Innovations in Patient Matching


Applied Clinical Trials

Applied Clinical TrialsApplied Clinical Trials-08-01-2016
Volume 25
Issue 8

More precision models in connecting patients to clinical trials and treatment options are vying to close the ever-elusive recruitment gap.

Two years ago, Applied Clinical Trials looked at the technologies intending to close the ever-elusive patient recruitment gap. In that time, other innovative approaches have emerged, four of which are briefly detailed below. 

Patient iP. Patient iP is a platform that securely de-identifies and aggregates electronic health record (EHR) data so that clinical trial protocols can be automatically processed to more quickly identify where and how many patients match the inclusion/exclusion criteria requirements. Michael J. Margiotta, CEO, told Applied Clinical Trials, “EMRs are just a repository of patient data. Those systems don’t capture data in a way that can be aggregated or analyzed and perform data mining on the patient populations.” This is where Margiotta stepped in-to provide a platform that would be able to leverage EMR data in a way the software currently can’t. In 2014, he launched his company to be able to match patients to specific criteria based on aggregated information including genetic markers, blood values, medications, and more to find those exact patients very quickly. Think of it as an EMR booster.

For contract research organizations (CROs) and sponsors, they can use Patient iP for protocol modeling-making sure patients actually exist for the protocol they have designed; as well as site feasibility. Sites can quickly know how many patients in their networks are potential participants through the EHR. Or for practices considering clinical research, they can find out how many patients in their practice are eligible for a current protocol. 

ePatientfinder. Tom Dorsett, CEO, believes that though many solutions for patient recruitment in clinical trials have emerged, there exists a lack of actionable models for getting those patients into clinical trials. And here is where his solution comes in. ePatientfinder uses a three-tier funnel or level of screening to find the highest quality referrals. The funnel includes ePatientfinder sending potential trials with patients to a physician through the EHR. If the physician opts in, ePatientFinder reaches out to patients initially to see if they are interested, then provides an IVR pre-screen survey to uncover any subjective issues that may not be in an EHR. Those patients are then referred to the opted-in physician for a consultation.

According to Dorsett, the platform builds on the trust inherently found between patient and doctor, and is a process that keeps the physician in the drivers’ seat, which Dorsett says they appreciate. In addition, the company has been achieving the best quality referrals to sites, and has feedback from the sites themselves that the three-tier screening provides very high conversion rates. 

MM LAB. In March 2016, MolecularMatch, a cloud-based, clinical informatics company that works with labs, hospitals, genomic cores and physicians to connect cancer patients to treatment options, launched its MM LAB software for pathology labs and others to match patients’ test results to personalized cancer treatments, including clinical trials and experimental drugs.

MolecularMatch offers a public-facing website for people looking for oncology treatments, searchable by diagnosis, specific gene mutation, comorbidities and more. The data behind the search is culled from web-based information sources including, registries, institutions, PubMed abstracts, COSMIC and more. It is fully automated to create structured data from unstructured sources.

According to Xuan Shirley Li, PhD, Chief Scientific Officer of Molecular Match, the MM LAB software was a natural next step for the company’s offerings. MM LAB generates a customized report from its culled data of specific trials and treatments, based on the specific markers that come from tumor testing. Basically, for labs, the software can be used to generate a value-add service for those physicians or health networks.

Quintiles. The company’s precision enrollment model, which is comprised of a network of 100 U.S.-based oncology centers, is designed to accelerate patient recruitment using pre-identified patients based on study and biomarker criteria, across broad geographic areas, and incorporating EHRs and other data sources. In this newly-launched model, patients, upon entering the network, have their tumors tested. The genomic analysis and alterations of these tumors are reported back to the patient and site and can be matched to protocols using the genomic alteration criteria for the protocol. It isn’t until a qualified patient is identified that the site is activated. 

In this article, Jeff Ventimiglia, Director, Site & Patient Networks, Quintiles, explains that study start-up time is reduced because the site previously joins the Quintiles network and fills out all the documentation and service agreements and joins the Quintiles Infosario Site Gateway. A site is activated once the patient is identified and the remaining start-up activities take 21 days. 

Also, a recent pilot conducted by Quintiles suggests potential to increase screening rates and shorten timelines for clinical trials by providing a broad genomic panel rather than using a single biomarker.

Read the full version of this article here.

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