Bridging clinical 2019

Real innovation is going to be centered on how we collect, standardize, and harmonize data: Industry expert

By Melissa Fassbender

- Last updated on GMT

(Image: Getty/g-stockstudio)
(Image: Getty/g-stockstudio)

Related tags Data collection Litmus Health Wearables mHealth

Bridging the gap between clinical care and research means creating two-way collaboration, improving the way in which data is collected, organized, shared – and engaging EHR vendors, says conference panelist.

Dr. Sam Volchenboum (SV)​ is the director of the Center for Research Informatics at the University of Chicago and a board-certified pediatric hematologist and oncologist. He also is the co-founder of Litmus Health, a data science platform for early-stage clinical trials​.

For Volchenboum, as a physician and a researcher, bridging the chasm means creating a two-way collaboration and ensuring the makers of the electronic health record (EHR) systems are engaged in the conversation.

Volchenboum recently spoke on this topic at the Bridging Clinical Research and Clinical Care Health collaborative in Washington, DC.

Outsourcing-Pharma (OSP)​ caught up with him to further discuss who and what will be needed to create this bridge – and what comes next for the industry and his company, which is looking to make data collection more efficient and effective.

OSP: As a physician and a researcher, what does bridging the gap between clinical care and research mean to you?

SV:​ For me, it means creating a two-way collaboration – not just a bridge – between research and practice.

As you mentioned, the position I am in as both a physician and a researcher means that I understand deeply the benefit each side of the equation can provide to the other. For example, I see patients every day who would benefit from being enrolled in clinical research, and I have access to one of the richest datasets through those same patients.

To get a bit more specific, the way in which we do this is by improving the way in which we collect, organize, and share patient data. 

If we are able to create a standard of data that makes it truly interoperable from research to clinic and back again, we’ll close a huge gap in terms of making sure we’re not losing insights and improving the ability for clinicians to act quickly on new information.

OSP: In what ways does the use of mobile technology make this possible?

SV: ​Mobile technology is especially powerful for its ability to collect raw, unbiased data from the point of experience. I’ve already seen the difference wearables can make for clinical trial participants in their visits, and I know that their impact can extend to the average doctor’s office visit.

For example, in a clinical trial setting, we often ask participants to give us information about their sleep habits from the past month—in the same way that a doctor might ask how you’ve been feeling and then enter in your information to the computer.

In either case, the answers you give aren’t going to be very accurate. Our memories are faulty, and we may be giving biased data without meaning to. After all, who can truly remember every night of sleep they’ve had over the past month?

However, once you introduce wearables into the doctor’s visit or clinical trial setting, you’re much more likely to get more accurate results. This is better for both doctors and patients, and while it definitely improves your care behind the scenes, it also makes a huge difference to the patient’s experience. With wearables, the clinical trial participants I’ve worked with have been astounded by the fact that now they get to understand their data and track their health.

To come back to the conversation about bridging the gap, if these data are collected in a manner that it can be interoperable, we create an invaluable shared vocabulary that can improve the transfer of knowledge between research and care.

OSP: What are the challenges of using this technology in clinical trials?

SV: ​Most data are still collected without regard to any standards. This means that clinicians and researchers often choose their own ways to collect and store data. So, when it comes time to share or combine data, the points do not align and the data have to be transformed into a common standard. This process often leads to data loss or compromise.

So much effort is focused on machine learning for analytics, but the real innovation and contributions to clinical research are going to be centered on how we collect, standardize, and harmonize different kinds of data.

That’s why we’re laser-focused on using our machine learning chops to develop tools for better data collection and integration, facilitating easier interoperability and collaboration across the entire industry. 

OSP: Pharma has often been reluctant to adopt new technology, how can real change be driven?

SV: ​Much of my time as an entrepreneur is not only understanding health care’s pain points (which I am especially privy to as an MD myself), but also working with​ them to identify ways in which technology can integrate into an existing system and impact positive change, without breaking what’s already there and working.

There’s a message in here of, “we’re all in this together.” Health care and even other major industries like education or agriculture don’t need a knight on a white horse to swoop in and save the day. Rather, they need somebody who will listen, collaborate, and tackle today’s challenges alongside them.

OSP: Who and what is needed?

SV: ​We should be asking questions of the industry status quo as often as possible. This can be a difficult ledge to walk, since compliance with regulatory guidelines and laws is absolutely critical. But it is also easy to get bogged down in the traditional way of collecting and using data.

But just because something has been done in a certain way for 20 years (e.g., assessing sleep via a questionnaire), this does not mean there cannot be a validated new way to do this (e.g., collecting sleep data from a Fitbit).

It’s definitely a fine needle to thread, and it takes having players from regulation, pharma, researchers, patient care and more all collaborating to truly make it work.

OSP: What are the next steps for mobile health? For Litmus?

SV: ​We are just at the cusp of understanding how this technology can expand to and impact multiple diseases, and how to build solutions that can be deployed quickly and effectively so that researchers can start leveraging real-world data collected at the point of experience starting today.

Over the past decade, I personally have turned my attention to trying to bring tools and technology to the research community to help facilitate more discoveries in pediatric cancer. In the past few years, my group at the University of Chicago has been working on building and launching data commons to collect and make available data for clinical trials.

Of course, Litmus Health’s work fits into this seamlessly, in improving the richness of the data we collect and helping make the collection of these data more efficient and effective.

We’re very excited about these initiatives, and we are hoping to expand our work across other disease areas and across the world.

OSP: What keeps you up at night?

SV: ​Constant progress. Sometimes, it can seem as if the answer is obvious but we’re moving so slowly to get there.

One thing that helps is looking back at how far we have come with the advancement of how we conduct research and provide patient care, and the steps every player – from the FDA, to pharma, to academic researchers – have taken to help improve the way in which we save and care for lives.

A critical element should be the makers of the electronic health record systems. They have been slow to come to the table to develop and accept standards for clinical research.

I hope we can do a better job engaging them in the near future.

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