"Access to good data is the foundation of new, creative solutions in health care"
Lisa Simpson, President and CEO of AcademyHealth
Welcome to the EDM Forum’s 2016 Review, a primer on the scientific, policy, technical, and market forces shaping the way electronic health data (EHD) is used to transform health and the health care system. The Review highlights diverse perspectives from those on the frontlines using EHD to address some of the biggest challenges in healthcare—serving as a “go to” guide for the most important developments in the last year.
This Review is the culmination of a number of the EDM Forum’s analytic efforts and begins where last year's edition left off ...
Last year's inaugural edition of the Review focused on the ways public and private investments have cultivated an EHD infrastructure primed to support the government’s payment reform goals for rewarding value - not just volume of care. The 2016 Review picks up where last year’s left off and highlights four key trends and drivers of change within the EHD ecosystem:
"Access to good data is the foundation of new, creative solutions in health care"
Lisa Simpson, President and CEO of AcademyHealth
"We’ve got to support quality data analysis that can turn raw data into information that can help care givers and their patients"
Vice President Joe Biden at Health Datapalooza 2016
The Review provides a primer on the scientific, policy, health IT, and marketplace forces shaping the way electronic health data (EHD) is used to transform health and the health care system. The sections below synthesize noteworthy events, policies, and programs, organized by four key trends in 2016:
Make no mistake–the design and implementation of the Centers for Medicare & Medicaid Services’ (CMS’s) Quality Payment Program (QPP) is the undisputed headline of 2016. As part of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), the QPP makes good on the promise of lasting payment reform that will leverage electronic data and reporting. Once implemented in 2017, the QPP will represent a major milestone in the quest to pay for value and quality of care that clinicians are providing, rather than on volume of services.
The QPP includes two “paths” that care providers can follow to move to value-based payments:
1. Merit-based Incentive Payment System (MIPS). At present, CMS evaluates clinicians on how they provide quality patient care and reduce costs through a patchwork of programs, including the Physician Quality Reporting System (PQRS), the Value Modifier Program, and the Medicare Electronic Health Record (EHR) Incentive Program. MACRA combines and streamlines these programs into one new Merit-based Incentive Payment System (MIPS), allowing Medicare providers to be paid for achieving care quality and efficiency thresholds in four performance categories:
|Category||Percentage of score in performance year 1 (2017)|
|Advancing care information||25|
|Clinical practice improvement activities||15|
CMS is set to begin tracking physician performance via electronic reporting in these 4 categories in January 2017, which will establish a baseline for reporting. Payments will be based on physician performance relative to this baseline beginning in January 2019. Payment adjustments based on a physician’s composite score from these categories can range from +/- 4 percent in 2019, +/- 5 percent in 2020, +/- 7 percent in 2021, and +/- 9 percent in 2022 and thereafter.
2. Advanced Alternative Payment Models (APMs). Generally viewed as an alternative path to MIPS, care providers who are part of an advanced APM -- such as an accountable care organization (ACOs), patient centered medical homes, or bundled payment models – will shift toward value through other means. Providers engaged in APMs can be exempted from MIPS’ payment adjustments while also qualifying for a 5 percent incentive payment.
Since half or more of U.S. clinicians will not be part of an APM by this time, CMS expects the vast majority of eligible care providers will follow the MIPS path. CMS issued a draft rule for implementing the MIPS and APMs in May 2016. By close of the comment period in June 2016 they had received over 3,900 comments. The final rule is expected later in the fall of 2016, while Figure 1 below shows a number of other important milestones.
Getting quality measurement “right” is critical to the success of the QPP, the transition to value-based payment, and achieving better health and health care. CMS currently maintains a library of 64 electronic clinical quality measures (eCQMs) just for the EHR incentives program. As pointed out by Donald Berwick in a recent commentary, many clinicians already feel “overcontrolled” and over-measured. Berwick suggests embracing nine specific changes for getting measurement right and ushering in a third important era for medicine and health care. The first change that Berwick advises is for CMS, commercial insurers, and regulators to work with the National Quality Forum to reduce “the volume and total costs of measurement” by 50 percent in three years and by 75 percent in 6 years. He also advises that we measure “only what matters, and mainly for learning.”
Increasingly, there are signs that CMS agrees. In December 2015, CMS posted a draft Quality Measure Development Plan to support quality measurement with the changes coming through MIPS and APMs. The plan was a product of the Core Quality Measure Collaborative—an effort led by the America’s Health Insurance Plans (AHIP), which involved leaders from CMS and the National Quality Forum (NQF), as well as national physician organizations, employers and consumers.
After a public comment period, CMS, on behalf of the collaborative, released the final Quality Measure Development Plan in May 2016. The plan presents a strategic approach for streamlining measure development for the transition from quantity to quality. In a statement accompanying the release of the final plan, CMS emphasized the identification of known measurement and performance gaps and prioritized approaches to close those gaps by developing, adopting, and refining quality measures. CMS also reaffirmed its commitment to promote and improve alignment of measures. For example, this past February, CMS announced the release of a draft core set of quality measures in 7 areas:
CMS and the larger collaborative intend these core measures to be meaningful to patients, consumers, and care providers, while also reducing variability in measure selection, collection burden, and cost.
In March 2016, the National Quality Forum’s (NQF’s) Measure Applications Partnership (MAP) offered guidance to CMS on the measures under consideration for MIPS. The MAP emphasized the need to align measures across all federal programs, while also noting that significant gaps remain in clinician-level measures. For example, NQF urged CMS to explore the role of patients’ socioeconomic status on measure results. CMS plans to seek public comment on the measures in a forthcoming rulemaking period.
In addition to streamlining existing measures, new measures must be developed over time to adapt to new sources of evidence about how best to improve quality and outcomes, such as patient-centered outcomes research. As one example of innovative efforts in this arena, the EDM Forum’s collaborative project led by Dr. Daniella Meeker at the University of Southern California, has built a platform—described in greater detail during a recent AcademyHealth webinar—for health researchers, quality improvement experts, and operational leaders to rapidly develop, test, and implement eCQMs. The goal is for this platform to facilitate an iterative learning process for measure development that is both more efficient and more representative of measure performance in diverse settings and populations. Similarly, NQF this year launched its Measure Incubator to nurture the development of needed measures by connecting groups interested in particular measure concepts with measure development experts, financial and technical resources, and data.
Despite the clear promise of MACRA and MIPS, and the benefits of shifting toward value-based payments, there are many—inside and outside of health care—who are concerned that providers and hospital systems simply aren’t ready to implement the changes needed. A recent nationally representative survey by the Deloitte Center for Health Solutions found that—as of this writing, mere months before the start of the first MIPS performance year beginning January 2017—half of U.S. physicians have never heard of MACRA.
Furthermore, many medical associations—including, the American Medical Association and the American College of Cardiology—have voiced concerns with the planned implementation of MACRA and the QPP, with some calling for a delay in implementation. There is recent evidence that CMS may be considering ways to address these concerns. For example, in a July hearing before the U.S. Senate Committee on Finance, CMS Acting Administrator Andy Slavitt stated that CMS is considering “alternative start dates” and other measures to ensure that practices (and especially small and rural practices) can comply with the required changes.
However, as of September 2016, CMS has not announced plans to delay or alter the current implementation plan, so it is likely that the implementation will proceed with a final rule in fall 2016. At the same time, CMS understands that implementation isn’t a one-time turnkey operation; built into CMS’s plans are methods for refining the QPP and the measures that underlie it. For example, over the course of MIPS and APM implementation, as part of the Quality Measure Development Plan, CMS plans to issue three “calls for measures” to gather and respond to stakeholder feedback identifying gaps in quality measurement and developing new measures.
