#faster cures

FasterCures’ Philanthropy Advisory Service (PAS) engages with donors, family offices, and foundations to facilitate more strategic and informed philanthropic investment. PAS encourages funders to consider several questions when starting their philanthropic journey to ensure donors fully understand the challenges at hand:

What is the current state of the disease?  What is the burden of disease? What is known about disease etiology? How is the disease diagnosed? What is the standard of care?

What is the state of research?  What are promising, emerging therapies? Which are leading the race to commercialization and why? What is each sector funding and at what scale?

What challenges are holding back R&D?  What don’t we know about the biology? Is there a lack of research tools and infrastructure? Are there regulatory challenges to developing treatments? What are the funding gaps and why?

Who else is working in this space?  What academics have research efforts underway? What nonprofits and disease foundations are active? What are other philanthropists funding?

                Dedicated efforts to understand these questions can inform an investment roadmap that is poised to truly meet unmet need and have a greater return on philanthropy. At FasterCures, we’ve seen venture philanthropists direct their financial resources to address key challenges often unfunded by the government or industry. Just to name a few examples, philanthropy can be used to:

Deploy capital to de-risk early stage assets for follow-on funders;

Invest in therapy development tools (animal models, biomarkers, etc.) to better understand therapy efficacy and disease progression;

Build patient registries and clinical trials networks to ensure enrollment of the right patients at the right time;

Support nonprofits that can open dialogues with regulators and payers about expectations and evidence needed for decision-making; and

Create incentives for team funding and support early-career scientists.

Implementation of the roadmap also includes close consideration of where to make a gift and how to structure it. There are a plethora of options – academic labs, independent research institutes, disease research foundations, etc.

Awards to academic institutions and independent research institutes can be used to support research programs at specific institutes, centers, or investigator labs, but resist the urge to just fund your alma mater. Also, we’ve seen the most successful awards underpinned with detailed research agreements with mutually agreed upon milestones and funding allocated in tranches accordingly. 

A class of strategically minded disease research foundations (or venture philanthropies) is emerging and deploying their resources to take on many of the unmet needs described above to accelerate research progress for specific disease areas. Check out our Philanthropy Toolkit for questions to understand what organizations are going to be thoughtful stewards of your financial contributions and have the potential to make real R&D progress.

Other venture philanthropy efforts are not focused on any one disease, but laser in on R&D issues within a specific geography, such as supporting the entrepreneurial physicians or financing high-risk “valley of death” research in certain region.  

                Finally, think about what success looks like. For many, curing a disease, or at least developing new therapies for suffering patients, is the Holy Grail of their philanthropy. Bringing a new treatment to market is a long, risky, expensive, and complicated road with a lot of different players. You may not be able to count the number of new therapies you’ve funded over a decade, but you could potentially think about amount of follow-on funding to the assets you “de-risked”, the reduced clinical trial enrollment time because of patient registries, or the increased effectiveness of preclinical studies because of a new animal model. There are many more ways to think about the impact of your philanthropic investment – these are but a few.

So, regardless of how many zeroes might be in a total gift amount, you’re likely it will yield a greater return if you first understand the landscape of unmet needs and other players, develop a giving roadmap that strategically utilizes philanthropic dollars, assess potential recipients and properly structure funding, and devise an approach for measuring success and impact.

If you’ve been involved with M&A or significant transactions, chances are you find due diligence to be a must. Significant philanthropic investments should be made with the same rigor as other investments. Science can’t afford for us not to.

Resources you can use:

Giving Smarter Philanthropy Toolkit

Measuring and Improving Impact: A Toolkit for Nonprofit Funders of Medical Research

First, funding for basic science is flat if not shrinking: award rates for NIH grants are lower than they were in the early 1990s. Second, the traditional career path in biomedical research is getting longer as it gets narrower. NIH reports that the percentage of investigators under age 36 receiving an ROI dropped from 18% in 1983 to 3% in 2010. The mean age of receipt of a first RO1 is 42 years. And third, many young life scientists are discouraged. They are admitted to graduate programs in large numbers, not always as talent but as labor. Yet their prospects for tenure, their own lab, and an independent path are dim. Many are searching for another lane to a science career and translational work provides an interesting and potentially productive option.

