Vaccines, antibiotics, chemotherapy, insulin, organ transplants and anti-rejection drugs, HIV treatments, immunotherapy, CAR-T, and neurological disease therapies have all greatly improved and saved human lives. These breakthroughs were discovered by science and advancements in Technology, but they were powered by patients.
If only it were that easy…
Patient recruitment and clinical trial matching are among the biggest challenges in clinical development. Eighty percent of all clinical trials are delayed or fail due to patient recruitment issues. Traditional patient recruitment and retention strategies cannot keep pace with trial demand, regulatory changes, data analysis, patient needs, diversity, and site burdens -derailing efficiency. This is where AI, defined by technology that allows computers to perform tasks that typically require human intelligence can help. By harnessing Large Language Models (LLM), machine learning, AI assistants, AI automation and more, we can deliver greater, faster outcomes for patients.
Finding patients and predicting recruitment success
Increased patient privacy regulations, restrictions on health-related advertising, and health data declassification requirements limit both the speed and effectiveness of traditional patient recruitment strategies. When combined with extensive eligibility criteria, particularly for rare indications, finding eligible and consent-ready patients becomes even more challenging.
Researchers now need to expand beyond conventional digital data sources to include medical records, lab results, pharmacy data, and genomic information to identify patients. However, this process is incredibly time-consuming and still offers little insight into a trial’s success.
Large Language Models (LLM) accelerate this process by extracting relevant details from unstructured data. When combined with predictive analytics and machine learning driven by historical data, researchers can not only identify eligible patients more efficiently but also predict trial success rates.
Outreach, engagement, and patient support
It’s important to BOLDLY call that AI is incapable of empathy. AI does not have the ability to comfort a cancer patient or ease a caregiver’s worries. However, AI-powered assistants can answer patient questions and deliver automated reminders. Well-informed and educated patients are more likely to comply with protocols and provide trial consent.
AI can also refine trial protocols to better engage required patient populations. It can translate patient communications to accelerate enrollment in global and multi-country studies. Additionally, AI-powered tools can define, measure, and track diversity goals, helping to minimize diversity-related delays and ensure that medications are safe and effective for all people.
Reducing site burden
Administrative burdens associated with compliance, data entry, and eligibility screening strain site resources, delay processes, and distract from patient care. These challenges negatively impact the patient experience and increase the risk of patient dropout.
AI-powered automation eases site burden, speeds up the recruitment timeline, and improves patient retention—ultimately enhancing the efficiency and success of clinical trials.
AI – a promising perspective
Clinical trials and clinical research drive healthcare breakthroughs. Aside from discovery they ensure that new treatments and therapies are safe, effective, and accessible. By integrating AI-driven solutions with human compassion and expertise, researchers can enhance efficiency, increase impact, and ensure breakthroughs reach those who need them most.
Image Source: ID 126727990 | Artificial Intelligence Medical ©
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Fred Martin
Fred Martin, the CEO of SubjectWell, has a vision for improving global patient access to healthcare. With the most comprehensive and patient-centric solutions for patient recruitment, Fred and the SubjectWell team are challenging the clinical world to rethink patient recruitment.
