Solving research problems through social media

As more scientists take their work to the Web and the cloud – publishing papers in open-access journals, recruiting trial participants through Facebook, and crowd-sourcing investigations – the availability and traction of social media tools to support medical research have grown. Facilitating everything from the establishment of "virtual lab space" to the crowd-funding of promising science and scientists, these networks are gaining traction in an increasingly collaborative research environment.

A Partnering for Cures panel featured leaders from diverse organizations that are each using social media in unique ways to create new data platforms and solve research problems. In discussing the impact of social media on research norms, they agreed that social media represents a powerful new source of data, but in order to properly harness the data, the research community needs to become more comfortable collecting and probing the data from perspectives that may be unfamiliar.

Jon Fredrickson of Innocentive anecdotally pointed out that the community needs to develop an appreciation for “different angles of attack” to solve problems. Frederickson explained that in the world of crowd-sourcing investigations, a major hurdle for organizations when a member of the “crowd” solves the problem is actually accepting the solution: “The personal issue that the [organization] has to face is when I get the answer and that person doesn’t look like me, wasn’t trained like me, and doesn’t have gravitas that I have, can I accept the fact that that answer really does solve my problem.”

Moderator Matthew Herper of Forbes Magazine pointed out that while social media can generate a lot of data, the quality of the data can sometimes be called into question.

Sally Okun of PatientsLikeMe explained that there is a story behind each piece of data, whether it is structured or unstructured. PatientsLikeMe uses a clinical networking platform to more accurately assess and report patient outcomes. Much of the data that are collected is unstructured, as their system allows patients to relay their experiences in their own words. The data are then curated and coded into clinical terms, and validated by sharing the clinical interpretation with patients to ensure accuracy. Okun explained that they have found this type of assessment more accurate than the conventional method of clinically assessing patient-reported outcomes through questionnaires.

Elizabeth Iorns explained how her organization, Science Exchange, creates an online marketplace for scientific facilities and services provided by universities and companies around the world, which otherwise may prove to be difficult to find or access. Using a fee for service model to create an online exchange for science experiments, Science Exchange provides a new way to ensure research reproducibility and validation of findings. Iorns noted that nearly 70 percent of data produced by academic labs cannot be reproduced independently, which may be a key reason that many drugs fail. One of the reasons that this problem exists, according to Iorns, is that the incentive system is geared toward quantity, not quality: “There are not stringent checks and balances in place to ensure that the data have been validated and are identified as correct.” Iorns explained that the quality of data generated from Science Exchange is generally higher, not only because of the validation process, but also because single investigator bias is removed, and it creates an overall auditing process. “These audited results are more likely to be the ones that lead to a cure,” she stated.

All panelists agreed that the norms as they relate to social media and research are indeed changing; however, the rate of change is not evenly distributed. John Wilbanks of the Kauffman Foundation said, “The rate at which norms evolve compared to the rate at which technology develops, which is so much faster, means that we are going to have a weird 5-10 years ahead of us, and we don’t have a culture right now that is tolerant of weird interstitial periods where we have capacity and not norms.” Wilbanks explained that the immediate danger to be most aware of is knee-jerk regulation and legislation in response to these changes. He warned that if we do not plan strategically, it will be very difficult to survive this period and really harness the power of social media data.

Greg Simon of Poliwogg added to the discussion by talking about the power of the crowd in funding high-risk, high-reward research, and the tools that allow for this type of distributed funding model. He explained that although it is becoming increasingly clear in other industries that there is value in the thoughts and actions of all people, not just private investors, and entire industries are driven by assessment of these thoughts and actions, the financial community has been slower to catch on to the power of creating and evaluating social platforms to drive new innovation.

In March 2012, Congress passed the U.S. JOBS Act, which allows non-accredited investors access to private equity investments (crowdfunding). Simon explained, “If you believe there is wisdom in the crowd when it comes to finding and shaping new health solutions, shouldn’t we give [the crowd] the opportunity to help shape the future by picking the winner [financially]?”

