So your trials stay on plan, not in recovery mode.
Enrollment forecasts only matter if they hold up in the real world. We bring together AI, multimodal data, connected health records, and predictive intelligence with patient, referring-physician, and site engagement to identify enrollment risks earlier, keep recruitment on track, and create a seamless experience that helps patients find and access relevant clinical trials.
2-3X
higher conversion
40%
faster patient recruitment
36%
reduction in per patient enrolment cost
Every trial starts with an enrollment forecast. However, recruitment plans can fall behind when eligible patients are unaware of relevant trials or struggle to find and access them. At the same time, sites lose momentum, coordinator capacity gets stretched, and downstream study milestones come under pressure.
Adding more sites can expand reach, but it also adds cost and complexity. Without improving how patients are identified, informed, connected to relevant trials, and supported through enrollment, the same recruitment challenges persist across a larger network.
Patients and caregivers are becoming more informed and involved in healthcare decisions, including exploring clinical trials as a treatment option. Recruitment needs to make trial information easier to understand, discover, and act on, giving patients a clearer path from awareness to participation.
Connected datasets across health records, claims, labs, and other sources are making it possible to identify patients who may be suitable for specific trials earlier. AI can further support recruitment by creating personalized content based on patient needs and preferences, helping engage the right patients with information that is relevant to them.
Reaching patients now requires more than traditional and digital advertising. Search, AI assistants, pharmacy networks, patient advocacy groups, lab visits, healthcare providers, and community channels can all play a role. Recruitment strategies need to connect these online and offline touchpoints to reach patients where they already seek information and care.
Large academic medical centers are increasingly capacity constrained, driving sponsors to expand into community-based sites and other care settings closer to patients. For late-phase trials, sponsors are also exploring new countries and markets to broaden access, accelerate enrollment, and reach more diverse patient populations.
Sending more patient leads to sites does not necessarily improve enrollment. When poorly qualified patients reach sites and fail screening, coordinators spend valuable time on candidates who are unlikely to enroll. Better patient identification and prequalification can reduce this burden, allowing sites to focus their capacity on patients more likely to be eligible and supporting faster enrollment.
Every shift points to the same need: recruitment run as a connected performance system that learns from every site, patient, and enrolment signal.
Combine multimodal data across claims, labs, social, digital, and other sources to predict where eligible patients are. Layer this with historical site performance and competing trial activity to build stronger enrollment forecasts and select sites with greater recruitment potential.
Retention and enrollment signals feed back into the forecast, helping improve the next recruitment decision.
Biotech organizations are leveraging AI to enhance research productivity and speed innovation.
Explore how AI, ML, and RWD optimize patient recruitment to improve trial speed and efficiency.
Data-driven recruitment powered by AI improves patient access while reducing enrollment timelines.
Multimodal patient data and predictive analytics to inform patient identification, site selection, and enrollment forecasts
Personalized outreach across digital, pharmacy, advocacy, lab, healthcare, and community channels.
Referring physician identification and engagement informed by treatment and influence patterns.
Clinically trained nurse support to educate, counsel, screen, and connect qualified patients to trials.
Community site and geographic strategies to expand trial access and reach diverse patient populations.
Qualified patient referrals, coordinator support, scheduling, and automation to reduce site burden.
Site-level tracking against committed forecasts with early risk identification and corrective action.
Recruitment accelerates when patient finding, engagement, pre-screening, and site execution work as one connected process. Indegene combines multimodal data, predictive analytics, omnichannel engagement, and nurse-led support to identify suitable patients earlier and keep enrolment on track.
Claims, labs, connected health records, social, digital, and other datasets can be linked to identify where potential trial participants are and how best to reach them. Indegene uses these signals to inform patient targeting, site selection, and enrolment forecasting.
Patients need to be reached where they seek information and care. Indegene connects digital search and AI assistants with pharmacy networks, patient advocacy groups, labs, healthcare providers, referring physicians, and community channels to improve trial awareness and access.
Site burden falls when coordinators receive better-qualified patients rather than higher volumes of unqualified leads. Indegene combines patient identification, nurse-led pre-screening, scheduling support, and automation to help sites focus their capacity on patients more likely to enrol.
Enrolment stays on track when emerging risks are identified early and recruitment strategies adapt quickly. Indegene tracks performance against committed forecasts and uses patient, site, and enrolment signals to guide corrective action throughout recruitment.
Share your protocol synopsis and current enrolment plan. We can model the forecast, identify where recruitment is likely to fall short, and define an operating approach to close the gaps.