Author + information
- Received November 29, 2017
- Revision received February 8, 2018
- Accepted March 13, 2018
- Published online June 4, 2018.
- Abhinav Sharma, MDa,b,c,∗ (, )
- Robert A. Harrington, MDc,
- Mark B. McClellan, MD, PhDd,
- Mintu P. Turakhia, MD, MASc,e,
- Zubin J. Eapen, MD, MHSa,
- Steven Steinhubl, MDf,
- James R. Mault, MDg,
- Maulik D. Majmudar, MDh,
- Lothar Roessig, MDi,
- Karen J. Chandross, PhDj,
- Eric M. Green, MD, PhDk,
- Bakul Patel, MS, MBAl,
- Andrew Hamer, MBChBm,
- Jeffrey Olgin, MDn,
- John S. Rumsfeld, MD, PhDo,
- Matthew T. Roe, MD, MHSa and
- Eric D. Peterson, MD, MPHa
- aDuke Clinical Research Institute, Duke University, Durham, North Carolina
- bMazankowski Alberta Heart Institute, University of Alberta, Edmonton, Alberta, Canada
- cDepartment of Medicine, Stanford University, Stanford, California
- dDuke-Margolis Center for Health Policy, Duke University, Durham, North Carolina
- eVeterans Affairs Palo Alto Health Care System, Palo Alto, California, and Center for Digital Health, Stanford, California
- fScripps Translational Science Institute, La Jolla, California
- gQualcomm Life, San Diego, California
- hHealthcare Transformation Lab, Massachusetts General Hospital, Boston, Massachusetts
- iBayer AG, Wuppertal, Germany
- jSanofi, Bridgewater, New Jersey
- kMyoKardia, South San Francisco, California
- lU.S. Food and Drug Administration, Silver Spring, Maryland
- mAmgen, Inc., Thousand Oaks, California
- nUniversity of California, San Francisco School of Medicine, San Francisco, California
- oAmerican College of Cardiology, Washington, DC
- ↵∗Address for correspondence:
Dr. Abhinav Sharma, Stanford University, Falk Cardiovascular Research Center, 870 Quarry Road Ext, Palo Alto, California 94304.
As we enter the information age of health care, digital health technologies offer significant opportunities to optimize both clinical care delivery and clinical research. Despite their potential, the use of such information technologies in clinical care and research faces major data quality, privacy, and regulatory concerns. In hopes of addressing both the promise and challenges facing digital health technologies in the transformation of health care, we convened a think tank meeting with academic, industry, and regulatory representatives in December 2016 in Washington, DC. In this paper, we summarize the proceedings of the think tank meeting and aim to delineate a framework for appropriately using digital health technologies in healthcare delivery and research.
Broadly defined, digital health describes using digital information, data, and communication technologies to collect, share, and analyze health information for purposes of improving patient health and health care delivery (1–10). More than 20 years ago, health care’s industrial age (characterized by physicians ruling over tertiary care centers) was predicted to shift to an information age (characterized by patients being at the center of health care delivery) (1,2,11). Until recently, health care has remained relatively isolated from the digital and mobile technology revolution. In parallel with the creation of more powerful, versatile, and low-cost digital health technologies, titanic shifts in U.S. health care have been stimulated by the $27 billion federal investment under the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. Venture capital and private investments in digital health have also risen significantly, exceeding $3.5 billion dollars in 2017 (Figure 1) (2).
Digital health technologies also have the potential to accelerate, streamline, and optimize clinical research operations while reducing costs. These technologies could facilitate and advance more conventional randomized clinical trials (RCTs), which is particularly necessary because RCTs are becoming increasingly expensive and complex, are slow to complete, and take an extensive amount of time to implement results into practice (12). Integration of digital technologies into clinical trials remains to be explored, but there is a critical need to evaluate these technologies in order to conduct more streamlined and pragmatic trials. Perhaps the benefits of digital health technologies for both clinical care and research will be appreciated when certain challenges have been resolved regarding data quality, safety, accessibility, privacy, and the need for regulation. Furthermore, the negative consequences of integrating digital technologies into clinical workflows, such as a paradoxical decrease in productivity, the decline of patient-physician interaction, and the creation of silos within health care teams, require further study (13).
