A leader from the cancer patient matching and analysis solutions firm discusses how and other forces are pushing innovation in the oncology research space.
Outsourcing-Pharma (OSP) talked with Marie Lamont (ML), president and chief operating officer of Inteliquet, about the company, challenges impacting cancer research, and what the future might hold for the industry.
OSP: Could you please tell us about Inteliquet—who you are, what you do, key capabilities/specialties and what sets you apart from the competition?
ML: Inteliquet is a leader in the clinical trial patient matching and data analytics space. Inteliquet’s cloud-based OncWeb platform securely, accurately, and quickly aggregates and analyzes healthcare data, helping to:
- Ensure clinical trials are designed more effectively,
- Patients are matched to trials more rapidly, and
- Support patient-care decisions with real-world evidence.
Our solutions benefit cancer centers, treating physicians, researchers, life science firms, and patients. At our core, we match patients to clinical trials, and thereby bringing more treatment choices to physicians and patients.
For healthcare organizations, the combination of Inteliquet technology, data analytics and clinical support enable dramatic efficiencies. Cancer centers benefit the most with complete patient match and full transparency. This helps to reduce their time and costs involved in screening, matching and evaluating which trials to open.
For life-science organizations, such as biopharma/sponsors, CROs and clinical development consultants, the company’s services and analytics which are identified from the clinical data consortium accelerate clinical research process, from feasibility to screening and matching to commercial launch. A recent case study noted significant improvement in results using Inteliquet’s solutions which includes three-times faster patient enrollment, including into hard-to-enroll trials; 30% faster clinical trial feasibility and site start up; and nearly two-times faster site activation.
What we do better than anyone else is:
- We are EHR agnostic: we can connect into and extract data from any EHR or source system and are only interested in the data necessary to match patients to a clinical study.
- Effortlessly convert unstructured data to structured data.
- Sophisticated proprietary technology: We utilize NLP and multiple advanced parsers to enable precision matching.
- We conduct deep, detailed digitization of the entire clinical trial protocol inclusion/exclusion criteria (e.g., diagnosis, stage, histology, molecular and diagnostic laboratory results), helps identify patients in a single key stroke.
- We provide a population querying tool Inteliquery to allow a quicker to yes or to no trial decision for feasibility assessments.
- We augment our technology with experts: Analytics and dedicated clinical engagement expertise help ensure success with our software as well as help reduce administrative burdens.
OSP: Could you please share some of the key challenges associated that trial teams face when recruiting patients for oncology trials?
ML: There are three care options for oncology patients: on-label therapy, off-label therapy and clinical trials. Enrollment to clinical trials remains the most costly and difficult part of the process. In fact, approximately 50% of clinical trials don’t meet enrollment goals. Among the many barriers to enrollment are:
- Physician workload and Institutional Support are two of the top five barriers to clinical practice participation in oncology clinical trials.
- 56% of cancer patients do not have a trial available at their cancer center.
- There may be no available trial for the patient’s cancer type and stage at treatment site.
- Of the 3-5% cancer patients in trials, only 1% participate in the community setting, yet >85% received oncology care in the community setting.
- Only 60% of what is needed to match a patient to a trial is included in the EMR and more than 80% of EMR’s is unstructured data.
OSP: What solutions and tools do trials typically turn to when recruiting, and how might some of these methods fall short?
ML: Advertising for patients and Interrogating the EMR remain the top methods to identify patients for a clinical trial, but it is not without its challenges. There are data gaps:
- Spending money advertising for patients has a low return.
- As mentioned above, EMR does not contain all patient data or not updated to show longitudinal patient data.
- Structured vs. unstructured data.
- Estimates suggest up to 80% of all EMR data includes unstructured data.
- Molecular / genomic data may be missing or never added to EMR.
- There are interoperability and conformity limitations:
- Multiple EMR systems exist—even among the same care system.
- Limited interoperability among EMRs and other systems used in the clinical trial process.
- Lack of conformity of clinical trial eligibility criteria and EMR data structure.
- Requires detailed chart review.
- A lack of oncology-specific data:
- Not every institution has an oncology specific EMR module.
- Lack fields in some EMRs to collect oncology-specific information, such as new biomarkers.
- Difficult for EMRs to keep up with changes in oncology—which happen fast.
- EMRs designed for patient care—may not contain all important data frequently used for oncology eligibility screening.
- And as work toward precision medicine, the landscape only becomes more challenging:
- Inclusion/Exclusion criteria is becoming increasingly complex.
- New classes of therapeutics, such as targeted therapeutics and immune checkpoint inhibitors, have and continue to emerge.
- In many cases, patient inclusion into a trial is predicated on the presence, or absence, of a single molecular alteration, or dependent upon treatment naïve, disease stage, etc. Some trials require combinations of alterations to be present for a patient to qualify.
- Timing/window of an enrollment decision is vital. For example, a patient might not qualify after initiating certain treatments or a patient must fail first or second-line therapy before becoming eligible.
- Molecular data and other important data may not be integrated into the EMR or is often “buried” within it.
OSP: Similarly, could you describe the challenges cancer patients frequently face when looking for trials, and ways they try to overcome them?
ML: We don’t work with patients directly. However, studies have shown that that there may be many reasons patients don’t participate in a clinical trial, including fear of a reduced quality of life, concern about receiving a placebo, potential side effects, and concern that the experimental drug might not be the best option.
