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Leveraging Technology to Improve Clinical Trial Match Rates

By Jodi Treharne

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.

Two Sides of the Same Problem

Matching a patient to a relevant trial as early as possible is critical for success. While everyone recognizes the need to act fast, however, identifying clinical trials for a cancer patient takes on average 120 minutes. Consequently, only 35% of patients are evaluated for trials. This may lead to negative impacts on both the patients and the cancer centers.

Looking at the two numbers I provided above shows the "patient" side of the problem -- namely that most patients don't get the chance to participate in trials. However, the problem has a "research” side too:

What these stats mean is that most clinical studies are failing or being largely delayed due to matching and recruiting inefficiencies. Even if they eventually succeed, they do that at a very inflated cost.

Root Causes for the Problem

The breakdown we are seeing here is not due to any lack of desire on behalf of the physicians to enroll patients on the right trials. And it surely is not due to patients' lack of interest or awareness about the potential life-saving benefits of trials. On the contrary, patients these days are savvier than ever, looking into every treatment option available. In fact, one study reveals that 75% of cancer patents would have participated in a clinical trial if their oncologist had discussed such options with them during treatment planning. And yet, here we are -- most trials go unfilled. 

I have spent the majority of my career in different areas of health care technology. As I talk to hundreds of senior executives at diverse health systems about this issue, I start seeing a pattern in what I hear. And it goes roughly like this:

  1. Clinical staff are often too time constrained to find all the patient data necessary to match against a trial's eligibility criteria.
  2. The plethora of disparate systems a typical hospital uses makes finding patient records difficult. Some information resides in the EHR. Other bits and pieces are available through pathology, radiology, or genomic reports. 
  3. A large part of the data is stored in unstructured formats, such as free text, which make it hard to process automatically.
  4. To complicate things further, 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. 
  5. Even if clinicians manage to overcome all those challenges, they need to repeat those steps multiple times as they look across thousands of trials and evaluate hundreds of patients. Which 2-3 trials are most relevant for patient A? Which patients can be grouped in a cohort for trial XYZ?

Each of the detriments above contributes significantly to the patient-trial matching problem we identified at the begging of this post. There is one final detriment that we haven't mentioned yet. Perhaps because this detriment is easier to spot and understand, it is often the one that gets the most attention. In short, this detriment boils down to EHR workflows. To accomplish steps 1 through 5 above, clinical staff have to switch from one system to another, often having to log in multiple times using different usernames and passwords. This breaks clinicians' workflow. No one likes to break their workflow because it makes 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. It is 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.

I am excited to be part of the Inspirata team who has developed a ground-breaking automation solution that allows clinicians and their hospitals to overcome all the detriments to effective trial matching at once. Over the past few months, we have started introducing our Trial Navigator solution to customers in North America and Europe. And we are seeing very positive reactions from our initial conversations. 

To provide for a forum to discuss the current state of clinical trial matching, evaluate the many benefits of implementing an automated matching solution, and learn more about Inspirata's Trial Navigator, we invite you to our virtual fireside chat on November 10th, 2020. If you read this post after the live event takes place, please request an on-demand replay and we will give you access to the recording.

We look forward to seeing you at the fireside chat or hearing back from you in the comments section. Let us know whether you face the same detriments I've listed in this post. Would a solution like Trial Navigator be potentially useful to you? What would make it valuable for your institution?

Jodi Treharne is Director of Sales for Inspirata's Cancer Informatics business unit.

Tags: NLP, clinical trials, cancer research, clinical studies, trial matching