To establish an understanding of digital health technologies in health care delivery and clinical research, cross-sector stakeholders from academia, industry, professional organizations, regulatory bodies, and government agencies convened for a think tank meeting in December 2016 in Washington, DC (for a complete list of meeting attendees, please refer to Online Table 1). The aims of this meeting were as follows: 1) to understand the current landscape of digital health technology use in health care delivery and clinical trials; 2) to identify issues and barriers to the development and adoption of these technologies; and 3) to identify potential solutions using perspectives from providers, industry, regulatory agencies, payers, and professional societies.
Current Digital Landscape in Health care Delivery and Clinical Research Conduct
Use of digital health technologies as a diagnostic tool
The rapid growth in computing power of digital technologies has enabled the development of machine-learning algorithms that are used by companies such as Facebook, Amazon, and Google to optimize search queries and advertisement placements. These algorithms arose from the development of artificial neural networks in the 1940s and 1950s (4), which attempted to simulate the human brain’s neuronal response to external stimuli in order to perform learning and pattern recognition (4). One example of the utility of these neural networks arises from a convolutional neural network analysis conducted by Gulshan et al. to aid in the detection of diabetic retinopathy (5,6). In the field of cancer, the use of deep learning has expanded into detection of lymph node metastases after the diagnosis of breast cancer using whole-slide pathology (14). A number of studies are under way that are attempting to leverage consumer wearable sensing technologies to aid in clinical diagnosis of common diseases. As an example, the Apple Heart Study is evaluating whether the Apple Watch can identify irregular heart rhythms such as atrial fibrillation (15). Given the significant clinical and treatment implications of identifying atrial fibrillation, this study demonstrates the potential for consumer digital products in health care. Although these results are encouraging, next steps include determining the feasibility of applying these and other algorithms in clinical screening programs and existing care models and assessing their impact on care and outcomes.
Digital health as a disease management and decision support tool
Digital health technologies can potentially improve health outcomes by increasing patient engagement in self-care and caregiver care, closing communication gaps, and personalizing services to meet patient needs. For example, BlueStar was one of the first apps (applications) that received U.S. Food and Drug Administration (FDA) clearance as a diabetes mellitus management platform. The app requires a prescription from a physician and enables patients to titrate insulin dosing by using the proprietary insulin calculator (7). Decision support apps allow for disease management to occur outside of clinics while empowering patients to optimize their health conditions, but a key driver in their success is how user-friendly or complex these devices and electronic communications are to the patient. The rapid development of disease management tools has resulted in increased regulatory recommendations. Recent draft guidance from the FDA has addressed some of the issues regarding the use of clinical and patient decision support tools (16). The FDA regulates software that meets the definition of a device under section 201 (h) of the Federal Food, Drug, and Cosmetics (FD&C) Act. This includes decision support software that aids in diagnosis. However, section 3060 (a) of the Cures Act amended this definition to exclude certain decision support software from the definition of a medical device (16). Such software that presents diagnosis or treatment recommendations must enable health care professionals to independently review the basis for such recommendations; the intention is not for health care professionals to rely primarily on these recommendations to impact clinical decision making. In the event that decision support software does not show the basis for such recommendations, then the software could be subject to regulation as a medical device.
