Digital health and patient centricity: One and the same?
by Julien Rashid, Intern
Digital health – a term that refers to the confluence of electronic health records, cloud computing, artificial intelligence, big data, and other emerging technologies – will define a new era of more patient-centric health care. On Oct. 24, the Milken Institute’s Lynda and Stewart Resnick Center for Public Health hosted the Future of Health Summit with a breakout panel, “Driving the Digital Health Revolution,” that discussed this topic. The panelists, a group of business leaders, policymakers, and philanthropists, conveyed their visions of a digital health future. Their compatible visions had promising implications for patient-centricity – a topic of focus at FasterCures.
From left: Indu Subaiya, Esther Dyson, and Donald Jones
The digital health wave has been pushed by new currents in health information technology (HIT). Panelists tacitly agreed that HIT would inevitably converge health data into centralized hubs. Karen DeSalvo, the former acting assistant secretary for health and national coordinator for HIT at the Department of Health and Human Services, predicted this convergence would lead to a “digital medical home” – a place for patients to access and archive their medical data and receive input on health-care decisions. Ideally, from a FasterCures perspective, this tool would also give individuals greater control over how their data are shared and further transparency about their use.
Donald Jones, the chief digital officer at the Scripps International Science Institute, believes that digital health will not only help patients answer “what’s next?” but also will multiply their options. Digital health will give patients more autonomy and access to knowledge. This will allow more efficient self-guidance through the health-care system. Cindy Elkins, co-founder and chief operating officer of MyWays, noted that utilizing patient capacity will lighten some of the excessive burdens on health-care providers. Further, by giving patients more freedom to move between institutions and greater control over how their data are shared, digital health will facilitate ambulatory care and create a more patient-centric health-care system.
A benefit of patient-centricity, DeSalvo stated, would be the “generation of health without health care” – a coverage of the “middle-ground” between public health and primary care. Throughout the Future of Health Summit, several speakers emphasized the economic burden of chronic diseases, which is projected to soon be $3.4 trillion annually. Unhealthy behavior, a major cause of chronic disease, is one of many health determinants outside of the health-care system. In fact, as DeSalvo noted, most health determinants are independent of the system. Digital health, by creating new platforms for behavioral change interventions, could have a major impact on how chronic diseases are addressed in this “middle-ground.”
As Esther Dyson, executive founder of Wellville, put it, one way digital health may cover this “middle ground” is through behavioral health precision medicine. Paul Chew, the chief medical officer at Omada Health, discussed Omada’s initiatives in the digital health therapy space at the individual level. Omada provides digital health coaching and an online network for people with diabetes. Digital health may also have benefits at the community level. Dyson noted that digital health could improve the effectiveness of local health leaders by delivering tailored community health curriculums. Both Dyson and Chew commented on how organizations will be able to assess and tinker with digital health approaches and tailor them to individuals. Dyson said this is similar to how startups in Silicon Valley approach product development. For payers to buy into this new digital health model, new metrics will be needed, stated Chew, including patient engagement.
At the end of the panel, DeSalvo discussed her experience in New Orleans after Hurricane Katrina. In the wake of the storm, the health-care silos typically buttressed by regulation and payment structures became irrelevant under martial law. What evolved was an organic patient-centric system. Social workers, doctors, and other professionals joined forces to address patient needs holistically. In DeSalvo’s view, in the past, when society invested in health care it erected barriers that isolated the field from public health, business, and humanity. Digital health might be just the treatment needed to restore a patient-centric system with fewer barriers, with an emphasis on prevention, and in which, to paraphrase Dyson, society will begin investing in health instead of renting it.
Digital health will continue to be a defining feature in a new health-care epoch. On Nov. 13-14, FasterCures held its annual Partnering for Cures conference in San Francisco, the second of 2017. Many of the panels at both conferences discussed topics related to patient centricity and digital health, including data science, patient value frameworks, precision medicine, artificial intelligence, and health citizenship. Read summaries of plenary panels and videos of all the sessions at www.partneringforcures.org.
