According to the American Cancer Society, “Fewer than 5% of adults (less than 1 in 20) with cancer will take part in a clinical trial…. The main reason people give for not taking part in a clinical trial is that they didn’t know the studies were an option for them.”
In an age where clinical trials of molecularly-targeted therapies are often the best care options available to cancer patients, this low participation rate seems like a gaping hole in oncology practice. Why do clinical trial sponsors and sites struggle with the seemingly simple task of signing up participants? The truth is that it’s hard for clinicians to track which trials are applicable to which patients and stay aware of new trials. On top of this, the process for identifying suitable trials is manual and error-prone, leading to missed treatment opportunities and trials that struggle to find participants.
Today, many health systems employ dozens of research coordinators who manually review each day’s patient records, using the EMR system to see if any patients might be eligible for a trial. Health systems without these resources, typically community hospitals and clinics, rely on clinicians themselves to perform this review on top of their already staggering workloads. It is very easy to accidentally miss a patient who might have been eligible, especially given that critical information is often buried deep within the patient record, or not in the EMR. And with increasing development of drugs targeted toward specific mutations in a patient’s cancer, a growing number of trials include molecular information in their eligibility criteria. Molecular profile data is often stored in the EMR as a scan of a faxed paper report with large, unstructured blocks of text, adding to the probability of missing key eligibility criteria.
Syapse set out to enable healthcare systems to offer their patients access to the latest clinical trials and therapies as quickly as possible. A critical step was to build an automated system to flag patients who might be eligible for a trial. A core feature of Syapse Precision Medicine Platform is the storage of structured clinical data from the EMR and other clinical systems along with molecular profile data from testing laboratories. Capturing these data in structured format enabled us to build a rules engine to match patients with trials.
Trials matching can take two distinct forms. In the first case, the user is a clinician, focused on finding the optimal treatment course for an individual patient, who uses Syapse to identify trials for which a patient is eligible. In the second case, the user is a research coordinator who uses Syapse to see all the patients in their network who are candidate matches for a trial. Syapse gives the coordinator the needed information to proactively reach out to those clinicians whose patients are best suited for their trial.
For each trial, a healthcare system’s research coordinators capture the most important eligibility criteria as structured data in their Syapse knowledge base. Important criteria often include genetic alterations, diagnosis, histology and stage, performance status, age, gender, and prior treatment. The Syapse rules engine automatically identifies patients who match these structured criteria and notifies research coordinators and clinicians of the candidate match. The research coordinators or the patient’s clinician can then review each case in Syapse to evaluate whether the patient meets the full list of eligibility criteria (as described in a previous blog post). Gone is the need for clinicians and coordinators to manually search through the EMR or remember the clinical trial criteria in their heads.
Much of the data required to determine trial eligibility currently resides in the EMR, stored as unstructured fields of text. Syapse can automate the most onerous part of the matching process because Syapse stores both clinical and molecular information as structured data. Combined with Syapse’s trial eligibility assessment workflow, clinicians and research coordinators now have a vastly more efficient means to get patients into trials, providing new care options to patients while enhancing trial accrual and increasing the pace of clinical research.
For the average clinician, automated clinical trial matching can vastly increase the number of trials they can reasonably consider for their patients. At community health systems that lack the number and diversity of on-site trials available at many large academic centers, better trial matching can democratize access to the latest therapies and studies. As more patients are considered for more trials across the nation, Syapse is helping healthcare systems change the face of clinical research, yielding studies with larger, more complete cohorts, better powered and more likely to produce conclusive results.