Complementing payment reform in health care delivery are major changes in the conduct and support for new patient-centered research at a grand scale. With investments from both the public and private sectors, many of these efforts are helping to identify and understand the experiences and outcomes that matter most to patients - enabling an even stronger measurement system.
The capacity for data collection has expanded considerably over the last 12 months with the growth of national initiatives such as the White House’s Precision Medicine Initiative and its efforts to develop a national research cohort, and Patient-Centered Outcomes Research Institute’s (PCORI) efforts to grow and sustain its broad research network, PCORnet.
President Obama launched the Precision Medicine Initiative (PMI) in 2015 to “revolutionize how we improve health and treat disease.” This model proposes new therapies to customize health care to the individual patient. A major piece of the PMI is a new, voluntary national primary data collection effort led by NIH to extend precision medicine from cancer to all diseases by building a voluntary national research cohort of one million or more U.S. participants. Participants are asked to provide or provide access to a wide range of health related data –from medical records to genetic profiles, environmental and lifestyle data, personal device and sensor data, as well as other patient-generated data. The resulting data resources and samples will be broadly representative of the U.S. population and will enable qualified researchers to generate new insights into a wide array of diseases, conditions, and treatments.
In April 2016, NIH announced Eric Dishman – a former vice president with Intel Corporation’s Health & Life Sciences Group and a longtime patient advocate– as the new director for the PMI Cohort Program.
In May 2016, the White House announced the release of the PMI’s final Data Security Policy Principles and Framework, which establishes security expectations for organizations participating in the PMI. Notably, the framework takes a novel risk management approach to achieving data security and privacy goals, resulting from a collaborative interagency process (see sidebar for a list of federal partners) that also included security and privacy experts from academia and industry.
Already a number of organizations, projects, and collaborations have received support from the NIH under the PMI and the National Cohort program. Examples of awards within each of the Cohort’s four program components are provided below:
Meanwhile, the White House and NIH continue to actively recruit participants for the PMI Cohort program, and President Obama recently wrote an opinion piece in which he referred to the PMI as one of the “greatest opportunities we’ve ever seen for new medical breakthroughs.” The NIH has set a goal for the Cohort Program to recruit 1 million participants by 2019.
The capacity for “re-use” of clinical data systems, or secondary data, is also expanding. There is increasing enthusiasm about access to PCORI’s PCORnet, a “network of networks” that seeks to harness the power of large amounts of health information and unique partnerships among patients, clinicians, health systems and others. Several early studies using PCORnet demonstrate the capabilities of this extensive network to conduct clinical trials and observational research, and develop new studies led by ‘patient powered research networks PPRNs’ to generate and test hypotheses.
Led by researchers at Duke University, ADAPTABLE (Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness) is a very large randomized controlled trial to compare two different daily strengths of low-dose dose aspirin in order to determine which dose is best for balancing aspirin’s benefits against its risks, such as bleeding in the gastrointestinal tract for some patients. The 3-year, $14 million study will enroll 20,000 patients by leveraging PCORnet’s Clinical Data Research Networks (CDRNs) and Patient-Powered Research Network (PPRNs) to identify participants based on EHD. ADAPTABLE will also feature an online portal to allow patients to report data directly.
PCORnet’s Obesity Observational Study is also using data from nine CDRNs to evaluate the short- and long-term effects of antibiotics on childhood obesity. Led by researchers from Harvard Pilgrim Health Care, Inc., the nearly $4.5 million, 3-year study uses EHR data from nearly 1.6 million children from 42 healthcare systems within 9 CDRNs across the United States. Researchers will use information on antibiotic prescribing in the first two years of a patient’s life and assess the same patient’s body mass indexes (BMIs) at ages 5 and 10 to determine how many of them are clinically obese.