A New Generation of Philanthropy Joins the Battle Against Disease
by Taylor Cusher, Senior Associate
FasterCures’ second Partnering for Cures of 2017 closed with a discussion of the new generation of philanthropists pledging their funds to help solve some of humankind’s most pressing health challenges. The conversation covered evolving approaches to philanthropy, support and collaboration with other sectors, and problems unique to the medical research/health-care space that donors face. While philanthropic giving in other types of giving like art or education can be approached at a slow, dedicated pace, “medical philanthropy often comes at you like a brick at your head, and you jump feet first into a field that you never wanted to be involved in,” said Richard Ditizio, the Milken Institute’s president and chief operating officer and panel moderator.
To set the stage, Ditizio shared that there are 1.6 million nonprofits in the United States, which employ 11 percent of the U.S. workforce. Since organizations and causes supported by philanthropy make up a significant portion of our economy, he argued that we should treat philanthropy like the business that it is. Effective philanthropy requires diversification, taking bets on the most promising areas in the marketplace, having predetermined measures of success, and a timeframe for exit – just like a traditional investment portfolio. David Panzirer, trustee of the Leona M. and Harry B. Helmsley Charitable Trust, supported this approach. He noted that impactful giving in medical research requires a targeted approach of conducting due diligence, understanding the landscape, and then understanding where philanthropy can play a role in that landscape. Panzirer cautioned against more traditional ways of giving by saying, “It is philanthropy’s job to understand the full landscape. Don’t just write a check to the guy in the white coat and wait to see what happens.”
One aspect of the next generation of philanthropy is bringing together other stakeholders, whether to better understand a disease area or to support a cohesive approach to philanthropic efforts. LaTese Briggs, director of strategy and programs for the Milken Institute Center for Strategic Philanthropy, emphasized the importance of looking beyond medical researchers and basic scientists to figure out the roles of other stakeholders, like patient advocacy organizations and other funders, to achieve a nuanced understanding of the challenges of a disease. As a member of a nonprofit that funds disease research, Todd Sherer, CEO of the Michael J. Fox Foundation for Parkinson’s Research, suggested that philanthropists should fund activities within the disease area that will truly make a difference. Providing additional funds on top of an already well-funded area will make little difference. Briggs frames what philanthropists can bring to the table as four T’s: Treasure (their available financial resources), Talent (their expertise), Time (an active approach), and Ties (their connections to other sector experts and other geographies). Combining these areas is allowing the next generation of philanthropists to more deeply consider how best to achieve their mission, vision, and impact in a disease area.
Sherer also shared the power that funders can wield over how research is conducted. They can require sharing of data and collaboration among researchers, patients, caregivers, stakeholders, or other partners to help push research in their desired direction. David Beier, managing director of Bay City Capital, spoke of his involvement in the development of the Parker Institute for Cancer Immunotherapy. The Parker Institute has brought together a toolkit of resources, including data sequencing, analytics, and tumor samples, that incentivized participation from some of the best scientists in the field because those same resources didn’t exist elsewhere. Beier added that the Parker Institute is becoming a leader in the space, addressing barriers in research through a common institutional review board for all clinical sites, and attracting further investment in immunotherapy by enrolling 15 percent of patients in cancer clinical trials in the United States. These attractive measures – a streamlined protocol approval for all clinical centers and a robust patient population – are appealing to researchers and industry alike.
Bringing together resources and interdisciplinary collaborations can also speed up processes and progress, as Mark Smolinski of the Skoll Global Threats Fund shared. The foundation brings together engineers and experts in human health and animal health to work through problems in open data sharing and tool development. The interdisciplinary teams brought together in this manner achieve great strides over a short period and create a strong collaborative foundation for the future work. Sherer advocated for a balance between collaboration and competition. Both approaches can improve the speed at which research is done and problems are addressed, but philanthropists should consider which challenges may lend themselves to a team approach and which may inspire groups to race for the finish line. Collaboration and innovation are often thought to be costly or difficult to achieve, but Smolinski shared his belief that “no community is too hard to reach. No country is too poor to innovate. Curiosity outshines fear if you create a chance for innovation to happen.”
Artificial Intelligence and Machine Learning in Medical R&D: Hope or Hype?
by Kristin Schneeman, Director, Programs
To the relief, perhaps, of more than a few in the Partnering for Cures audience, Atul Butte of the University of California, San Francisco began the lunchtime plenary discussion on artificial intelligence and machine learning by asking, “What on earth do all these terms mean?” He described artificial intelligence (AI) as taking aspects of human intelligence and modeling them with computers, machine learning as one type of AI related to processing data in a supervised or unsupervised fashion, and deep learning as focused on modeling brain architecture.
And why does everyone in medical R&D seem to be talking about them right now? Butte and three other experts agreed that this is a unique time with a number of factors converging to make the application of computational approaches especially important in the fight against disease and for improved human health. Butte summed it up as a time when “we have great hardware, open software libraries, plentiful data sets to learn from (including genomics and electronic health records), and lots of hard questions in biology that still need answers.”
Iya Khalil, co-founder and chief commercial officer at GNS Healthcare, noted that while advances in computation have been underway for some time, what’s changed is the availability of data. Now, data are deeper, richer, more varied, and in greater quantities, which allow us to illuminate human biology and health in a more robust and actionable way.
Alice Zhang, CEO and co-founder of Verge Genomics, became an entrepreneur three months before finishing graduate school, borne of her frustration that drug development, particularly in neurodegeneration, is still fundamentally a guessing game. She saw an exciting convergence of new technologies in machine learning with a deeper understanding of the human brain as “an opportunity to eliminate the guesswork.”
And John Baldoni, senior vice president at GSK Pharmaceuticals, called out the advent of factors like greater sharing of data, high-performance computing capacity, modeling, and high-throughput cell biology as being key enablers for efforts like the ATOM Consortium. This recently launched effort aims to mine the “dark data” on failed compounds within companies to generate models and hypotheses that can accelerate drug development.
Another common theme among the speakers was the need to “bring in both computation and biology from day one. The only path to fully realizing AI’s potential is breaking down the silos between software and drug development. The most sophisticated algorithms are meaningless without good data to train them on,” said Zhang. Khalil eschewed the notion of “practicing AI” in favor of thinking in terms of how to solve problems: “At the core of solving problems in biology now, you have to have AI in your toolkit, and keep learning along the way.”
Butte turned the conversation back to data, asking speakers whether they view it as “oil” (i.e., the world’s most valuable resource) or “soil” (i.e., a regenerating medium for growth). Baldoni replied neither: “Data is the currency of the new pharmaceutical world,” to be invested in creating value for patients and society, and that generates returns. Zhang and Khalil commented on the proliferation of new types of data, which are necessary if we’re going to get the answers we seek from these computational approaches. Said Zhang, “Biology is unique in its ‘missing data’ problem. We don’t fundamentally understand a lot of disease biology, unlike other applications of AI.” Verge is trying to address this challenge by, for instance, collecting living brain tissue from Parkinson’s patients undergoing deep brain stimulation, allowing them a view of early disease progression, as well as by pioneering single nuclei sequencing to help verify cell types and clarify signals. GNS is working with data both “broad” and “deep” from health systems as well as clinical research data and patient registries such as those collected by groups like the Multiple Myeloma Research Foundation.
Zhang called “patient-level data our biggest hair-on-fire problem.” All felt that big cohorts like the All of Us Research Program and cohorts organized by patient groups would be extremely useful. And all related to the challenges of insufficient data standardization, lamenting the time and effort devoted to “data munging.” Zhang noted that one-third of Verge’s first 1 million lines of code were devoted to data curation.
In the end the panel expressed much more hope than hype about the promise of AI and machine learning to accelerate biomedical R&D. As Baldoni said, “Cycle times could be dramatically reduced, regulators would have more confidence in the models, flow would be greater, and the pharma model will change.”
"Recommended for You:" Can Direct-to-Consumer Expertise Inform Precision Medicine?
by Colin Ward, Senior Associate
Can direct-to-consumer expertise inform precision medicine? That was the question posed to the panelists of the opening plenary at FasterCures’ Partnering for Cures conference in San Francisco, held Nov. 14, 2017. The answer was a resounding “yes!”
