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Leveraging Technology to Improve Clinical Trial Match Rates

Hospitals are under pressure to enroll more patients into trials to speed up the discovery and approval of new medicines. This is especially relevant in the oncology space where medical breakthroughs can significantly improve outcomes for cancer patients.

Matching a patient to a relevant trial as early as possible is critical for success. While everyone recognizes the need to act fast, identifying clinical trials for a cancer patient takes on average 120 minutes. Consequently, only 35 percent of patients are evaluated for trials.

Two sides of the same problem

These numbers show the “patient” side of the problem — that most patients don’t get the chance to participate in trials. However, the problem has a “research” side too when 10 percent of sites fail to enroll a single patient, 25 percent of investigative sites fail to enroll enough patients, and 80 percent of clinical trials fail to meet recruitment timelines.

What these statistics suggest is that most clinical studies fail or are largely delayed due to matching and recruiting inefficiencies. Even if they eventually succeed, they do so at a very inflated cost.

Finding the root causes

What we’re seeing here is not due to any lack of desire from physicians to enroll patients in the right trials, nor is it due to patients’ lack of interest or awareness about the potential life-saving benefits of trials. In fact, one study reveals that 75 percent of cancer patients would have participated in a clinical trial if their oncologist had discussed such options with them during treatment planning. And yet, most trials go unfilled.

After speaking to hundreds of senior executives from diverse health systems about this issue, we begin to see a clear pattern, indicating difficulty accessing information.

Clinical staff are often too time constrained to find all the patient data necessary to match against a trial’s eligibility criteria. The plethora of disparate systems a typical hospital uses makes finding patient records difficult — some information resides in the EHR, while other pieces are available through pathology, radiology, or genomic reports. A large part of the data is stored in unstructured formats, such as free text, which make it hard to process automatically. Some records may still exist in a non-digital format. If scanned poorly, those records may create challenges for OCR technology to recognize characters from the images.

Even if clinicians manage to overcome all these challenges, they need to repeat those steps multiple times as they look across thousands of trials and evaluate hundreds of patients. Which trials are most relevant for patient A? Which patients can be grouped in a cohort for trial XYZ?

Each of these roadblocks contribute significantly to the patient-trial matching problem, and so do standard workflow procedures. To access all relevant patient data and information as outlined above, clinical staff must switch from one system to another, often having to log in multiple times using different usernames and passwords. This breaks clinicians’ workflow, making them less productive.

Decision support at the point of care

It is possible to deliver real-time information about trials that best match a patient’s clinical and genomic profiles and it’s possible to do the matching and referral in a fully automated way, completely within the workflow, without leaving Epic or whatever EHR your hospital is using. Inspirata’s Trial Navigator, for example, is an automation solution that allows clinicians and their hospitals to overcome the barriers to effective trial matching, to customers in North America and Europe.

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