In March 2016, PCORI’s board approved $12.5 million for a set of new PCORnet PPRN Demonstration Projects. The new 3-year projects will examine specific conditions that impose major burdens on patients, their families and the healthcare system as a whole, from the effectiveness of a specific carbohydrate diet versus a Mediterranean diet among patients with Crohn’s disease to the engagement and education methods to help reduce depression among gay, lesbian, and bisexual populations.
In a move that will have positive implications for national research capacity, PCORnet has recently opened its new “Front Door” to facilitate access to the network, and will consider additional, new projects this fall. The Front Door will administer access to PCORnet’s data resources and facilitate projects among collaborators and funding partners for three distinct levels of requests – data network requests, requests for network collaboration, and requests for PCORnet study designation.
Merck, Optum, IBM’s Watson Health, and numerous other commercial data analytics firms have built or are building large private data repositories – primarily with secondary data from claims and clinical EHR records. All are advancing their work in big data in order to move beyond the previous constraints imposed by working with of (relatively) small datasets and samples. Examples of these efforts, including goals and key partners are provided below:
The past 12 months also saw Alphabet Inc. (Google’s parent company) rebrand its Life Sciences division as “Verily” and deepen its involvement in health care and research with a focus on big data and devices. In August 2016, for example, Verily entered into a partnership with GlaxoSmithKline (GSK) to create a company focused on developing bioelectronic medicines for chronic diseases that disrupt or affect electronic signals in the body to alter the pathways of certain diseases or conditions. The new company, Galvani Bioelectronics, will leverage Verily’s big data analytics engine to address diseases with greater precision and improve treatment outcomes.
In his January 2016 State of the Union address, President Obama called for a new, national “Moonshot” initiative to eliminate cancer. Vice President Joe Biden is leading the initiative’s task force, and in remarks delivered at the Health Datapalooza conference in May 2016 the Vice President emphasized the need for increased access to data for cancer research and asked for attendees to participate in the Cancer Moonshot. Recalling his son Beau's treatment for brain cancer, the Vice President remarked on the need “to support quality data analysis that can turn raw data into information that can help caregivers and their patients.”
In June 2016, in connection with its Cancer Moonshot Summit, the White House released a fact sheet illustrating the overwhelmingly positive response to the project in both the public and private sectors—from federal agencies like the Veteran’s Administration and the Department of Defense to private foundations, societies, and even community support groups. For example, the Department of Energy (DOE) is partnering with the National Cancer Institute (NCI) to launch three new pilot projects leveraging the power of DOE’s supercomputing capabilities. The projects will bring together nearly 100 cancer researchers, care providers, computer scientists, and engineers to analyze data from preclinical models in cancer, molecular interaction, and cancer surveillance data.
Health information interoperability—that is, the ability to capture, review, share, and reuse EHD seamlessly across the health system—remains as a key shared challenge in moving toward a learning health system and improving health and health care. But this challenge isn't necessarily what many may think it is today; indeed, it's no longer just about getting different computer systems to connect with each other ...
Intermountain Healthcare, Stanford Cancer Institute, Providence Health & Services, and Syapse, Inc., announced a new data-sharing network in response to the initiative. The new network, called the Oncology Precision Network, will allow participants to share previously untapped real-world cancer genomics data and promising treatment insights with the ultimate goal of making new breakthroughs in cancer care.
Next steps for the Cancer Moonshot include encouraging new public-private partnerships and increased research investments by private sector groups like the American Cancer Society and the Breast Cancer Research Foundation, both of which recently committed to doubling their investments in cancer research by 2021.
Despite the investments and progress noted above to harness EHD for better care and research, we’re still waiting to see a real shift in patients’ ability to access their own EHD and share it between different providers and systems as needed. In his remarks at Health Datapalooza 2016, Vice President Biden described the difficulties his own family experienced getting and sharing his son’s health data with other care providers at a critical time during his treatment. Limited accessibility and interoperability of EHD (see sidebar) continue to cause negative effects for patients, clinicians, and researchers alike, and they will be two key issues that will be a focus for policymakers in the year.
