Industry Experts on Designing Research Platforms to Serve Patient Participants
By Bio-IT World Staff
April 29, 2015 | Large and growing volumes of data, whether from -omics technologies, wearables, or clinical records, have made researchers in the life sciences hungry for bigger cohorts of research volunteers to cut through the noise. From basic research to the pharmaceutical industry and hospital systems, the need for new ways to reach patients is palpable. Last Thursday at the Bio-IT World Conference & Expo in Boston, a keynote panel showcased three organizations taking approaches that give study subjects a stake in research and aim to make it easier and more rewarding to participate. The session, titled “Data Custodians, Patient Advocates,” drew connections between the meaningful use of data and meaningful engagement of the individuals providing that data.
The panel, moderated by Bio-IT World Editor Allison Proffitt, featured Katherine Wendelsdorf, a field application scientist at QIAGEN who also serves as spokesperson for the Empowered Genome Community; Andreas Kogelnik, founder of the Open Medicine Institute; and Benjamin Heywood, co-founder and President of PatientsLikeMe. All three of their organizations have turned to social networking models for inspiration in building platforms to host and share patient data.
The most targeted of the three projects is the Empowered Genome Community, which Wendelsdorf likened in her presentation to a Match.com for genomic information. The Community is open to anyone who personally owns their own genome sequence or genotype, whether through research initiatives like the Personal Genome Project or consumer services like 23andMe. “What it aims to do is help individuals make their own well-sequenced genomes more scientifically useful,” Wendesldorf explained. Members can upload their personal data to an online platform, and immediately get free access to a commercial-grade genome analysis suite, QIAGEN’s Variant Analysis.
Members’ presence in the Community also lets researchers contact them for enrollment in scientific studies. Like Match.com, the Empowered Genome Community looks for key attributes that make a researcher and a community member a good match. “We have researchers who contact us and say, ‘I need a few more controls for my study. Do you have any genomes right now from women over the age of 50, with no history of breast cancer?’” said Wendelsdorf. Those researchers can then send notifications to members who fit that description, and if the members are interested, they can be put in direct contact. (For more, see “An Online Community for the World’s Whole Genome Owners.”)
At the Open Medicine Institute (OMI), Kogelnik’s team has opted for more of an electronic medical record (EMR) model, but with the patient as the administrator. Patients who build accounts in OMI’s platform, OpenMedNet, can deposit as much data as they like into their personal profiles, and interface with researchers or hospital EMRs to bring in data they’re unlikely to own themselves, like full medical histories or genome sequences. Like Wendelsdorf, Kogelnik believes the best tools for spurring collaboration give patients a better view of their own data at the same time as researchers. OpenMedNet also shares with the Empowered Genome Community the strategy of telling scientists what kind of data is available within the platform, without actually transmitting patients’ data until a collaboration with privacy protections can be formed. (For more, see “OpenMedNet Provides a Platform for Understanding Chronic Disease.”)
PatientsLikeMe is already well known as a Facebook-style networking platform for patients, usually those with less common chronic diseases. Members are known not only for sharing their treatment histories with one another, but also for advocating for patient-centered research in focused disease areas. With a very large and growing member population, PatientsLikeMe is starting to do valuable research of its own on patients’ attitudes toward different research models. For instance, Heywood said in his presentation that 94% of patients are willing to share their personal data for research, even though more than 70% are concerned about consequences to their privacy and data security. That strong level of motivation to contribute to science, Heywood suggested, needs to be better reflected in conversations about how, why, and when data is shared. “The language of privacy is 100% on the risk side, and it’s never on the benefits,” he said. “Every article on technology or IT or bio-IT says, ‘Of course privacy is an issue,’ end stop… Does that inhibit our ability to talk about and be open and actually have an honest discourse about the effects of chronic disease?”
One of the pet passions of the PatientsLikeMe team has always been understanding which outcomes matter most to patients’ own experiences, with the goal of placing those outcomes at the center of clinical trials for new therapies. “Ultimately what we need to do is take the patient experience and raise it to the level of medical evidence,” Heywood said, noting that the easily-quantified metrics normally tracked in clinical trials may be very distant from patients’ real concerns. Finding ways to translate subjective symptoms like pain, fatigue, and “edginess” into rigorous measurements is a major focus of PatientsLikeMe’s engagement with its members. (For more, see “PatientsLikeMe: Outcome Measures About to Get Crowdsourced.”)