Digital health to improve research recruitment
Patient recruitment is often noted as the Achilles heel of clinical research studies, particularly RCTs; poorly recruiting trials often face growing costs, increased time to completion, or even termination (12). The potential utility in harnessing digital health technologies as a means to enhance trial recruitment was demonstrated by the MyHeart Counts Cardiovascular Health Study, which recruited 48,986 patients from March 2015 to October 2015 using the Apple Research Kit (17). The Health eHeart study, a cardiology-focused e-cohort, has enrolled >140,000 (consented) participants and used digital technology to engage a large cohort over time, with participants enrolling in additional studies and substudies, along with integration of multiple wearable sensors into the data collection. In addition to app-based recruitment, leveraging social media represents another avenue for patient enrollment (18,19). MyHeart Counts and Health eHeart demonstrate the potential use of digital technologies in recruitment of patients, but whether such tools can be used to enhance enrollment in more conventional trials remains to be evaluated.
Informed consent on mobile devices
Informed consent respects a patient’s right to decide whether participation in the research or health care intervention aligns with their interests. In the context of clinical research, informed consent protects the welfare and rights of potential study participants. Although informed patient consent is a vital part of patient engagement, an inherent tension has arisen as the informed consent process has become increasingly regulated over the years. Consequently, patients often do not truly understand study details, which results in increased patient confusion regarding the risks and benefits. Furthermore, the excessive use of legal and medical jargon for medicolegal protection results in potential study subjects being dissuaded from research participation (20,21). To address these issues, innovative strategies such as electronic informed consent, mobile app-based consent, and video consent are being used (20). For example, the Patient and Provider Assessment of Lipid Management (PALM) registry (22) used a novel tablet-based consent procedure that involved sequential interactive videos and patient quizzes. Despite the perceived advantages of these strategies, issues such as ensuring subjects do not “click-through” agreements, difficulties in identifying the person consenting, and assessing capacity for understanding will have to be addressed before these strategies can be widely adopted.
Digital clinical study endpoints
Digital technology can collect novel endpoints for clinical studies that might predict or yield new insight into the risks or benefits of therapies. For example, the NEAT-HFpEF (Nitrate’s Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction) trial randomized 110 patients with heart failure with preserved ejection fraction to a 6-week dose-escalation regimen of isosorbide mononitrate or placebo (23). The primary endpoint was daily activity level, as quantified by 2 kinetic activity monitors that contained high-sensitivity triaxial accelerometers. The nitrate group had a lower activity level than the placebo group (−439 accelerometer units, 95% confidence interval: −792 to −86; p = 0.02) despite no significant between-group differences in the 6-min walk distance. One potential explanation of these results is that traditional endpoints used in many phase 2 or 3 clinical trials, such as the 6-min walk distance, were not sensitive enough to pick up a worsening of activity as demonstrated by the accelerometer measurement. Furthermore, collecting data on these results is important in light of 2 recent studies that reported a lack of outcomes benefit associated with nitrate therapy in heart failure with reduced ejection fraction (24) and in heart failure with preserved ejection fraction (25). The daily activity measures as obtained by the accelerometers provided superior prognostic information compared with traditionally captured measures of functional status (26). This novel class of digital biomarkers might complement traditional markers, with the distinct advantage of providing dense longitudinal data outside the clinical encounter, and these data support additional insights that might emerge from continuous collection of digital data.
Challenges of Using Digital Health Technologies in Health care Delivery and Clinical Research
Although digital health technologies have great potential to optimize health care delivery and clinical research conduct (Table 1), several barriers significantly impede broad implementation, including quality of data, productivity paradox, accessibility, privacy, and the need for regulation.
Quality of data and interoperability
A consistent challenge is ensuring adequate data quality and robustness. Big data is a term that reflects the use of large data volumes (through electronic health records [EHRs] or other sources) for understanding population risk, treatment responses, and trends in health care utilization (28). Nonetheless, the practical application of big data in health care is currently rudimentary (29–31). Data quality has been identified as one of the biggest issues with broad implementation of large data analytic programs among the “five V’s” of big data: volume, velocity, variety, veracity, and value (29). Use of large public data sources, such as Twitter, has attempted to identify drug safety events and even track cardiovascular disease (32,33). Yet many key issues remain, including lack of data synchronicity and interoperability in data sources, as well as whether there is enough signal given the extensive background noise in large datasets. Furthermore, the generalizability, methodological standards, and ethics in linking large data with individual medical information require rigorous exploration (34).