Translational research is a catchall and unfortunate term that implies we just need research that translates basic knowledge into application or can work back from observation to the bench. While that is true, not all basic knowledge needs or deserves to be applied and often our observations are so skewed or inaccurate that they don’t provide a useful map for bench science. It is almost heresy to suggest that we don’t need deep knowledge to effect change, but when people are suffering and dying, we need a better approach to developing young scientists and nurturing their brilliant ideas in a more timely fashion. Consequently, we must think differently about the skills and environment that our future scientists need and how best to provide them.

Translational scientists intentionally focus on multi-disciplinary collaboration. They tend to search for people who can help them think and work through a problem and often shine brightest at the edges and interfaces of other disciplines. These scientists might seem more “workmanlike” in their approach and, as such, face several challenges—the culture of academic institutions and funding mechanisms are not always sympathetic or encouraging and the skills of recent graduates are often mismatched or not responsive to the needs of translational science. Yet these scientists are out there emerging from the ooze of the research ecosystem. We need to create settings in which they can flourish. Anna Barker, Arizona State University, told us “organizing for translational science is the most innovative thing going on in the research world right now.”

Keith Yamamoto, University of California San Francisco, is a longtime advocate for innovative training programs in the biological and biomedical sciences. He said that biology is well into the transition from a descriptive endeavor to quantitative one, so new approaches to analysis have become increasingly powerful. As such, this is changing the way we ask questions. We have the potential to apply all sorts of discoveries in a Google-like way, analyzing disparate information, comparing, and analyzing it again. This should fundamentally change our approaches to research and training. Yamamoto said that the ways we now learn are undergoing dramatic changes, which requires everyone to have a basic scope of scientific literacy. But not everyone has to dive deep—multidisciplinary teams will do the best work if translational skills are in abundance. This reality has profound implications for how we define the university and for the ways that we teach and fund science. “Young scientists get this—this is how they have learned since they were children,” said Yamamoto. “They don’t mind working in teams and they want to make a difference, sooner rather than later.” Lita Nelsen, Massachusetts Institute of Technology, agreed, saying “this generation of young scientists and engineers are noticeably more interested in pursuing socially important problems.”

Other factors besides the changing nature of science are exposing the need for new types of researchers.

Integrated Health Systems

Frances Toneguzzo, Partners Healthcare, told us that a truly integrated health system—the wave of the future—focuses on testing innovations on site, with a focus on perpetually redesigning care. This emphasis requires different skill sets and different research personnel who can work expeditiously in both directions from bench to bedside and back, who want to answer questions quickly, and who are impatient. Toneguzzo is encouraged that many scientists in training right now understand that their future path is not the same as their mentors and that they will have to forge a new one. With funds and traditional academic positions out of reach for most, she asks whether there is more that can be done to help this cohort along the way so we don’t lose their interest and their ideas. 

Similarly, Peter Margolis, Cincinnati Children’s Hospital, warned that we don’t just need more scientists to develop more drugs; we need more effective care and that requires researchers who understand medicine, clinical care, research, and how to measure effectiveness of interventions. “Academic medical centers, of all places, should have an unrelenting focus on improving outcomes,” said Margolis. This means that we need people who can collect prospectively, well-informed research-grade data in the course of clinical care, make sense of it, and use it to continuously adapt care and inform basic studies.

The Clinical and Translational Science Awards program at the National Institutes of Health has been a step in the direction of establishing homes for translational research but it is too soon to tell whether its training programs will yield the desired benefits. Large efforts can get bogged down in overemphasizing competencies, creating barriers to entry, and overall top-down bossiness. Gordon Bernard, Vanderbilt University (the coordinating center for the CTSA Consortium), is hopeful that the programs will stay focused on openness, flexibility, and a strategy that provides tools for translational researchers. 