The past 12 months have seen unprecedented interest and investment in data-enabled, community-led initiatives to improve population health. Some are rooted in health system and payment reform efforts while others strive to address social determinants of health by improving linkages between community-based services such as housing, and transportation.
The CMS State Innovation Models (SIM) Initiative, launched in 2012, is now nearing the midpoint of its Round Two phase, which is providing more than $665 million to support state-led, multi-payer health care payment and service delivery models that improve health system performance, increase quality of care, and decrease costs for Medicare, Medicaid, Children’s Health Insurance Program (CHIP) beneficiaries, and all residents of participating states. The initiative supports both model “design” awardees – that is, those who are designing plans and strategies for statewide innovation – and model “test” awardees – states that are taking the next step from “designing” to “testing” and implementing comprehensive statewide health transformation plans.
CMS is also thinking broadly about population health measures for use with their models and programs. Indeed, in early 2016, CMS launched a technical expert panel (TEP) titled “Population Health Measure Development: Multisector Collaboration” to identify and potentially develop one or more candidate multisector collaboration performance measures that could inform community health improvement in the context of CMS initiatives.
Meanwhile, the Health Resources and Services Administration (HRSA) announced $16 million in awards in August 2016 to improve access to quality health care for those in rural communities, including funds for expanding the use of telehealth technology for veterans and other patients. The awards will support 60 rural communities in 32 states through four grant programs:
Another key driver of population health perspectives are changes in the design of community benefit programs, which for years have provided tax breaks to local hospitals and health centers. More recently, community health needs assessments (CHNAs) have emerged as an increasingly useful tool to assist hospitals and communities to understand and help address the socioeconomic and environmental factors that influence an individual’s health beyond the health care system. Indeed, the Affordable Care Act (ACA) includes a requirement for non-profit hospitals to conduct CHNAs every three years to maintain their tax status, and as a result, more and more CHNAs are being conducted across the country, providing a better picture of local and community health needs and solutions.
At the same time, there is also growing pressure for local hospitals to work together on CHNAs. For example, in a recent opinion piece, authors urged 12 Boston-area hospitals to set aside competition for the benefit of the broader community and work together on developing one, citywide CHNA that would enable a citywide strategic plan for health and allow stakeholders to better focus resources and attention.
Private foundations are also shifting more attention to improve population health. In 2015 the Robert Wood Johnson Foundation (RWJF) signaled a shift towards a broader vision of health improvement by changing their mission to focus on building a ‘culture of health.’ In March 2016, the foundation, along with AcademyHealth, hosted the first Sharing Knowledge to Build a Culture of Health conference, bringing together evidence producers and users to showcase findings and foster the development of a diverse, dynamic, and cross-sectoral research community.
As a part of this new direction for the foundation, RWJF launched a new grant program, Data Across Sectors for Health (DASH) to support collaborations focused on improving the health of communities, promoting health equity, and contributing to a culture of health by strengthening information sharing, engaging additional sectors, and building sustainable capacity. In September 2015, DASH released the results of an environmental scan investigating multi-sector collaborations among health care, public health, and other community systems. The scan revealed two major types of challenges facing those engaged in such collaborations—technical/operational challenges and relationship management challenges—and identified a number of barriers preventing collaborators from addressing the challenges, including time constraints, funding, lack of needed expertise.
In January 2016, DASH awarded ten grants totaling $2 million to support projects that improve health through multi-sector data sharing collaborations, including, for example, a grant to the Center for Health Care Services in San Antonio, TX, to use a connected information system to enable spontaneous, shared treatment of adults with severe mental illness.
Simultaneously, the Office of the National Coordinator funded the Community Health Peer Learning Program, a national peer learning collaborative addressing community-level population health management challenges through expanded collection, sharing, and use of electronic data. The CHP Program, directed by AcademyHealth, aims to advance progress toward population health improvements through the expanded capture, sharing, and use of electronic health data from diverse sectors. Engaging ten Participant Communities and five Subject Matter Expert (SME) communities in the peer learning collaborative, the CHP Program encourages demonstrations of how linking critical information within and outside of health care can
These programs are tackling significant challenges. As one example, the Louisiana Public Health Institute is administering a project investigating better ways to identify and intervene for individuals with severe and persistent mental illness or other vulnerable individuals who are high-utilizers of social services. The project is looking across multiple sectors and services including the criminal justice system, emergency departments, emergency medical or crisis response services, and social services agencies in order to develop a comprehensive picture of strategies to support individuals with severe mental illness.
The CHP Program is also conducting a limited environmental scan of ongoing community-based transformation efforts. Acknowledging the progress-to-date toward HITECH’s vision for health data exchange and interoperability, data sharing and increasing opportunities herein, the scan characterizes the growing movement for improving community health through multi-sector collaboration (often health IT-enabled). The results show there are 17 national programs working on these issues. Each of which is funding multi-year programs to advance community health transformation at the regional and local level. Though many programs encourage data sharing and use, additional programmatic aims include, systems redesign, increased geographic scale, leadership development, financing and investment, and the reduction of equities/disparities, among others. More than 300 local initiatives focused on regional health transformation have received support from these 17 NPOs.
Taken together, these community-based efforts also will help to inform national strategy and align with other delivery system reform efforts driving toward better care, smarter spending, and healthier people. In this spirit, DASH and the CHP Program have partnered to form All In: Data for Community Health, an effort dedicated to building a data movement that empowers communities to address the social determinants of health. As part of this effort, DASH and the CHP Program are co-creating a “network of networks” to share knowledge across initiatives and cultivate peer-to-peer learning opportunities.
Most recently, payment reform efforts are beginning to address population health from a systems perspective. Most value-based payment models focus on clinical or payer populations and only address the needs and outcomes of individuals. These may include programs for individuals served by the specific organizations or provider groups implementing a model, or individuals with a defined clinical condition. Yet, payment and financing models do not yet adequately support community-wide population health. They do not appropriately reward health care providers or other service providers, or encourage collaboration for broad population health improvements in a specific geographic area (“payment for population health”).
In order to identify where health plans and providers are addressing population health and to stimulate a deeper conversation about promising practices, the Robert Wood Johnson Foundation is supporting a new initiative, Payment Reform for Population Health. Led by AcademyHealth and informed by subject matter experts, practitioners, and key stakeholders, the initiative is exploring current efforts and successes related to payment reform activities that support community-wide (i.e., geopolitically-based) population health improvement. Intersections between programs that support non-clinical social determinants of health, such as housing, employment, food insecurities, and other services are of special interest. In the near-term the project team aims to understand the marketplace characteristics, organizational structure, mission and business case for payment reforms that will improve population health, as well as other supporting elements that encourage and enable such approaches.
The health sciences community has engaged in a fierce debate this past year regarding pathways to achieve the goals of open science—that is, making scientific research, data, and methods more broadly available so that others might learn, collaborate, and contribute. The most notable debate of the year was ignited by an editorial in the New England Journal of Medicine (NEJM) by Dr. Dan Longo and Dr. Jeffrey Drazen in which they presented their concern about the emergence of “research parasites” – that is, “people who had nothing to do with the design and execution of [a] study but use another group’s data for their own ends.” While NEJM editors did issue a clarification making clear that the journal supports this year’s data sharing proposal by the International Committee of Medical Journal Editors, it’s clear that evolving attitudes around data sharing have significant implications for the progress of open science and team science in health care.
There have also been a number of encouraging signs that attitudes and practices towards data sharing in health care are changing – for the better. For example, a March 2016 paper in Nature Biotechnology presents a data-sharing framework designed to respect the choices of participants and data providers in a Parkinson’s study while also qualifying researchers who would like to access that data for future research. The paper’s authors also present their view that “data sharing has powerful potential to accelerate discovery.”
