Clinical trials are important for making new medicines and treatments, especially for diseases like cancer. But only about 7% of cancer patients join clinical trials now. Studies show that up to 50% of patients would join if they were properly matched and informed. Finding the right patients is hard and has mostly been done by hand. Doctors and researchers have to look through patient records to see who qualifies.
In cancer trials, the rules to join are often very detailed. These include things like the type of disease, certain markers in the body, past treatments, genetic information, and other medical facts. Most of these rules—over 80%—are in patient notes written by doctors inside electronic health records (EHRs). These notes are not easy to search by hand, which makes the process slow and expensive.
Hospitals and drug companies spend billions of dollars every year to find patients for trials. Because of this, improving how patients are found is very important. AI-driven matching can handle large amounts of data quickly. It finds suitable patients much faster than old methods.
Artificial Intelligence, or AI, especially machine learning and natural language processing, is playing a bigger role in matching patients to trials. AI looks at different types of patient data—from organized data like lab results and medicine history to unorganized notes, genetic information, and images.
For example, Carta Healthcare’s AI platform can quickly analyze all types of EHR data to find cancer patients for trials in seconds instead of days. A case study from UPMC Hillman Cancer Center showed that their AI software matched patients seven times more often and doubled enrollment rates compared to manual work. This shows AI can speed up matching and improve the number of patients joining trials.
AI can spot patterns in many types of medical data to check if patients qualify. It works with knowledge from doctors to give reliable matches. This lowers the chance of missing potential patients and helps researchers reach enrollment goals faster.
AI systems are strong because they can combine data from many places. For example, Tempus has a database with over 8 million research records. This database mixes clinical, genetic, molecular, and imaging data. This mix of data helps AI get a full picture of each patient and match them to trials more accurately in real time.
Tempus works with about 65% of all academic medical centers in the US. It also supports more than half of US cancer doctors with gene sequencing, trial matching, and research. Tempus partners with top drug companies and cancer groups to help design trials that carefully target the right people.
ConcertAI’s Precision Suite uses real-world data from EHRs, insurance claims, and social factors that affect health. It uses generative AI to adjust how patient groups and trial rules are defined. This helps trials meet diversity goals and stay on schedule.
These platforms manage huge amounts of data—Tempus handles over 300 petabytes. They analyze data continuously to help choose trial sites, find patients, and make quick changes to trial plans when needed.
A big step forward is using AI to match patients by biomarkers. Biomarkers are biological signs found in blood tests, gene data, or images. They help predict how well a patient might respond to a treatment.
Data from the Personalized Medicine Coalition shows that treatments based on biomarkers raise response rates from about 20% to almost 42%. AI looks at complicated biomarker patterns and combines genetic, protein, and trial data to better tailor treatment.
WCG, a clinical trial group, says that AI and machine learning help researchers create personalized treatment profiles. They find the best patients for biomarker-based trials and cut down on trial-and-error in choosing treatments. AI also keeps track of changes in biomarkers and updates treatment plans or trial eligibility in real time.
This way of personalizing medicine helps both patients and research teams by improving care and making trial enrollment faster.
AI also helps run digital trial operations beyond just patient matching. ConcertAI’s Digital Trial Solutions (DTS) platform covers the whole trial process—from planning and site selection to patient matching and automating workflows.
DTS uses live dashboards and site performance data with AI predictions about patient enrollment and study timelines. This helps sponsors avoid trial sites that perform poorly and reduces costly changes. The platform also keeps screening patients automatically, ranking them based on clinical and social health factors to reach trial diversity goals.
Leaders at Bristol Myers Squibb (BMS) say that digital trial platforms speed up patient access to new cancer drugs and are likely to become standard in cancer research.
By automating routine data tasks and improving trial design with AI, these platforms lower the workload on research sites while keeping data quality and meeting regulations.
AI combined with automation improves how clinical trial matching works. There are tools to automate checking and confirming data quality, which is very important in clinical trials where rules are strict. Accurate data and timely reports are needed for success.
Companies like Agilisium offer clinical trial management systems with AI-based automation. This allows real-time tracking of trial progress, automatic compliance checks, and easier patient communication using remote monitoring technologies. The system also sends reminders for visits or medication based on patient data, helping patients stick to their plans and stay in trials.
AI-powered agents also help clinical teams by quickly analyzing data, choosing trial sites, and changing patient recruitment plans as needed. These technologies reduce paperwork and manual work so administrators and IT managers can focus on bigger tasks.
Medical practice administrators and IT managers are important in bringing AI-driven trial matching into their organizations. Successful use means integrating AI systems with current EHRs while keeping data safe and following privacy laws like HIPAA and GDPR.
Platforms like ConcertAI’s Precision Suite use APIs to fit easily into existing IT systems. This limits disruptions and helps set them up quickly. Training staff to use simple AI interfaces and chat systems makes these tools easier to use.
Administrators should also connect clinical, research, and IT teams to support automated workflows, remote patient monitoring, and live data tracking. Investing in AI technology improves patient recruitment, cuts costs, and raises the quality of trials for healthcare providers.
Healthcare providers, AI developers, and drug companies are working together to improve clinical trial matching in the US. Companies like Tempus, ConcertAI, Carta Healthcare, and Agilisium show through partnerships and data how AI tools help enroll more patients and simplify research.
Tempus reports over 200 partnerships with drug companies and wide AI use by academic medical centers and cancer specialists. AI-driven platforms are moving from experiments to common practice. Early results, like doubling enrollment rates at some centers, show clear benefits from AI.
The growing use of AI for personalized medicine, biomarker matching, and workflow automation points to ongoing improvements in clinical trial efficiency and patient care across the country.
Healthcare providers and those involved in clinical trials who want to boost patient enrollment, reduce delays, and improve research quality should think about adding AI-driven data analysis and automation to their work. These tools are becoming essential for solving patient enrollment challenges and helping bring new, safer treatments to patients faster.
AI-enabled precision medicine uses artificial intelligence to enhance patient care by accelerating the discovery of new treatment targets, predicting treatment effectiveness, and identifying suitable clinical trials, ultimately allowing for earlier diagnoses of various diseases.
AI can help healthcare providers make more informed treatment decisions by analyzing large volumes of data, identifying care gaps, and providing tailored insights that lead to better patient outcomes.
AI can efficiently handle high call volumes, reducing wait times for patients, streamlining appointment scheduling, and improving overall patient engagement, which enhances the patient experience.
AI assists in clinical trial matching by analyzing patient data and identifying individuals who may qualify for specific trials, increasing the chances of successful enrollment and outcomes.
Tempus partners with over 95% of the top 20 pharmaceutical companies in oncology by providing molecular profiling and data-driven insights to enhance drug development and treatment personalization.
Tempus utilizes multimodal real-world data, including genomic, clinical, and behavioral data, helping to provide comprehensive insights into patient care and treatment options.
AI improves patient care by enabling high-quality testing, efficient trial matching, and deep analysis of research data, all contributing to better patient outcomes.
Olivia is an AI-enabled personal health concierge app designed for patients and caregivers to help them manage, organize, and proactively control their health data.
Tempus launched a collaboration with BioNTech for real-world data usage and received FDA clearance for its AI-based Tempus ECG-AF device to identify patients at risk of atrial fibrillation.
AI accelerates the identification of novel therapeutic targets, enhancing the speed and accuracy of treatment development in precision medicine, which is critical in improving patient outcomes in complex diseases.
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