The bottom line: we need a broadly trained scientific workforce, said all interviewees. 

In recent decades, investment in biomedical research has transformed scientific discoveries into medicines, vaccines, diagnostics, and devices that improve and save people's lives. Yet the flow of funding into this area, both public and private, has slowed due to a retreat from risk, regulatory hurdles, and other issues. Nevertheless, a tremendous range of promising, innovative research is underway, backed by industry and public-private partnerships, and a growing number of leaders are recognizing the value of such investments. Now is the time to lean in and ensure everyone understands the value proposition and relevance to patients and the economy, because it already takes too long to get from an idea to a treatment.

While we assess the damage of the shutdown, and regroup and prepare for the next round of cuts from sequestration and tight budgets, the rest of the world is marching on. I can say with certainty that outside the United States, many people looked at us incredulously because Day One of the shutdown coincided with the Milken Institute London Summit. There I led a panel discussion, "Funding Medical Breakthroughs: Enriching Society," with science policy leaders from the United Kingdom and European Commission as well as from a global pharmaceutical company. It was a fast-paced discussion that traversed a wide terrain: from the big challenges in getting ahead of diseases of the aging to the assets that patients bring to research (e.g., clinical trial participation or biobank donations) to the universal sentiment that we can no longer have inward-looking medical research and must forge collaborations. Panelists were optimistic about the promise that long-term investments in science have offered, as well as about the power of data, information, and the genome to give us keys to new treatment pathways. Because we know that diseases won't wait. Collaboration in medical research is a game changer. To collaborate, to band together, to cooperate, to conspire, to unite. Whatever synonym you choose, it can add up to time and potentially cost savings. Our current model of discovery costs too much and so we need to look for savings wherever possible. Evidence of collaborative models is abundant at our upcoming Partnering for Cures conference, where we'll highlight 30 innovator presentationsprofiling cross-sector collaborations aimed at reducing the time and cost of getting new medical solutions from discovery to patients. In an upcoming FasterCures report "Consortia-pedia," we look closely at the emerging model and the future of collaboration by consortium. Research by consortia is here to stay. There are now more than 250 consortia creating opportunities to contribute to research. Many consortia focus on creating tools that will benefit the entire biomedical research sector. Government plays a major role as does industry, academia, and foundations. Asia is just starting to work in this space, but the European Union has already bypassed the United States in terms of consortia based in a geographic location. Much ado about nothing? Perhaps, but in a 2012 United for Medical Research paper, it was clear that although other nations may face similar budgetary challenges, these countries are choosing to increase investments in biomedical research. Data supporting the value of this investment are vast, and it impacts the scientific workforce as well as industry decision-making. Growth in high-wage, high-skill jobs in the life sciences sector is flat-lining in the United States, yet trends in employment in European countries show the opposite -- consistent growth. If present trends continue, China's financial commitment to biomedical research will be twice that of the United States' in the next five years (and four times greateras a share of GDP). That scale may be tough to match, but Congress needs to prioritize medical research and innovation to even stay in the game.

In the United States, every 68 seconds, someone develops Alzheimer's disease. Every 24 seconds, someone is diagnosed with cancer. Every 18 seconds, someone is diagnosed with diabetes. Patients' lives are literally on the line. We've seen the power of patients since the founding of FasterCures. Tenacious venture philanthropy groups like the Cystic Fibrosis Foundation, Multiple Myeloma Research Foundation, and Myelin Repair Foundation are pioneering new ways of getting to a treatment and ultimately a cure. Recent history shows what happens when conflict converts to collaboration. ACT UP. FIGHT BACK. These cries echoed through the early days of the HIV epidemic by AIDS activists, and look what treatment options came of it.

The open source metaphor holds enormous power for reaching our goal of faster cures. The key is to understand when and where in the process the open source metaphor can immediately “port” over, and to understand where we need to take the ideas behind open source – distribution, peer production, low transaction costs, and political freedoms – and do some translational research of our own to bring them into local context. Because that’s the only way we’ll realize the massive potential of a transition to open systems in drug discovery.

First, let’s look  at open source in software. Software is a human construct, built in languages designed for its creation, and governed by a harmonized and powerful international copyright regime (the drawbacks of this regime are widely known and discussed elsewhere, but are out of scope for this context). Developers have spent decades embedding abstraction and modularization into software development. And the tools of software development are widely available at low or zero cost: a computer, an internet connection, and the willingness to learn to write code. 

This is the foundation for what most people mean when they think about “open source” – a loose collection of individuals, connected by technology, coming together for a variety of reasons to collectively create a product that is larger than the sum of its parts, distributed through computer networks at costs far lower than traditional commercial products. These products emerge in technical frameworks that track and reward the small edits and changes that improve software over time, and allow collective governance of projects without a centralized command-and-control system.

And most importantly, these products contain within themselves political freedoms: freedom to contribute, to change, to edit, to distribute, to reuse. The freedoms are embedded in copyright licenses that pass rights on from person to person, that travel with the documents that contain the software code. It is a remarkable thing, open source. It would have sounded insane in the 1970s.

And we’ve seen open source move from software to culture. The most obvious example is Wikipedia. Despite all the obvious reasons why no one would ever trust an online dictionary edited by pseudonymous users, one that contains entries on topics no academic would deem worthy, Wikipedia not only exists but has been empirically demonstrated to be as accurate as the best traditional peer-reviewed encyclopedias (this study was disputed at the time by Encylopedia Britannica, which has ironically signaled a move to a peer production model itself).

So it’s no surprise that the vision of a loose collection of individuals coming together to discover cures, connected by technology, empowered by technology, is having its moment in the sun for drug discovery. We should ask the same benefits in pharma that we have seen in software and culture. But the discovery process is a different animal than the software development process. A simple mapping of open source doesn’t exist.

The final product is not a modular piece of software, but a chemical entity, or a device. It must be manufactured at a certain level of quality, and cannot be distributed at zero marginal cost. The legal regimes are those of trade secret and patent, not copyright. And since drugs and devices cannot - yet - be tested in silico, there is a major human component not present in software – the humans who courageously volunteer for study, and their political rights.

So given this reality, where in the process can we make a rapid transition to open source approaches?

We start with the knowledge construction space around human biology. Knowledge is closer to code than anything else in the pharmaceutical value chain: it can be captured digitally, transmitted at zero marginal cost, and it’s not something that payors will reimburse as part of care. There is a massive public investment in the creation of knowledge about health and biology, in the form of data and scholarly papers. And there is momentum at state, federal, international, and institutional levels to expose knowledge for open reuse and recombination (despite some objections and lobbying efforts by knowledge brokers like publishers and scholarly societies).

Perhaps most telling, there is movement from within the industry itself to move towards an open source approach to biological knowledge. In the past five years, three distinct projects have been initiated from within the world’s largest pharmaceutical companies – a group not known for aggressive pro-sharing stances – to create pre-competitive spaces for data sharing and analysis. 

             1. First, in 2009, Merck spun out the Rosetta Inpharmatics unit into a non-profit organization called Sage Bionetworks (disclosure – I serve on the management team). Sage Bionetworks is focused on the platforms and services required for distributed knowledge creation. Sage builds and distributes not only the knowledge modeling processes first proven inside of Merck, but technology platforms that allow teams of geographically dispersed scientists to collectively analyze data, that allow the tracking of individual contributions to complex projects, and that allow patients to engage directly in the research process.

Sage Bionetworks’ Synapse platform is the driver for internal research teams publishing more than a paper per month for more than four years as well as the Cancer Genome Atlas Pan Cancer Consortium (18 papers in press or published) and the DREAM computational challenges. This is validation that the analysis of data doesn’t need a large company’s walls and support systems. It demonstrates that tasks can be broken into modules, contributions can be tracked and rewarded, and that the outcomes can be integrated into the larger systems of scientific knowledge distribution. All of these are key proof points in the advance of open source methods in the life sciences. 

             2. A second example is the release of transMart by Johnson & Johnson and Recombinant Data Corporation. tranSMART is an open source knowledge management platform that combines a data warehouse with access to federated sources of open and commercial databases with a dataset explorer that integrates and extends the open source i2b2 application, Lucene text indexing, and GenePattern analytical tools.

tranSMART also enables investigators to search published literature and other text sources to evaluate their analysis in context, and data in the platform is aligned to allow identification and analysis of associations between phenotypic and biomarker data, and it is normalized to conform with CDISC and other standards to facilitate search and analysis across different data sources. transMart was used initially by pharmaceutical researchers in Johnson & Johnson’s Centocor R&D division, and the transMart Foundation recently released a major new version of the software, available under an open source license. transMart has had real success penetrating the industrial knowledge management market with 20+ adoptions.

Taken together, Sage’s Synapse and transMart are evidence of the very real emergence of common platforms – which are a pre-condition for the kind of peer production we associate with the open source metaphor.

             3. Third, the community awaits the launch of Project DataSphere from the CEO Roundtable on Cancer. Driven by Sanofi scientists, DataSphere promises a universal platform to share oncology clinical trial data sets among researchers, industry, academia, advocacy, and others in a collaborative effort that aims to transform “big data” into novel solutions for cancer patients. Since DataSphere is not yet released, we cannot examine the inner workings of its technology and governance, but early presentations indicate a model more inspired by low transaction costs than other elements of open source: a consortium to manage and broker access to data subject to both trade secret and privacy protection, with technical connections to platforms for collaborative analysis and knowledge management.

I look forward to their innovator presentation at Partnering for Cures in a couple of weeks.  They will be among the 30 cross-sector programs to present their approach. 

These three projects together represent a sea change in the pre-competitive landscape for pharmaceutical development. But it’s notable that each of them focus on the biology. Whether it’s early stage data like the TCGA, or late-stage data like clinical trials, the data is about targets and bodies – not the lead compounds. This is where we’re likely to see the most movement out of industry, and indeed this level of progress would have been unthinkable just a decade ago at the height of the first genomics bubble. But when three industry titans like Merck, J&J, and Sanofi are driving sharing, it’s fair to say the idea has traction. 

Why come?

1. Find a partner who could make a difference. An easy-to-use partnering system allows you to schedule one-on-one meetings with the more than 1,000 leaders from across all sectors of medical research.

2. See collaboration in action. 30 cross-sectors programs will be presenting their innovative approaches. These programs were granted presentation slots after the most competitive application cycle to date.

3. Problem-solve with medical research leaders. 16 panels will focus on ways to overcome barriers that stand in the way of medical progress. Panelists will deliberate solutions to big data challenges, determine how value can drive innovation, and develop strategies to turn outputs into outcomes.

4. Get expert advice to help you make informed decisions. Experts are at the ready to meet with you one-on-one, arranged through the partnering system. Get advice on attracting capital, collaboration structuring, and regulatory and reimbursement strategy.

5. Share your ideas openly. Whether through Q&A sessions at panels or at focused networking opportunities, you will have numerous opportunities to connect with others who share your therapeutic affinity or area of expertise.  

                                                               Have you registered yet? Register today! 

At #P4C2013, content is king. Check out the remarkable line-up of panels, panelists, and presentations.  But, context is the kingdom. Content only matters if it's relevant. This program is designed to focus on solutions and collaboration – the goal of each program element is to allow you to advance your own objectives. To view the most up-to-date information, visit www.partneringforcures.org.

