Improving Clinical Trials With Real-World Data

Victor Wang, Senior Vice President of Data & Research at Q-Centrix, shares new research findings about the collection and use of real-world data from hospitals and health systems to improve clinical trials.

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Please share a little about your affiliation and background. 

I'm focused on the expansion of Q-Centrix offerings to include research stakeholders, as well as bringing our partners better leverage of the data that we help them collect. So background-wise, my career has always been at the intersection of patient data and research, primarily working with life sciences and research stakeholders to build data sets and drive insights from those data sets. I was previously on the founding team at ConcertAI, an oncology-focused data and research services company.

A little bit about Q-Centrix, maybe. So we're an enterprise clinical data management company that partners with software, the largest and broadest team of US-based clinical data experts, and we partner with about 1200 different hospital partners to curate high-quality data for a variety of purposes. So this data supports things like regulatory needs, as well as registry submissions across diseases like cardiology, oncology, trauma, research, and more.

Please share some of the key findings from the Q-Centrix Survey. 

Yeah, so maybe as a little bit of a briefer, clinical data is just really rapidly becoming the health care industry's most valuable resource. And as the use and impact of this data expands, we felt like it was important to understand how facilities are using and thinking about clinical data broadly. How do they use it to inform clinical research and advance health equity in particular? So the survey results, when taken together, provide a pretty good snapshot into how hospitals and health systems are thinking about their priorities, their needs, and their progress against this type of goal. 

Health equity and using real-world data in clinical research are high priorities for most facilities and health systems. Nearly two-thirds of respondents reported that health equity is a high priority at their facility. Fifty-nine percent of respondents felt that patients would be comfortable with the idea of their de-identified data being shared for the purpose of clinical research. And the biggest barriers reported by facilities in tracking health equity metrics and sharing real-world data concerned a lack of resources, so things like staff, time, and funding, followed by a lack of sufficient tools, technology, or IT infrastructure.

If 60% of patients are thought to be comfortable with their de-identified data being shared, why are only 32% of hospitals and health systems sharing RWD actively? 

Yeah, it's a great question. We noticed it as well. While patients may be bought into the idea, for most hospitals, these activities are really pretty secondary to providing care, and there remain quite a lot of obstacles moving forward, also cited in the survey. So this goes to things like infrastructure and IPs, staff, funding, data strategy, and we hear a lot about siloing of data within hospitals. It's just difficult to access this information, and then even then, is it the right information? How do we actually use it?

So a huge challenge for hospital research departments is that the data they need is oftentimes not yet captured. So things like patient outcomes, key events, and symptoms are often only found in physician notes or other documents that really require an intensive amount of parsing and abstraction to build a clean data set that you can leverage. And then there's a lot of commentary on how much data healthcare interactions are spitting out in every single second, but a lot of that data doesn't necessarily mean the data's easy to use or it's stored cleanly, or even the right data to support these various strategies around research and health equity.

How can hospitals and health systems overcome these barriers to better share RWD for clinical research? 

Yeah. Many hospitals speak to the lack of sufficient tools and infrastructure to process data for clinical research, but they may not realize that their organizations have already made progress in these areas in support of other hospital requirements, so this alludes to that data siloing that we're talking about. So for instance, participation in federal and state reporting efforts, quality registries, and other programs oftentimes require similar tools and processes as clinical research, and the hospital's already doing this.

So these programs, coupled with the readily available external expertise and external help that could be leveraged can help produce these high-quality data sets for research, piggybacking off of what already exists. Additionally, with the funding of research efforts, there's no shortage of research opportunities in the industry. Whether this is participation in clinical trials, the contribution of data for retrospective studies, or participating in research networks, hospitals actually have a lot of ability to leverage their data to better align to the types of studies that their patient population can support. So doing more of this can help bring the right research opportunities and really start driving some of that revenue and funding to support these initiatives overall.

What are common worries of hospitals and health systems have on securing data and privacy while sharing RWD, and how can they mitigate data breaches?

Yeah. Hospitals' and health systems' concerns regarding safety and privacy of real-world data should really be no different than how they view the storage of their other clinical data. So a lot of this goes to the need to invest in a modern IT infrastructure, ensure that third parties who have access to the hospital's data systems also have protective measures in place, and ultimately treat their facility like an information technology company that houses some of their most valuable assets. And this includes clinical data.

One of the biggest differentiators between, call it standard hospital privacy versus the leveraging of this information as real-world data, is that the manipulation of real-world data oftentimes requires that the data itself is de-identified for research purposes. So there's a range of different options to efficiently remove patient identifiers, ensure statistical de-identification, and securely transfer this data. And so with that said, actually, these de-identified data sets for research actually pose less of a risk than the raw data that resides within the hospitals already.