Curtis Hougland, FounderSeen & Heard Health exists to identify participants beyond these established pools, helping sponsors identify overlooked participants that traditional recruitment methods often miss. Through its purpose-built AI platform, it helps sponsors verify, recruit and onboard the difficult-to-find 15 to 20 percent of participants that often determine whether trials meet recruitment goals.
Traditional recruitment methods rely on fixed channels like referral networks, patient databases, EHR and digital ads, limiting their ability to adapt to evolving eligibility criteria.
Seen & Heard Health applies AI and graph analysis to public digital data to identify disease-related signals, behavioral patterns and relationships associated with harder-to-reach participant populations. The platform supports more targeted recruitment, engagement and enrollment strategies by helping sponsors identify high-potential recruitment regions, tailor outreach and activate trusted patient voices.
“We have trained our AI specifically for recruiting difficult-to-find patients, representing a new front in clinical trial recruitment,” says Curtis Hougland, founder.
Built over more than a decade, Seen & Heard Health’s platform is designed specifically for clinical trial recruitment. It also applies machine learning to organize unstructured public data alongside structured healthcare data.
Building Smarter Paths for Clinical Trial Enrollment
Seen & Heard Health applies AI to analyze digital signals individuals generate through text, images and online behavior, helping identify participants who match specific trial criteria. It uses unstructured data and digital breadcrumbs to help make probabilistic assessments about participant fit against trial criteria, contributing to stronger outcomes.
The process begins by mapping the criteria that define patient compatibility, from other medical conditions to gender, before ingesting and enriching data to form an initial cohort. The system then refines future recruitment through ongoing yes-or-no candidate feedback. It also supports engagement through trusted peer communities, familiar communication styles and outreach strategies designed around how prospective participants prefer to receive information, as trust in healthcare advertising continues to decline.
-
We have trained our AI specifically for recruiting difficult-to-find patients, representing a new front in clinical trial recruitment.
As AI becomes more embedded across healthcare, Seen & Heard emphasizes balancing automation with human engagement. Its team brings decades of experience across clinical trials, AI and patient engagement, reinforcing recruitment decisions where eligibility, outreach and trust cannot be reduced to automated data matching alone.
Across programs, the platform has supported the enrollment of more than 10,000 participants. Seen & Heard Health is also brought into stalled trials after traditional recruitment channels exhaust existing participant pools, helping sponsors surface overlooked participants beyond referral networks, databases and digital advertising.
Advancing Healthcare Access through Trusted Recruitment
Seen & Heard Health views diversity as part of the missing-cohort challenge in clinical trials, focusing on participants across ethnicity, gender, socioeconomic status and geography that traditional recruitment methods may overlook. It links broader representation in clinical trials to understanding how therapies perform in different conditions as they come to market.
Originally developed in partnership with the Defense Advanced Research Projects Agency, the platform’s early applications focused on outbreak detection through digital signals. That work established the foundation for interpreting fragmented public data to support complex participant identification challenges.
Looking ahead, Seen & Heard Health is pursuing responsible global expansion across life sciences, medical device and pharmaceutical applications while expanding direct platform access for sponsors and CROs to manage recruitment, verification and onboarding workflows.
As AI adoption accelerates across healthcare, Seen & Heard Health continues positioning recruitment around trusted engagement, human expertise and purpose-built automation for difficult-to-find participants.
Deep Dive
Rethinking Patient Identification in Clinical Trial Recruitment
Clinical development timelines are no longer constrained by discovery alone; they are increasingly dictated by the ability to identify and enroll the right patients at the right moment. Breakthrough therapies continue to advance, yet enrollment delays persist as a structural bottleneck. A large proportion of trials still fail to meet recruitment targets, not due to lack of interest, but because conventional outreach methods repeatedly surface the same limited patient pools. Referral networks, registries, digital campaigns and electronic health records offer reach, yet struggle to extend beyond already visible populations.
This imbalance introduces a deeper issue than speed alone. Trial viability now depends on accessing patients who are not actively signaling participation intent, those who remain outside institutional touchpoints or who exhibit low trust in healthcare systems. Recruitment strategies that rely on explicit engagement signals tend to exclude individuals who do not self-identify or who exist in fragmented data environments. The consequence is not only slower enrollment but also reduced diversity and limited representation across geography, socioeconomic background and disease profiles.
A shift is underway toward leveraging broader data ecosystems, particularly unstructured data generated through everyday digital behavior. Text, images and interaction patterns offer probabilistic indicators of health status, yet extracting meaningful signals requires systems that can interpret nuance rather than rely on predefined categories. The challenge is not access to data, but the ability to structure and refine it into actionable cohorts aligned with precise inclusion and exclusion parameters. Systems that continuously learn from recruitment outcomes, improving candidate selection with each iteration, demonstrate clear advantages in environments where criteria evolve.