The panelists respond to audience questions at the Bio-IT World Conference & Expo at Boston's Seaport World Trade Center. From left: Bio-IT World's Allison Proffitt, Katherine Wendelsdorf, Andreas Kogelnik, and Benjamin Heywood.
The panelists observed that giving patients a personal stake in research, already a difficult proposition, is also in flux today as new types of data change the demands on physicians, scientists, and patients alike. Volume is one large concern: Kogelnik shared one project that OMI is pursuing with patients with orthostatic blood pressure problems, which integrates wearable sensor data with gene expression profiles taken at multiple time intervals. “It’s an incredible amount of data that we can generate just at one instant, let alone longitudinally over the course of a patient’s treatment or experience,” he said, making it much harder for even professionals to understand what data is significant, and what’s just noise.
That problem is compounded by complex, little-understood data types like whole genomes, where moving from a list of genetic variants to a meaningful interpretation of what they mean for an individual’s health is both labor-intensive and highly speculative. “It can easily overwhelm a physician,” says Kogelnik. “It can easily overwhelm a patient. And yet when I sit in my clinic I have patients who walk in the door and say, ‘Doc, here’s my genome.’” Short visits to a doctor’s office are not equal to the task of integrating that kind of information into health plans.
The Empowered Genome Community exists in part to tackle that problem, by extending analysis tools freely to members of the public, yet Wendelsdorf acknowledges that this approach is limited to fairly tech-savvy users. Still, she believes that just by making toolkits more transparent, and giving the broader community access to some of the same analysis methods as scientists, researchers can open up a higher level of trust with the public. “Right now, it’s very opaque to patients what’s going on with their data,” she said, “and it leads to people putting too much and too little faith at the same time in what we are telling them scientifically. The more white boxes we can put on the clinical and scientific process… I think the more we can actually have an open dialogue.”
That “white box” approach also benefits researchers themselves, who need to understand exactly how data has been manipulated to have honest collaborations across institutions, or to integrate data from different sources. “Every pedometer on the iPhone uses a different algorithm from the raw data of the chip to calculate your steps,” Heywood pointed out, which makes it much harder to compare results between apps. With DNA, which can go through multiple layers of analysis and various sample preparation protocols, this problem is even more acute.
Nonetheless, with new data sources and online networks, the opportunity to draw more patients together is large. OMI, for example, uses OpenMedNet to provide support to the Rare Genomics Institute, which seeks out patients with rare disease presentations and tries to draw connections between them. “We’re collecting basically n-of-one studies,” says Kogelnik, “but often these turn into n-of-12, n-of-20 types of collections around what turn out to be very rare diseases.”
With more common, but little-documented medical events, the opportunities may be even greater. One broad project OMI is involved in aims to catalogue how widely-prescribed medications interact with pregnancy. As Kogelnik observed, since half of all Americans have a chronic disease, a wealth of data on pregnant women using common medications is surely out there waiting to be methodically collected. The challenge is mainly getting patients to appreciate how they can contribute, and what benefits they might see in return.
In many cases, it will take broad cultural change before patients can be placed at the center of the life sciences enterprise. Despite the flood of data into the industry, the biggest players in medicine may still be only beginning to grasp what information is really important, either to patients or to science. Heywood, for instance, noted that near-universal EMR adoption is still far from offering either the natural flow of data between institutions or the useful interpretation of that data that advocates for integrating clinical systems have been waiting for. At least in clinical settings, the industry’s failure to make it easy to share data may be masking an even more profound problem, that the types of data being collected would make little difference to care even if they could be looked at holistically.
“I think the next ten years, maybe five years, is going to be about interoperability, and we’re going to start connecting these [EMRs] and the data will flow,” Heywood said. “And then we will realize that we don’t actually collect any meaningful health data in EMRs, and start on that problem.”
More from the Bio-IT World Conference: