Paradigm Health’s CEO Kent Thoelke tells Fierce Pharma how they are using large language models to reduce the labor required for community and rural health systems to participate in oncology clinical research and integrate into physician workflows

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Q: How does it actually work?

KT: We build data pipes into healthcare systems. We operate under BAAs, so we're an extension of the physician and the healthcare systems and have the same privacy constraints as the physician or the study staff. Our platform parses medical records while we simultaneously digitize clinical trial protocols to then match patients to the criteria for the trials. We automate most of this process with the last mile being where we still have a human in the loop. We're not replacing the physician, we’re providing the physician all of the information for why a patient might be a good fit for a clinical trial.

In the last two years, with the adoption of large language models and generalized artificial intelligence, machine learning has significantly changed. Previously natural language processing was roughly 40% sensitive, which resulted in a big top of the funnel and still required heavy manual lifting to do the final matching. Today, with large language models, we operate with 95-98% sensitivity. We can screen hundreds of thousands of patients in the same time it takes a human to do a handful.

Q: How are you integrating into the existing workflow to alert physicians for potential patient matches?

KT: When we ingest all of the medical records of a health system, we are also ingesting all of the scheduling data. Now we know when a patient is coming in for a particular procedure so that we can alert the physician a week ahead of time that they might be eligible for a trial.

Currently, the existing model is very reactionary. A physician sees a patient, they progress and they become eligible for a trial. That requires someone on staff knowing that a trial is available, having pre-reviewed that data and eligibility criteria and then presented it to the physician. There are so many pieces in that process that require luck.

Q: How are you leveraging data and LLMs to help sponsors?

KT: Because we have all the data for community and rural oncology patients, we’re leveraging it to help trial sponsors optimize their protocol development to better reflect actual care in the community. So whatever the care paradigm is for a particular patient in the community and rural setting, we help pharma companies match their trials. That's important, not only because it makes it easier to find and match patients, but it also lessens the work needed for quality data capture, and most importantly, lessens the burden on the patient.

Patients in rural or community settings might be of lower socioeconomic status, don’t have paid time off, etc. If we want to make clinical research a care option for them, the barriers to entry can’t be so high that they can’t actually participate. A protocol can’t have multiple visits, lab draws, or scans, that wouldn’t be part of their normal care.