The drive towards “team science” as an approach to solving multifaceted problems and achieving insights not possible by lone researchers continues. Last September the National Academies of Sciences, Engineering, and Medicine held a symposium titled "Enhancing the Effectiveness of Team Science" where they presented an expert report noting that today “90 percent of all science and engineering publications are authored by two or more individuals” and that most papers are now co-authored by between 6 and 10 individuals from more than one institution. In further support of team science, the National Institutes of Health has sponsored a Science of Team Science conference over the past five years, which has created an international venue to understand the characteristics of successful scientific teams.
Another emerging trend that we’ve seen more evidence of over the last 12 months is the lure of the private sector for data scientists from academia and other clinical or research backgrounds. A late 2015 article from Nature, for example, described a number of examples of top scientists leaving more traditional research positions for a chance to be part of Google’s Verily unit. As the article explains, there are a number of reasons beyond money why private technology companies might appeal to top scientists, including greater access to resources and different metrics of success.
Authors of a more recent commentary argue that the move of top talent into technology companies, and indeed the broader move by technology companies into health care, may actually widen inequalities and harm research by enabling more closed systems in which companies control the tools and methods used to match people's digital health profiles to specific services. However, it remains unclear at the moment just how widespread this shift toward the private sector is and just what it may mean for open science.
Findings from the EDM Forum’s own social network analyses assessing the trajectory of collaboration and team science specifically in the field of health data science revealed an average of 5.8 authors per paper. The study reviewed the characteristics of collaborative networks and emerging lessons about the dissemination of evidence, methods, and tools from a unique cohort of federally-funded projects designed to increase the nation’s capacity to use electronic clinical data infrastructure for CER, PCOR, and the development of delivery system science. This analysis has shown an increase in collaborative and team science over the span of four years and confirms research conducted in this space continues to involve multiple collaborators and collaborating organizations.
One of the most striking examples of the power of open and team science in recent years is a paper by an international collaboration published in the Proceedings of the National Academy of Sciences examining levels of consistency in therapies. Researchers from the Observational Health Data Sciences and Informatics (OHDSI) collaboration used their Common Data Model and a common analysis protocol to examine treatment pathways for type 2 diabetes, hypertension, and depression in four different countries (Japan, South Korea, the United Kingdom, and the United States). Using electronic health records and administrative claims data on 250 million patients in the four countries, the study revealed wide variation in treatments prescribed for the same condition. Perhaps more importantly, the study confirmed the feasibility and usefulness of large-scale observational research across widely different databases.
The efforts and progress described above are all encouraging signs for open and team science in health care. However, there is still a significant amount of inertia and numerous challenges to overcome. As noted in a recent eGEMs editorial, ongoing challenges to open science include significant cultural or ‘sociotechnical’ hurdles including the inability to unambiguously cite all scientific material, a lack of access to data and code used in research, and lack of consistent quality control of peer review.
"Science is not a major or a career. It is a commitment to a systematic way of thinking, an allegiance to a way of building knowledge and explaining the universe..."
Atul Gawande, surgeon, writer, and public health researcher
For more information on the EDM Forum, and to access more than 350 resources developed by the Community, please visit our website at www.edm-forum.org or refer to the following ‘quick links’:
Recommended Citation: Padgham D., Cole E., Connors D., Edmunds M., Gautam S., Kang K., Kennedy S., Martinez-Vidal E., Rein A., Waite C., Weiss S., Holve E. (EDM Forum), "EDM Forum Review" (2016, September). Washington, D.C. http://www.edm-forum.org/review/.
© 2016 EDM Forum - The EDM Forum is the result of a cooperative agreement between AcademyHealth and the Agency for Healthcare Research and Quality (AHRQ) under grant #U13 HS19564. Current support for the EDM Forum comes from AHRQ grant #U18 HS022789.