There are a number of innovative tools and technologies which are enabling precision patient finding. From AI to e-consent, here we explore how they are changing the face of the clinical trial landscape.
AI and predictive modeling tools can be applied to patient data to predict clinical outcomes and responses to investigational treatments using a combination of lifestyle data, EHRs, genomic data, and more. These models can assist clinicians in selecting participants who are more likely to benefit from the treatment or, conversely, experience adverse effects.
As patient recruitment can take up to one-third of a study timeline, creating optimizations using AI tools can have a significant impact on the time it takes for new therapies to reach patients. For example, in one study, an AI analysis system was applied to a clinical trial for a new lung cancer treatment with the aim of assessing how adjusting the criteria for participation impacted patient risk and safety. The researchers found that the AI system was able to suggest adjustments that would double the number of eligible participants without increasing risk to patients.
Applying AI and machine learning (ML) models can help produce study criteria that optimize the chances of reaching enrollment goals while ensuring patient safety and cutting time to market. However, it is vital that such models are applied to high quality, standardized and accurate datasets so that the outputs are representative of real world patient populations and do not produce misleading results.
Real-time data integration involves near instantaneous updating and processing of data and minimizing the delay between data generation and its availability for use. Unlike traditional batch processing, which collects and processes data periodically in predefined chunks, real-time integration operates on a near-instantaneous basis. In the context of clinical trials and patient finding, real-time integration theoretically enables instant analysis of the latest genomic data and EHRs to identify relevant patients and increase understanding of treatment responses and health outcomes.
This type of integration also combines data from multiple sources to provide a comprehensive overview of a patient, their eligibility criteria, and progress in the screening and enrollment process. Ultimately this leads to enhanced decision making and an improved patient experience, not only during initial screening and enrollment, but throughout the entire clinical trial. However, it is vital that the relevant sources are formatted in such a way that interoperability and functionality enables both seamless and secure access and analysis. Such vast, comprehensive datasets must always protect patient anonymity and be compliant with HIPAA and other relevant data security regulations.
Access to such up-to-the-minute data also enhances patient safety, enabling dynamic decision making regarding a patient’s eligibility and offering a clear overview of interconnected factors which may impact an individual’s risk and response to a novel therapeutic.
Collection and analysis of genomic data is playing a key role in transforming how patients are matched to clinical trials and research opportunities. Use of genomic data enables more precise patient stratification, helping identify patients with rare genetic variants and enabling trial and therapy matching based on genetic characteristics.
Participation in genomic testing and genetic counseling initiatives allows for the possibility of recontacting patients—with their consent—for future research that leverages their existing genetic data. Patients who agree to be recontacted based on their genomic profiles significantly contribute to reducing the costs associated with patient finding. This approach also substantially shortens trial timelines, as suitable patients can be identified using existing genetic information without the need for additional genetic screening.
The cost of whole genome sequencing (WGS) has also dropped dramatically since the publication of the first human genome in 2003 – from an estimated $2.7 billion to around $600 in 2024. With speed of delivery increasing rapidly and the cost of this type of genetic test decreasing, offering WGS to patients is becoming more accessible than ever before. WGS also reduces the need for each individual trial and organization to shoulder the cost of running single gene, panel, or exome screening, providing a comprehensive overview of a patient's entire genome with a single, one-off test.
If shared and securely stored on a centralized database (where it can also be integrated with EHRs), this data can significantly reduce cost of patient finding by providing enhanced stratification which enables rapid identification of highly relevant patients.
Digital platforms and e-consent tools are also contributing to precision patient finding efforts, enabling either partial or complete from-home screening and participation.
Online e-consent not only streamlines the consent and enrollment process, but reduces barriers to entry for eligible participants. Time commitment and the need to travel are frequently cited as some of the biggest concerns and blockers for patients when it comes to participating in clinical trials. E-consent removes the need for complex paperwork distribution and management and provides a centralized, easy-to-follow process which patients can complete from home at their convenience.
Digital participation platforms provide a single source of knowledge for participants throughout a trial, where they can check for updates, next steps, and access educational information to understand their contribution and its impact - ultimately helping to reduce drop-out. For example, less than 1% of participants who join a Sano Virtual Waiting Room drop out, compared to the industry average drop-out rate of 30%.
These tools help to deliver a unified, high-quality patient experience and a space where participants can easily direct their questions and queries to the study team. Collectively they combine to provide an easy-to-use digital interface and single source of truth for participants, serving to build trust and enable timely communication, which in turn contributes to reduced risk of drop-outs and trial failure.
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