Leverage NLP/AI to Assist Clinical Trial Design and Recruitment
Clinical trials are research studies that evaluate the effects of treatments (e.g., vaccines, drugs, medical devices) on human health outcomes. A clinical trial is often considered as a gold standard to see if a new treatment is more/equally effective and/or has less harmful side effects than the standard treatment. To recruit a representative and clinically meaningful population, usually defining target cohorts through eligibility criteria in trial protocols, is a crucial step to conducting a successful clinical trial. However, suboptimal eligibility criteria (excessive or overly rigid) may lead to low trial accrual, restrict patient access to investigational treatment, and limit the generalizability of treatment to the broader population of patients.
A Crucial Representation of Clinical Trials
The selection of criteria is important for defining a more homogeneous population to better understand an intervention’s effects and to safeguard against undue harm. However, suboptimal criteria selection can lead to low accrual, resulting in trial incompletion. Furthermore, overly strict criteria may reduce the potential relevance for patients outside the trial that could otherwise benefit from the intervention. Balancing between these parameters remains challenging when planning future clinical trials.
Recruiting eligible participants is considered one of the biggest barriers for the successful implementation of clinical trials. A major bottleneck in recruitment is eligibility screening, a process where clinical research staff (eg, clinical research coordinators) reviews patients’ medical history for demographics and clinical conditions, collates and matches the patient data to trial eligibility criteria, and identifies eligible patients. Prescreening patients through electronic eligibility has been pursued as a potential solution by leveraging natural language processing (NLP) to automate cohort query generation from criteria text and query execution against medical records data.
As illustrated in the figure above, we have developed and implemented a clinical trial optimization platform, VITAL, empowered by NLP to prescreen and validate patients from real-world clinical settings for clinical trial design, optimization, and recruitment.
Conclusion
In conclusion, integrating NLP and AI into clinical trial design and recruitment streamlines the process and enhances the representativeness of the participant pool, leading to stronger trial results. Our platform, VITAL, demonstrates the potential of these technologies in revolutionizing medical research.
Request a demo today to learn more about leveraging NLP/AI to assist in clinical trial design and recruitment!
