson, Keri. “Analyzing the influence of key factors for patient willingness to participate in decentralized clinical trials.” American Pharmaceutical Review. May/Jun. 2024.

2. 2015-2019 Drug Trials Snapshots Summary Report Five-Year Summary and Analysis of Clinical Trial Participation and Demographics. U.S. Food and Drug Administration. U.S. Food and Drug Administration. Accessed 30 Apr. 2024.

3. “HINTS Briefs.” Health Information National Trends Survey. Jun. 2022.

4. Allison, Karen, Deepkumar Patel, and Ramandeep Kaur. “Assessing Multiple Factors Affecting Minority Participation in Clinical Trials: Development of the Clinical Trials Participation Barriers Survey.” Cureus. 14: e24424 (2022).

5. Moreland, Katie, et al. “Disparities in Clinical Trial Participation: A Cross-Sectional Survey of Cancer Patients at a Midwest Academic Medical Center.” Curr. Oncol. 31: 5367-5373 (2024).

6. Huang, Grant D, et al. “Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative.” Contemporary Clinical Trials. 66: 74-79 (2018).