Deloitte Consulting’s managing director and FasterCures’ former executive director, Margaret Anderson, moderated the session, featuring panelists Silas Buchanan, co-founder and CEO of the Institute for eHealth Equity; Eric Dishman, director of All of Us Research at the National Institutes of Health; Kathy Giusti, founder of the Multiple Myeloma Research Foundation and co-chair of the Harvard Business School (HBS) Kraft Precision Medicine Accelerator; and Christine Lemke, co-founder and president of Evidation Health.
The plenary opened with Anderson taking the audience through the history of the many disruptive innovations that have come to bear in the last several decades, from floppy disks to iPhones to artificial intelligence, specifically highlighting the way in which customer personalization has become the norm, such as with services like Rent the Runway and Birchbox. “What does the future look like with regard to medicine and health care?” Anderson asked the panelists.
Dishman from All of Us began the discussion by setting the ideal of a “holistic, longitudinal health record.” Not currently in existence, this record would leverage an ever-expanding array of real-world evidence and wearable technology data to capture the majority of life that is not occurring in a clinical environment. He highlighted the possibility for such a record to accelerate research.
Lemke picked up on Dishman’s thread of wearables to discuss her background as a technologist coming into the health-care space. She was excited to leverage the types of data – wearables, online behavior data, and search history – that don’t meet clinical rigor but still hold potential for phenotyping conditions and facilitating behavior change. For example, this type of data could be used to identify moments in time when a person is open to making behavioral and lifestyle changes that would support better health.
Giusti outlined her work with the HBS Kraft Precision Medicine Accelerator. She framed patients as consumers and underscored the need to seek to meet their needs. Through the Harvard initiative, five cancer foundations learned from tech giants how to better serve their customers (i.e., patients).
Buchanan and Dishman noted their respective efforts to engage underserved populations and communities of color. Buchanan highlighted the need to engage with the “grasstops” or mid-level community leaders as brokers of trust in communities that may be disenfranchised from the medical system. Dishman acknowledged the trust barriers that All of Us faces with communities that have faced prior abuses in research. “We are over-recruiting those who have been under-represented and on the margins of the traditional health system,” Dishman said.
Connecting back to Lemke’s description of the huge data sets that are manipulated by advertisers and sellers to encourage and predict consumer behavior, Dishman outlined some of the ethical boundaries that his team is facing in the All of Us recruitment. “What are the ethics of employing the engagement and retention strategies that tech uses in a health-care space? For us, we reach out to them, invite them to participate, but at what point do we cross a line into coercion? Opting into a tech app is not the same as consenting to medical research. Consent in our setting must be truly informed.”
But the lessons that healthcare can learn from big tech continued to come up. Lemke encouraged ease of access: “Don’t discount convenience, make it as lazy a possible.” Giusti shared a vision of a future where a disease foundation could walk with patients through their full journey, using data to match them with the right care at the appropriate time.
Cross-sector collaboration is key to these efforts. Coming from industry, Dishman acknowledged that he entered his current government role with a fair amount of skepticism. Nonetheless, he was impressed by the enthusiasm he found among career federal staff to be part of innovative change: “You can teach an old dog new tricks.” Lemke shared how patients are eager to engage, and she welcomed the richness and quality of responses her organization received when asking patients for insight and hypotheses about their health.
It is hard to talk about disruptive health-care technology and not get stuck on the issues of trust and privacy. Lemke encouraged a reframing of the conversation to be one around transparency rather than trust. As an example, she said that while many users do not “trust” Facebook, they are still willing to share a tremendous amount of data with the organization. She encouraged transparency around the intentions for the use of data and limitations around their use as a means to increase engagement and sharing.
To close the plenary, Anderson gave each panelist a proverbial “magic wand” and asked what their wishes were. Dishman wants to educate underserved communities on the power of precision medicine, Buchanan wants to use technology to close rather than widen health disparities, Lemke wants to bring the full expertise of technologists to bear on solving health problems alongside traditional health-care professionals, and Giusti wants to have better sharing of best practices for engagement.
We all know that it takes innovation, hard work and a lot of great partners to make progress on improving the medical research system. That is why we are grateful that so many of you, across the R&D system, have interacted with us this year, through our convenings, our policy work and all of our programs.
