How to Leverage AI for Effective Patient Engagement

Clinical research is transforming at an incredible pace for a variety of reasons, with factors such as the need for more inclusive and diverse clinical trials leading researchers to seek out new solutions.  The challenge lies in identifying patients from all over the world who meet, in many cases, complex eligibility criteria. Study teams must find effective ways to narrow down the search to enroll diverse cohorts within reasonable timeframes. Artificial intelligence (AI) can help by leveraging diverse datasets including both structured and unstructured data to create accurate patient profiles, providing crucial details like age, gender, race, and location as well as medical histories. This data can provide study teams with a better understanding of patient behaviors and preferences, enabling more effective outreach and education strategies.

How Does AI Help Clinical Trial Recruitment?

AI in clinical trial recruitment focuses on identifying patients who meet specific eligibility criteria for various studies and using data to give study teams insights into patient behaviors that may impact the recruitment process. This involves complex data analysis, pattern recognition, and predictive modeling. AI tools like BEKhealth’s purpose-built natural language processing (NLP) model integrate multiple data sources to streamline the recruitment process, making it faster and more cost-effective than traditional, labor-intensive approaches.

Data Integration and Patient Profiling

The heart of AI-driven recruitment lies in its ability to amalgamate and analyze vast arrays of data from disparate sources. These sources include electronic health records (EHRs), genetic data, patient registries, data from wearable health devices, and many more. AI systems employ NLP to sift through both structured data (from sources like EHRs and patient registries) and, importantly, unstructured data like hand-written physician notes. For example, hand-written notes are typically uploaded into a patient’s EHR as a digital image from a scanner or even just a picture from a smartphone. AI can be extremely useful in analyzing these documents and accurately extracting relevant information, overcoming challenges related to handwriting legibility and inconsistent terminology used by healthcare providers.

For instance, an AI tool might scan thousands of de-identified patient records to compile profiles that include specific characteristics, such as age, disease stage, or treatment history. This is helpful in identifying candidates more likely to meet eligibility criteria but can also be used to predict patient behaviors, such as adherence to study requirements, or likelihood of dropout. For example, the AI can analyze data from multiple sources, finding information like co-morbidities and past healthcare outcomes along with demographic data, then compare that against historical benchmark data (from past studies, patient registries, etc.) to predict how patients fitting certain data profiles are likely to perform throughout a clinical trial. This helps teams to eliminate likely non-compliant patients earlier, allowing more focus to be given to more eligible patients.

Once relevant data is extracted, AI tools use machine learning algorithms to create detailed patient profiles. These profiles are not static; they are dynamic, updating and becoming more accurate as new data becomes available. This dynamic profiling allows trial recruiters to understand not just the medical aspects of a patient’s profile but also socio-economic factors that might affect trial participation, such as geographic location, access to transportation, presence of likely comorbidities, etc.

The Future of AI in Clinical Trials

As AI technology advances, its potential to further enhance patient recruitment strategies grows. Future developments might include more sophisticated and accurate predictive models that can better assess patient compliance and likelihood of long-term participation, leading to more qualified applicants entering the recruitment funnel and, likely, much faster recruitment phases. Additionally, AI could be used to help monitor enrolled participants, using real-time data to look for specific markers or flags that may impact patient safety or lead to a dropout.

By integrating and analyzing diverse data types, AI tools can create accurate and comprehensive patient profiles, empowering research teams with the information they need to more easily find and enroll eligible patients. No matter where they are in the world, BEKhealth’s purpose-built AI model for patient recruitment can help teams to know who their target patients are, where they are, and how to reach them, facilitating more effective outreach and education strategies.

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