Panels >> View full program

Big science in the 21st century

Francis Collins, Director, National Institutes of Health

Michel Goldman, Executive Director, Innovative Medicines Initiative

Richard Pops, Chairman and CEO, Alkermes

Arati Prabhakar, Director, DARPA

Marc Tessier-Lavigne, President, The Rockefeller University

The People Behind the Science: Will Work for Food

Otis Brawley, Chief Medical Officer, American Cancer Society

Jonathan Dordick, Vice President for Research and Howard P. Isermann Professor of Chemical and Biological Engineering, Rensselaer Polytechnic Institute

Sally Rockey, Deputy Director for Extramural Research, National Institutes of Health

Cindy Wu, Co-Founder, Microryza

Rare diseases: Hot, but for how long?

Mike Collins, Vice President for Global Clinical Operations, Alexion Pharmaceuticals

Sharon Hesterlee, Vice President for Research, Parent Project Muscular Dystrophy

Raju Kucherlapati, Professor, Department of Genetics, Harvard Medical School

Matthew Perry, Portfolio Manager, BVF Partners L.P

Simon Stevens, Executive Vice President, UnitedHealth Group; President, Global Health

Matthew Herper, Senior Editor, Forbes Magazine

Learning to love failure

Robi Blumenstein, President, CHDI Management

William Chin, Executive Vice President, Science and Regulatory Affairs, Pharmaceutical Research and Manufacturers of America

Stephen Friend, President, Sage Bionetworks

Story Landis, Director, National Institute for Neurological Disorders and Stroke, NIH

Luke Timmerman, Vice President, Life Sciences Initiatives, Xconomy

Beyond Venture Capital

Alexis Borisy, Partner, Third Rock Ventures; Chairman and Co-Founder, Foundation Medicine; Chairman and former CEO at Warp Drive Bio; Co-Founder and Board Member at Blueprint Medicines

Ilan Ganot, Financial Advisor, J.P. Morgan Securities LLC

Andrew W. Lo, Charles E. and Susan T. Harris Professor, Professor of Finance; Director, Laboratory for Financial Engineering, Massachusetts Institute of Technology

This is no artifact. Economic geography shows that innovation thrives where cultures blend. It is true of countries built by immigrants, such as the United States, Australia, or Israel. It is also clear from the capitals of former European empires – Berlin, Vienna/Budapest, Paris, London – where the mingling of ethnicities created the ferment that transformed them into cosmopolitan beacons of artistic and technological power. Further back in history, Florence, Venice, Rome, Athens, Constantinople, and Alexandria have all at some point leveraged their positions on trading routes, or their dominion over faraway provinces, to create the cultures that gave us the Renaissance, Hellenism, and the Islamic golden age – when roving scholars from Cordoba to Bukhara made major contributions to medicine, mathematics, and astronomy. 

Today, innovation continues to thrive where cultures overlap: Silicon Valley, Boston, Quebec, Singapore, the Baltic Rim, Switzerland, Flanders, and many other regions.  In the United States, the foreign-born represent 12% of the population, but they account for 25% of its Nobel prizes, 25% of the founders of venture-backed companies, 30% of its patents, and 47% of its scientists and engineers with doctorate degrees. Immigrants are over-represented among members of the National Academy of Sciences and the National Academy of Engineering, and among the authors of highly-cited science and engineering journal articles.

It is as if innovation somehow comes more naturally to people who internalize various cultures. Research and casual observation actually support that idea. Polyglots know, for instance, that one does not think alike in various languages. A Frenchman does not think like a German. One is holistic, the other is methodical. These differences carry over to how we solve problems. Some cultures are analytic and reductionist, others are intuitive and associative. No single approach is better, but, depending upon the problem, some may be more appropriate. People steeped in multiple cultures can access a broader set of problem-solving pathways and pick one that best fits the situation at hand.

There are other reasons that put immigrants at an advantage when it comes to innovation. They are apt at challenging norms and authority, and prone to act when unhappy with their lot. With their accents and customs, they never quite fit in, and are used to being different and operating from the margin. They are also at ease with disruption, which is part and parcel of being an immigrant. Like entrepreneurs, they have a tolerance for risk, and a bias for action.  And they top that with a relentless drive to succeed because they can never be completely mainstream.