"Systems that operate across therapeutic areas and geographies, without relying on pre-existing patient pools, enable scalable enrollment, especially for rare or dispersed populations. Seen & Heard Health applies machine learning to advance this model in clinical trials."
Precision in defining eligibility remains central. Recruitment effectiveness hinges on translating clinical protocols into data-driven search frameworks that account for comorbidities, geography, demographic variables and behavioral signals. Static filtering approaches struggle under this complexity. Adaptive systems that refine candidate pools based on feedback loops allow recruitment strategies to become more targeted, reducing inefficiencies and improving match quality. This dynamic refinement also shortens the distance between identification and enrollment, which is critical in time-sensitive trials.
Equally important is the method of engagement. Traditional outreach channels face diminishing response rates as trust in institutional messaging declines. Patients increasingly respond to communication that reflects familiar language, peer context and situational relevance. Recruitment approaches that align messaging with patient context, rather than relying on broad campaigns, are better positioned to convert awareness into participation. Engagement is no longer a downstream activity but an integrated component of the identification process.
These shifts point to a model where recruitment platforms are expected to unify data ingestion, cohort refinement and patient communication within a single framework. Systems that can operate across therapeutic areas and geographies without reliance on pre-existing patient pools offer scalability, particularly for trials involving rare conditions or dispersed populations.
Seen & Heard Health reflects this direction through a focused application of machine learning designed specifically for clinical trial enrollment. It applies trained models to both structured inputs, such as health records and large volumes of publicly available unstructured data, enabling identification of patients beyond conventional databases.
Its approach centers on refining inclusion and exclusion criteria into continuously improving search parameters, allowing candidate selection to become more accurate over time. The platform also integrates context-aware engagement, aligning outreach with patient behavior and communication preferences. Having contributed to enrollment across thousands of patients, it demonstrates an ability to support trials that struggle to reach completion through traditional channels.
...Read more
AI-Powered Clinical Trial Patient Recruitment Platforms Info
Q1
What Should Sponsors Expect from AI-Powered Clinical Trial Patient Recruitment Platforms?
Sponsors should expect more than a larger list of potential participants. AI-Powered Clinical Trial Patient Recruitment Platforms should help identify eligible candidates, refine matches as new information becomes available and support meaningful engagement throughout the recruitment process. Their greatest value comes from bringing together identification, verification and outreach, particularly in studies where even small enrollment shortfalls can delay progress.
Q2
How Does Seen & Heard Health Approach Difficult Trial Enrollment?
Recruitment challenges often begin when traditional channels have already reached the most visible participants. Seen & Heard Health focuses on the harder-to-reach individuals who can make the difference between meeting enrollment goals and missing them. Its purpose-built AI platform helps sponsors identify, verify and onboard overlooked candidates. Developed over more than a decade, the platform combines structured healthcare information with broader public data sources to uncover opportunities that conventional recruitment approaches may miss.
Q3
Why Do Hard-to-Find Participants Often Delay Trials?
Many eligible participants are difficult to reach through traditional recruitment methods. Some are outside large healthcare networks, others do not respond to advertising and some may be hesitant to engage with institutional messaging. AI-Powered Clinical Trial Patient Recruitment Platforms help bridge these gaps by pairing eligibility criteria with more flexible discovery and communication strategies. Better matching reduces time spent on unsuitable candidates and helps studies stay on track.
Q4
What Data Signals Matter in Modern Patient Recruitment?
Finding the right participant rarely depends on a single data point. Factors such as health history, location, comorbidities and relevant digital signals can all contribute to identifying potential matches. AI-Powered Clinical Trial Patient Recruitment Platforms use these inputs to build and refine candidate cohorts over time. The strongest platforms also include human oversight, recognizing that a promising match still requires careful review before enrollment begins.
Q5
How Should Sponsors Evaluate Platform Quality?
Sponsors should evaluate a platform using a real study protocol rather than relying solely on demonstrations. Key questions include how eligibility criteria are translated into searches, how feedback improves future matching and how recruitment decisions are explained. AI-Powered Clinical Trial Patient Recruitment Platforms should also support effective patient communication, since trust, relevance and message quality often influence participation more than broad outreach volume alone.
Q6
How Does Seen & Heard Health Support More Representative Enrollment?
Seen & Heard Health approaches trial diversity as a recruitment challenge rather than a separate initiative. The company focuses on identifying participants across different ethnicities, genders, socioeconomic backgrounds and geographic regions that may be overlooked by traditional methods. Through trusted peer communities and participant-centered communication strategies, its platform has helped enroll more than 10,000 individuals. This approach demonstrates how AI-Powered Clinical Trial Patient Recruitment Platforms can improve representation when participant discovery and engagement are addressed together.
