AI-Driven Patient Recruitment

Artificial intelligence (AI) can revolutionize clinical trial patient recruitment by swiftly identifying candidates and cutting recruitment time. Natural Language Processing (NLP) can effectively extract vital data from diverse structured and unstructured sources, whereas Machine Learning (ML) can automate labor-intensive tasks, and predictive modeling can evaluate patient enrolment probability and compliance with trial protocols. AI accelerates trials and ensures diverse participant pools, enhancing trial success.

1. How do you define AI-driven patient recruitment within the context of clinical trials, and what role does it play in enhancing the recruitment process compared to traditional methods? What are the primary challenges in conventional patient recruitment methods, and how can AI-driven approaches effectively address these challenges to streamline the process?

With more clinical trials getting decentralized and the adoption of AI/ML witnessing an upward trend, the clinical development landscape is evolving at a rapid pace, demanding insightful decision-making for increased predictability and efficiency. Leveraging AI-derived insights, especially with real-world World Data (RWD), fosters a safer, streamlined research environment, thereby optimizing the clinical trial process and drug discovery process. In the patient recruitment space, AI-driven strategies are revolutionizing the entire process mainly by boosting the implementation of data-driven clinical trials’ design and patient enrolment activities, respectively.

Some of the significant challenges pharmaceutical companies face with traditional patient recruitment methods are:

• Huge patient dropouts during initial screening, especially for rare diseases’ clinical trials

• Inability to meet patient enrolment timelines due to limited success in identifying potential participants

• Delayed responses from clinical research sites and unresponsiveness of research sites about the availability of potential target pool of participants

Integration of extensive structured and unstructured healthcare data from various RWD sources coupled with advanced analytics helps automate tasks while helping to elevate data quality in numerous activities across stages of clinical trials. Outlined below are a few examples of how AI/ML is improving trial design and patient enrolment in clinical trials:

Improvements in Study Design: AI enhances trial design and optimization by identifying patterns in data, enabling predictions about patient behavior and drug efficacy. AI/ML-enabled platforms analyze past studies to determine optimal patient populations, diagnosis, and prognosis requirements. This optimizes study design and improves decisions regarding country and site selection, enrollment models, and patient recruitment, eventually yielding predictable results, minimizing protocol amendments, and enhancing the overall efficiency of clinical trials.

Site identification and patient recruitment: AI and ML address challenges in site identification and patient recruitment for clinical trials. As studies focus on specific target populations of patients, achieving recruitment goals becomes more challenging, leading to increased costs and timelines. AI and ML mitigate these risks by identifying sites with high recruitment potential, suggesting effective recruitment strategies, and proactively targeting sites with predicted patient populations. This allows sponsors to prioritize sites and reach out to fewer sites with high enrolment probability. This accelerates recruitment and reduces the risk of under-enrollment, ultimately enhancing the efficiency and success of clinical trials.

Synthesizing disparate data elements, ML uncovers meaningful insights for precise site identification, ensuring access to ample patient populations. This approach significantly increases global enrollment rates compared to traditional experience-based site identification methods. Manual efforts in analyzing site risks and generating action items for clinical monitoring can be alleviated as advanced analytics offer composite site rankings, enabling precise risk identification. This accelerates decision-making, allowing for timely actions and issue avoidance in clinical trials.

2. Can you provide specific examples where AI has been successfully employed to accelerate patient recruitment in clinical trials, highlighting the key outcomes achieved? And, could you elaborate on specific instances where ML algorithms have significantly improved the efficiency of patient selection and screening for clinical trials, and how were these algorithms tailored to address specific challenges?

Integrating advanced AI and ML algorithms with existing data and domain expertise enhances clinical research efficiency. Unlike the traditional method of selecting and activating research sites based on historical data, using real-time site data enables just-in-time site activation. Prioritizing sites based on specific criteria, including target patient demographics, reduces recruitment forecasting time and resources. This approach offers a more accurate study completion timeline by activating sites based on current data, optimizing the clinical research process, and streamlining the path to patient enrollment.

Below are a few examples where with AI/ML some of the leading pharmaceutical companies garnered substantial process efficiencies in clinical development by meeting patient recruitment targets in record time.

This case involves leveraging AI/ML with a geofenced strategy to boost participant recruitment for a phase three trial on cytokine storm during the COVID-19 pandemic. Faced with recruitment challenges, the approach involved creating hyperlocal campaigns within a radius of targeted clinical research sites. By analyzing real-time location data, demographics, and user content preferences, the team achieved remarkable outcomes. The geofenced strategy resulted in over 17,000 weekly unique visitors to the trial landing page. From these visits, 460 interested participants were identified, indicating a 7% conversion rate from website visits to secondary qualification - which is 50% higher than industry norms. The success demonstrates the effectiveness of AI and ML technologies in optimizing patient recruitment strategies and achieving superior conversion rates. This approach not only addressed the specific recruitment needs during the pandemic but also showcased the potential of innovative digital strategies to enhance clinical trial outcomes in a targeted and efficient manner.

AI/ML-driven methodologies have also been used extensively for ongoing clinical trials as well. In one such instance, the pharmaceutical company had already engaged a Contract Research Organization (CRO) to enroll patients for a drug trial. However, the traditional methods used by the CRO were not very effective in meeting the patient recruitment targets. By leveraging AI/ML and RWD, the pharmaceutical company conducted a comprehensive site feasibility assessment, real-time analytics tracking, and the activation of accurate research sites. In addition to this, hyper-local, geo-fenced digital outreach campaigns prioritized patient qualification. Support by RN Concierge Services ensured swift participant engagement and handover to research sites. The result was the activation of 60+ research sites in approximately just three weeks and a remarkable 3x increase in participant enrollment rate. Thus, with data-driven site prioritization, omnichannel marketing, and RN Concierge Services, the company synergized with the existing CRO model to enhance outreach, refine the secondary qualification of eligible clinical trial participants, and meet the patient enrolment targets on time.

Read more: https://www.pharmafocuseurope.com/expert-talk/ai-driven-patient-recruitment