IN 2016, WE FOUND OUT THAT:
1. Engaging patients in research isn’t just a goodwill gesture: it can make research better.
2. Momentum is building to incorporate patient preferences into the biomedical R&D system so that products and services better align with patient needs.
3. A #HealthCitizenship movement can enable citizens—both healthy and sick—to engage with researchers, industry and regulators in important ways.
4. Improving the medical research system is an overwhelmingly bipartisan issue.
5. Patient groups are important to the value and coverage debate...
6. ...So let’s formally incorporate patient perspectives into value frameworks.
7. Collaboration for the sake of it is a fool’s errand. Be clear in why you are collaborating.
8. Language matters, and we need broad agreement on what “patient-centered” and other terms mean.
9. Patient registries are evolving rapidly, and momentous change is on the horizon.
In addition, we try to take our own advice on innovation and will be taking Partnering for Cures on the road in 2017, after eight years in New York. Look for more details early next year about an even more focused version of the meeting on the west and east coasts.
Wishing you a peaceful, healthy and productive new year.
Sincerely,
Margaret Anderson
Executive Director, FasterCures
Challenge Accepted: Understanding How Data Shape Medical Research
What are the technical and scientific capabilities of data in life science? How can we incorporate data in medical research in a way that is clinically significant and meaningful? John Wilbanks, Chief Commons Officer of Sage Bionetworks, and his fellow panelists on the “Challenge Accepted: Understanding How Data Shape Medical Research” panel sought to answer these questions. In this highly interactive session, three themes bubbled to the surface.
1. It’s data trends that make health data intriguing.
Wilbanks shared an anecdote from his experience on the Parkinson’s mPower project: “We run a small Parkinson’s study via iPhones and use a lot of these methods to find patterns within data. In looking at collected data alongside patient-input free text, we saw that in groups of patients, words like ‘race relations’ and ‘news’ were affecting daily health information; the culprit was stress.” This trend was much more telling than the variance in empirical data alone.
Environmental data is another interesting source of data trends. When we collect massive data sets from mapping a population, and include environmental data, we can passively create a longitudinal cohort. With this well-characterized data set, we can run studies retrospectively—a potentially quick and efficient method. Russ Altman, professor of bioengineering and genetics and medicine at Stanford University, described this future. A current example of this type of effort is the newly announced Echo Program, which National Institutes of Health Director Francis Collins highlighted from the audience.
2. Incorporating data scientists and outside thinkers into the medical system is key.
Organizations should involve data scientists early in a research project. As highlighted by Leslie Fine, vice president of data and analytics at Salesforce, “People are bringing in data scientists at the end and are saying: ‘I have a screw, a bolt and a block of wood. Tell me how I can create a car.’ To avoid this, you have to bring them in at the beginning.”
Rachel Kalmar, fellow at Berkman Klein Center for Internet & Society, Harvard University
Organizations should involve not only data scientists, but also other people with diverse skills, such as pharmacists, to support physicians and patients to collect, curate and package medical data, according to Altman. The burden cannot be placed on physicians or patients alone. Physicians need help integrating data and information systems. And patients need new tools. If they have genomic or wearables data, they require tools to put it in a centralized place, clean it, and annotate it.
3. We have much to learn.
The personal health data that we have today—generated from sources such as wearable devices—do not always add up. Rachel Kalmar, currently a fellow at Berkman Klein Center for Internet & Society at Harvard University, and previously a data scientist at Misfit Wearables, is often asked questions about why step counts from various devices do not sync. Every tiny difference in an environment—the device used, the sensor and algorithms in the device—affects your personal health data. To extract meaning from this data, it’s important to understand its source and to thoughtfully calibrate the questions to help you understand it.
Perhaps the greatest source of learning for medical researchers is the consumer world. Companies such as Salesforce can create holistic views of customers. Fine shared that Salesforce is starting to look at people and their health information just like companies look at customers. She said, “When we have a holistic view of people, we have a better characterized population, which can help facilitate things like improved targeting for clinical trials.”
Although the challenges to using data science to augment medical research are great, beginning to understand how to tackle these challenges is an important first step toward leveraging data to accelerate cures.
