Life Sciences Companies Are Using AI to Empower Sites to Recruit Patients Faster

Life Sciences Companies Are Using AI to Empower Sites to Recruit Patients Faster

Patient recruitment has always been one of the major challenges in clinical research, with many trials failing to find and recruit the right patients. With AI, we can now contextualize patient journeys from unruly electronic medical record (EMR) data with unprecedented speed and precision.  

When recruiting for a trial, research staff must compare patient data against the trial’s inclusion and exclusion (I/E) criteria to find potential matches. However, more than 80% of patient data stored in EMRs is unstructured, such as physician notes, imaging, lab reports, family history, etc. Using traditional methods, this unstructured data is much more tedious and time-consuming to sort through versus coded, structured data. There are many medical terms that don’t have a standardized code and may be documented differently depending on the clinician or healthcare organization (HCOs. For example, muscle-invasive bladder cancer (MIBC) does not have a specific code and may appear in notes as ‘muscle invasive bladder cancer’, ‘MIBC’, ‘advanced bladder cancer’, ‘invasive bladder cancer’, ‘high grade bladder cancer’, ‘advanced urothelial bladder cancer’, etc. We did a test in the Deep 6 AI software to see how precisely we can find patients with MIBC. First, we searched MIMIC EMR data at a single health system by code only. We found 40,000 patients with ‘bladder cancer’ using the ICD-10 code C67. Then, we applied AI and mined clinician notes and disparate lab reports to find patients with ‘muscle-invasive bladder cancer’. AI-assisted chart review identified 10,000 patients with MIBC, pinpointing MIBC four times more precisely than by searching coded EMR data.

Not only does it improve precision, AI also improves matching and validation speed. Manual chart review typically takes about four hours of work to identify one patient. Research staff need to manually review the charts of four patients with bladder cancer to find the one patient with MIBC. Using AI-assisted review, it takes less than two minutes to find a patient with MIBC. Therefore, validating patients is 100 times faster using AI. 

Clinical stage cellular immuno-oncology company accelerates recruitment for a non-small cell lung cancer trial

AI has had a tangible impact optimizing study design, improving feasibility, and accelerating recruitment at clinical research sites. One HCO was recruiting for a clinical stage cellular immuno-oncology non-small cell lung cancer trial. Prior to using the Deep 6 AI software, they were manually reviewing doctor’s notes in the EMR which resulted in recruiting zero patients in five months and taking each staff member eight hours per week to screen patients. Using Deep 6 AI, they enrolled 124 patients in five months and reduced the time spent screening to two hours a week. Screening four times faster saved the HCO $52K in less than one year.

Pharmaceutical company helps their sites accelerate chart reviews and enrollment for a Phase II dermatology study

Another site was conducting a Phase II dermatology study of a topical treatment for an industry study of a topical treatment for acneiform rash. They were able to accelerate chart reviews and enrollment using AI, with 35% faster enrollment and four times faster screening after switching from manual processes.

Academic Medical Center reduces average recruitment duration by seven months using AI

A premier oncology academic medical center (AMC) evaluated 40 studies to compare recruitment progress before and after AI was used to identify patients. They found that recruitment was streamlined after shifting away from the manual chart review process. Using Deep 6 AI, they were able to demonstrably reduce the gap in their recruitment goals and reduce the time spent recruiting for a trial by seven months, on average.

Through these real-world examples, we can see how AI is precisely matching patients to complex I/E criteria and surfacing hard-to-recruit patient populations. Learn more about the Deep 6 AI Recruitment Acceleration solution here

About The Author

Nour Malki