Research also shows that immigrants have an uncanny ability to function in multiple worlds at the same time. They often grow up speaking one language at home and a different one at school, and they sometimes must learn several languages as their parents change countries to flee war or persecution. Those who become scientists retain the agility to move back and forth between multiple domains. They are boundary crossers, with interest and expertise in multiple disciplines, but operating preferably at their interface. They are not biologists or chemists or physicists, they straddle the boundaries between these sciences, and can see connections that might elude less versatile scientists. This makes them especially effective problem-solvers. They think differently, can approach problems from various angles, and harness multiple problem-solving tools.

With such attributes, one would think that countries blessed with immigrants would see them as assets to be leveraged, not a ball and chain on the economy. Indeed, those that value them, such as Israel, are among the world’s most innovative societies.  But most nations see immigrants unfavorably, like welfare collectors, and relegate them to camps and ghettos. It does not help that many have a precarious immigration status – like temporary visas that get overstayed or no visa at all. But then, people who flee war and misery seldom have their papers in order. This makes much of the current US debate on immigrants – and whether they arrived legally – a bit disingenuous. We should do what best serves the interests of America, and not lose ourselves in legalistic arguments.  Immigration is like free-trade. It benefits a country, even if the other party does not play fair. Just as a nation gains from engaging in free-trade, even if its partners do not reciprocate, it also gains from welcoming immigrants, however they arrived. This is not a plea to lift all restrictions, but one to manage them wisely.

“Conversations that bring together researchers, payers, regulators on the issue of patient needs are critical because otherwise, there is tremendous risk in making assumptions about those needs,” urged a workshop participant, highlighting a key theme of the day.

Here’s a top-line summary of ways in which all parties can contribute to a shared understanding of value.  

For patient organizations:

Seek opportunities to serve as “honest brokers” of patient-centered resources and assets with payers, innovators, policymakers, and providers. Examples include clinical trials registries and blood and tissue samples collected from well-characterized patients.

Gather data.  Anecdotes are important, but data about what matters most to your patient constituency is needed for diagnostics, therapeutics, and care. Develop ways to measure and collect data that supports these patient-relevant outcomes.       

Work toward shared performance standards and interoperability (of data and other assets, like informed consent) within your field, with other organizations serving the same constituents, and – ultimately – across all disease states. 

Work vigorously to understand what treatments/care approaches work for whom, under what conditions, in which settings, at what cost and advocate for the adoption of standardized care guidelines that equalize access to best-practice care for your constituents.

Leverage the trusting relationship you have with your constituency to educate about how quality is defined in your condition.  It is also important to understand  the financial realities that innovators and payers are pressured by which helps to facilitate open, informed communication about these challenging issues.

For payers, innovators, policymakers and providers:

Increase transparency of the evidentiary standards for reimbursing treatment and care and the desired outcomes payers will incentivize. Repeated throughout the day was the request that “payers show innovators what the answer key looks like.”

Create payment models that reflect the cost of innovation and recognize the savings achieved through potential “cure,” prevention and avoidance of harm.

Engage early and often with patient stakeholders to share information, understand mutual needs and expectations, and align capabilities. Develop and reward systems that make this kind of communication part of the ecosystem.

Invest in ways to expand access to and make better use of treatments and care that have already been shown to be effective and drive out what is not effective.        

Grow larger scale data pools through greater interoperability of data standards for collection, storage and analysis, integrating systems with federal initiatives and learning from other fields that utilize novel computational methods to guide resources decisions.

In the words of another workshop participant, “New therapies have thrown our patients a very, very important lifeline and we don’t want that lifeline to be frayed by lack of access in the future.” It’s clear these issues warrant new types of interactions and collaborations, especially to effect policy changes that will garner broad support.