In addition, some patients may not know that a clinical trial is an option, or, as cited above, more than half of cancer patients in the US may not have a trial available where they are being treated, especially if they are being treated in the community setting where most patients get treatment. The single most influential factor in enrolling patients in clinical trials is physician influence.
One way, to help patients overcome access to a clinical trial is to make sure they are not overlooked in the first place—and we can help accomplish that.
OSP: How does your matching software work?
ML: OncWeb is a proven, self-service technology that securely identifies more eligible patients for trials, more accurately understands patient populations, and helps to better inform care decisions—as well determining a site’s suitability when considering a specific trial. The OncWeb platform ingests and conforms patient data, including unstructured documents and structured data from consortium partners and applies Inteliquet’s proprietary patient screening and matching algorithms to improve patient matching while reducing recruitment and administrative efforts. It also pre-screens patients nightly, which helps to make sure no patient is overlooked.
We also deeply digitize the entire clinical trial protocols. Then, through our sophisticated algorithms, our platform is able to digitally screen patients and match them to trials more rapidly, accurately and efficiently.
Inteliquet has created the Inteliquet Cancer Research Consortium, through which it partners with cancer centers and hospitals to create a proprietary database of cancer patient health records today covering over 380 oncologists and over 2.8m patient records, including over 850,000 oncology patients.
Inteliquet digitizes the detailed protocol inclusion and exclusion criteria of oncology clinical trials for cancer centers and sponsors to enable patient matching and analytics. Inteliquet has digitized 440+ clinical trial protocols to date with 283 of those trials currently enrolling patients and more underway.
OSP: How does your technology help overcome some of the challenges associated with recruitment and matching?
ML: Speaking about the trial side, enrollment to clinical trials remains the single most time-consuming aspect of clinical trial success. We discussed above many of the challenges associated with EMR and traditional recruitment methods.
The bottom line:
- Without the right information/data, the right patients can’t be identified.
- Not finding the right patients means:
- missed recruitment goals, which leads to failed trials.
- missed opportunity for the right targeted therapy for the patient.
- Failed trials not only incur significant wasted costs and resources, they deny patients much-needed options.
The ability to securely, normalize and analyze comprehensive health care data, digitize complex trial protocol, conduct nightly automated pre-screening, and then identify more patients more accurately for clinical trials, means we are directly addressing one of the most challenging aspects of clinical research—finding patients for clinical trials.
OSP: Let’s talk about the unique challenges created by COVID-19—what are the problems the pandemic and associated shutdowns has created?
ML: In the beginning, the effect of COVID on cancer centers and the broad community in general is that patients weren’t going into the center unless they were acute patients.
Biopharma recognized this and started pausing, delaying or stopping trials all together. At the same time, they also started to see clinical supply challenges, meaning getting clinical supply to cancer centers, because in some instances clinical supply was either not deemed critical or some of the resources around clinical supply couldn’t get to where they needed to be.
In the end, 60 to 100 biopharma organizations around the globe paused clinical trials. We all saw the effect of that because patients were not being enrolled in trials.
With cancer centers, some states prohibited during the pandemic procedures considered “elective” which included surgeries and biopsies. When patients aren’t going into centers, they may not be completing their diagnoses: e.g. blood and/or genetic testing including biomarker tests. So, we have seen a drop in diagnostics that has remained as a result of the pandemic.
What that translates to is treatments and therapeutics are down, because if you don’t have a diagnosis, you can’t migrate to the right therapeutic option. There is a concern that there is a backlog of incredibly needy patients that will need care and a large number of late stage cancer diagnosis.
An additional effect on cancer centers is revenue. Without patients coming in, their revenue is down. Patients not enrolled in trials means revenue is down. This has a compounding effect on cancer centers when both traditional revenue and clinical research enrollment revenue are down.
Increased costs of PPE, testing barriers and contact tracing have impacted their bottom line. As a result, some institutions have had to reduce resources only to struggle to bring their staff back. Some of those staff were diverted to be on the floor, and some have not come back, which forces the centers to focus on trying to find new staff.
From a treatment perspective, we have seen a return in some instances to the “old” standard of care as opposed to targeted therapies. If you are not getting the diagnostics: biomarkers and genomic sequencing, the only choice is back to the standard chemotherapy and radiation.
This has all kinds of implications from cytotoxicity to patients not receiving better, more targeted therapies because the diagnostics are not being done.
OSP: How does your technology help surmount these challenges?
ML: Again, we go back to the ability to rapidly, effectively and more accurately identify patients who may be eligible for a clinical trial. This was a huge challenge before the pandemic, and it remains a challenge during the pandemic.
OSP: How different do you think the oncology trial landscape will look in a few years? What changes or improvements do you think might lie ahead?
ML: If there is a “pearl” within the pandemic, like with any unexpected crisis, it forces behavior change. One pearl may be that we will be forced to adopt better solutions and to turn to technology and other digital health solutions that can promise to make better use of data and also are proven to produce better results for clinical trials, such as faster and more effective patient matching.
We have seen that the cancer centers we work with who are doubling down on the patient population insights technology we have for feasibility as well as our screening and matching tools are continuing to match patients to trials—some maybe even more than before.
This may force us to find a way to do matching better, which means that not only can we hope to catch up, but after the pandemic, we may even get ahead. My hope is that, instead of 3% of cancer patients on a trial, we could go higher much higher after the pandemic.
Inteliquet is an executive sponsor of CRAACO 2020, a virtual online event (scheduled November 4-6) focused on sharing trends discussing solutions around clinical trial as a care option. Visit bit.ly/3kBUce5 for more information.
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