Using AI to Transform Your Clinical Trial Recruitment Process

Clinical trial recruitment must evolve to keep pace with advances in the broader scope of clinical research. To make this happen, researchers need to sort through an immense volume of patient data to determine where to look for eligible patients and how to successfully recruit them into studies. How do we analyze all this data in order to derive the insights we’re looking for to make fast, smart choices? This is where Artificial Intelligence (AI), which can be used to inform clinical trial eligibility criteria, enhance the diversity of participants, reduce sample size requirements, and more becomes critical.

Managing Expanding Data Sources

Traditional methods of data processing are proving inadequate for handling the sheer volume and variety of data now available. From electronic health records (EHRs) and genomics to wearables and patient-reported outcomes, each data type brings unique complexities. AI excels in managing this diversity, leveraging advanced algorithms to sift through data quickly and efficiently.

Without AI, the task of combing through such extensive datasets to find eligible trial participants would be not only time-consuming but also prone to errors. AI’s capability to quickly extract actionable insights from vast datasets can make the process faster, more accurate, and more efficient.

Finally, a Specialized AI for Clinical Trial Recruitment

While general-purpose AI solutions can process a broad range of data, they often lack the specificity needed for clinical trial recruitment. BEKhealth’s AI excels in two key areas: handling both structured and unstructured medical data. Structured data, such as EHR data, is highly organized and easily searchable. However, unstructured data, which includes things like physician notes, imaging data, and more, are equally useful in creating detailed profiles of patients that demonstrate if they will make strong study candidates. The BEKhealth AI system has been developed specifically to interpret these diverse data formats for patient recruitment. Because BEKhealth designed its natural language processing (NLP) specifically to look for terms and details useful for patient recruitment, it can extract relevant information more accurately and completely than generalized AI systems.

By accurately and quickly identifying suitable participants, this AI minimizes the time and resources spent on unsuitable candidates, accelerating trial startup processes. This also reduces the burden on members of site-based study teams. Because the AI can find more likely-eligible patients and disqualify ineligible patients earlier in the process, site teams will have fewer unqualified patients to vet, allowing them to focus on the stronger candidates.

Better Engagement, More Access & Diversity, More Control of the Process

The benefits of implementing AI in clinical trial recruitment extend beyond just efficiency and accuracy. AI-enabled recruitment strategies can significantly improve patient engagement and retention by ensuring that selected participants are the best fit for the trial, based on parameters set in the study design. These parameters help to create distinct personas of ideal study participants that the AI tools can then work against. By focusing on finding patients that fit these profiles, study leaders can build more engaged, adherent patient groups and reduce dropout rates. Additionally, AI helps researchers to expand their participant search all over the world in order to enroll more diverse and representative patient pools. This kind of expansion would have been difficult to manage just a few years ago, both in terms of cost and labor. Now, remote data collection technologies simplify getting the data from patients wherever they live, and AI solutions are able to quickly translate, format, and generate insights from that data.

Moreover, AI-driven analytics can provide ongoing insights over the course of a trial’s recruitment process, allowing for real-time adjustments and proactive management of recruitment strategies. Study teams can quickly understand what strategies are working and which are not, then make swift course changes in terms of messaging and communications channels.

Conclusion

As the volume and complexity of data informing the clinical trial recruitment process continues to increase, AI is fast becoming an essential partner for researchers. Specialized AI solutions like those from BEKhealth are particularly valuable, as they are designed to navigate the specific nuances of both structured and unstructured patient data to help streamline the recruitment process. By integrating these advanced AI technologies, research teams can ensure their clinical trials are as effective, efficient, and inclusive as possible, accelerating study starts and getting new therapies to patients faster.

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