Use AI to precisely find eligible patients in real-time EMR data

Mine data across 25+ major health systems, including 1K+ facilities, and connect directly with sites to recruit faster.

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De-risk your protocol and select the right sites

  • Assess cohort availability and DE&I based on your inclusion and exclusion (I/E) criteria
  • Evaluate the presence of each inclusion and exclusion (I/E) criteria in structured and unstructured EMR data
  • Select sites with eligible patients today and let us connect you to those site

Get continuous visibility into patient matches and their recruitment status for clinical trials

  • Find patients that precisely match your I/E criteria using AI to mine structured and unstructured EMR data that is refreshed daily
  • Get continuous visibility into the recruitment status of AI-matched patients at each site

Get direct access to sites and reduce staff burden

  • Connect with sites and get high-touch access to support recruitment acceleration
  • Accelerate accruals at the site by sharing AI-matched, screen-ready patients with IRB-approved staff to identify and validate
  • Encourage treating physicians to refer patients to in-network principal investigators (PIs)

Texas Tech University Health Sciences Center uses AI for clinical trial recruitment

John Griswold, M.D., executive director of the Texas Tech University Health Sciences Center (TTUHSC) Clinical Research Institute, spoke with KCBD-TV about how artificial intelligence (AI) will help researchers precision match participants to trials faster and ultimately improve patient outcomes.


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How does patient matching software with AI help accelerate patient recruitment in clinical trials?

It mines EMR data to instantly match patients to trials.The traditional methods used to find patients for clinical trials are imprecise and the process is time-consuming and burdensome for sites, which slows down recruitment. Traditionally, finding eligible patients for a trial is done by manually searching electronic medical records (EMRs) for structured codes (e.g., LOINC, ICD-10, and SNOMED) or performing keyword lookups. However, not all eligibility criteria map to a code and documentation can be inconsistent in the EMR (for example, ‘triple negative breast cancer’ may appear in text as ‘TNBC,’ ‘triple negative BC,’ ‘breast tumor – TN,’ and so on). Next-generation, AI-powered solutions deliver unprecedented matching speed and precision to accelerate clinical trials. They tap into large data sets, including structured and unstructured information from EMRs, genomic reports, and other clinical data to match patient populations to complex eligibility criteria in minutes. AI powered software can also precisely find patient populations that have specific genetic markers and identify patient populations with ‘worsening metastatic disease’ or ‘new mass’ documented in clinician notes or lab reports to determine disease progression.

How does patient matching software with AI improve diversity in clinical trial patient recruitment?

Commonly, patient matching solutions provide a race and ethnicity breakdown of patient populations at the cohort level to help sponsors optimize their protocol to meet DE&I goals for their clinical trials. However, to drive actionability, it is important to leverage more advanced software that can provide this breakdown at the patient level for IRB-approved site staff to help diversify patient recruitment too. Additionally, advanced patient matching software that mines EMR data from community and rural sites that serve diverse patient populations, as well as academic medical centers that serve predominantly White patient populations, can help find more diverse patients for clinical trials. And, it is equally important that the software allows treating physicians to refer diverse patient matches from community and rural sites directly to the principal investigator, to further empower sites to diversify patient recruitment.

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