In closing, the panelists discussed some ideas for incorporating checks and balances into the use of social media across research channels, agreeing that rating structures will need to be put into place to allow the research community to develop confidence in the platforms themselves, as well as the data they generate.

Increasing the efficiency of clinical trials

It's impossible to talk about "faster cures" without talking about significantly reducing the time and cost of the clinical trials process. The challenges are well known, and many solutions have been proposed, but transformation has been elusive.

A panel of diverse experts discussed current innovations that could feed systems change, and agreed that universally interconnected electronic medical records will greatly increase the efficiency of many aspects of the clinical trial process.

Clinical trials are a critical component to the development of innovative therapies; however, trial duration has increased by nearly 25 percent and the compound annual growth rate in cost has risen by 12 percent over the past three decades. Moderator Melissa Stevens of FasterCures began the discussion by asking the panelists their thoughts on how to introduce business process improvements into the clinical trials process.

According to Robert Califf of the Duke Translational Medicine Institute, a key incremental change that can be made to the business process to improve the overall system is the development of FDA guidance on wasteful monitoring. Something as simple as efficiently handling and reducing the number of reported adverse events can save time and money by preventing investigators from becoming inundated with data that do not have significant meaning. “Every trial should be designed to answer the [clinical] question, and we need to throw out the excess baggage that comes along with it,” he said. Califf also noted that the development of the www.clinicaltrials.gov database has been extremely useful in that it has allowed more transparency into the number of clinical trials that are poorly designed and not asking clinically meaningful questions. This transparency will help to trim the fat to improve overall efficiency and allocation of resources.

Lawrence Lesko of the Center for Pharmacometrics and Systems Pharmacology at the University of Florida pointed out that figuring out how to access and leverage large datasets – “big data” – will also be critical to driving down costs and improving clinical trial outcomes. Lesko proposed that conducting metadata analysis across large data sets, such as those housed at the FDA, would enable the development of disease progression and dose response models that could then correlate to early predictors of success or failure of investigational drugs. Lesko stated, “We need more predictors to design clinical trials, especially in therapeutic areas where no one size fits all … the FDA has the expertise to do this [metadata analysis] but not the time, therefore we need to farm that task out to collaborators and consortia that don’t have vested interest in the approval process.”

Clifford Hudis of the Memorial Sloan-Kettering Cancer Center presented the “big data” issue from a different perspective. Hudis pointed out that a plethora of patient data exists; albeit in silos of paper files or unconnected electronic filing systems. This problem of disconnected data has placed the research community at a huge disadvantage, particularly when it comes to clinical trials. If patient electronic medical records were housed in an interoperable network of databases, researchers would be able to use these data to design smarter clinical trials that address more clinically meaningful questions, more rapidly enroll clinical trials, and quickly identify safety signals. This would lead to what is commonly referred to as a rapid learning healthcare system.

Susan Solomon of the New York Stem Cell Foundation highlighted the importance of embedding patients into the process of designing better clinical trials. Solomon shared her difficulty in retaining patient participation in the organization’s stem cell research. She explained that patients were excited and wanted to donate stem cells; however, participation enthusiasm was overshadowed by “little things like making sure that their calls were returned quickly or that [patients] did not have to wait too long at appointments at the doctor’s office.” Solomon added that this customer service problem could have been avoided if they had embedded patients into the process during the planning stages.

Gary Neil of Apple Tree Partners punctuated this point by stating, “As we step back and look at this ecosystem, we really have to remember that the focus is the patients … the patients are the real heroes of the entire ecosystem, because they are willing to participate in these trials and take the risk … so we need to make certain that their efforts amount to something.”

The panel agreed that in order to effectively integrate patients into the process to ensure success of these trials, the clinical community needs to invest in creating an infrastructure that increases the number of eligible patients that are enrolled in these trials. In addition, patient participation should be simple, and patients should be involved in formulating key clinical questions.

In closing, the panelists agreed that the factor that will have the most impact on the cost, duration, and intelligent design of clinical trials will be an investment in developing an infrastructure of universally interconnected electronic medical records to create a rapid learning healthcare system.