Productivity paradox: Will implementation of digital health technologies reduce productivity?
The lack of increase in productivity associated with a rise in information technology and increased computational capacity (i.e., the productivity paradox) has long been observed in sectors outside of medicine. In the 1970s and 1980s, the computing capacity of the U.S. economy increased >100-fold, yet the productivity growth fell to less than one-half the rate of the preceding 25 years. The principal causes of this paradox involve information technology mismanagement, inadequate infrastructure to support new technology, and poor technology usability (35). Beyond linking data to EHRs, an additional challenge includes harmonizing data, so that disparate data sources are speaking the same language or code. In the context of health care systems, the productivity paradox is evident in EHRs: In the United States, for approximately every hour spent on clinical care by a physician, an additional 2 h were required for EHR documentation (36). The addition of data to the EHR that are generated from wearable monitoring devices, claims data, in-body sensors, social media, or patient report will not improve care unless tools are created to convert this data input into actionable information (1).
Privacy: Is medical information secure enough?
Privacy and data security have emerged as some of the most contentious issues in the conversation regarding digital health technologies. Recent data breaches in large companies such as Amazon, Sony, and Equifax have cast doubt on the ability of large data systems to keep sensitive information secure. A cross-sectional survey of 5,331 patients in primary and specialist care practices (from 2012 to 2013) in the United Kingdom suggested that 79% of patients worry about EHR security, and 71% thought the National Health Service was unable to guarantee EHR safety (37). Pragmatic issues such as access to sensitive health data if a mobile phone or computer is stolen, or the risk of hacking, will need to be thoughtfully acknowledged and addressed. The recent wave of health care-related data hacking, from hospital computer systems being held for ransom to remote unauthorized modification of implantable cardioverter-defibrillators (38), suggests that digital health still requires significant advances in data safety. Publications by the U.S. Department of Commerce have recently provided guidance on data safety and security strategies and should be considered in the development cycle of digital health technologies (39).
Issues in the context of clinical research
The use of digital technologies in clinical trials has significantly expanded over the years (Figure 2). Although digital health technologies can streamline many aspects of clinical research operations (Table 1), additional issues arise in the adoption of such technologies. Traditional clinical trial operations have multiple checks and balances to optimize the capture of clinical events, from safety monitoring to clinical event committee adjudication and frequent site-based audits. What is unclear is how missing data will be retrieved if an app or device that is being used in a trial fails. Whether or not traditional statistical imputation methods can be used to adequately account for missing data that are not collected through the devices remains vague. Furthermore, as the use of digital health devices evolves, patient adherence to these technologies could diminish with time; the impact of this remains unclear, especially if the technology is being used to capture clinical endpoints.
Regulatory perspective: Do we need to regulate digital health technologies?
The rapid development of digital health technologies has created challenges for regulatory agencies such as the FDA. An Instant Blood Pressure app claiming to measure blood pressure using the microphone and the iPhone LED light placed on the chest of a consumer was among the highest-grossing apps in the health section of the iTunes App Store; however, a validation study identified that this app was highly unreliable, with 77.5% of hypertensive individuals receiving falsely low readings (40). Two apps that claimed to reduce acne through blue lights emitted from smartphones received fines by the Federal Trade Commission and were removed from the iTunes App Store and the Android Marketplace (41). To address these concerns regarding the role of mobile medical apps, the FDA has released clarification based on the recent passing of the 21st Century Cures Act (42).
In general, the FDA will regulate technologies that meet the definition of a device under Section 201(h) of the FD&C Act. A mobile medical app is a mobile app that meets the definition of a medical device, which is intended to: 1) be used as an accessory to a regulated medical device; or 2) transform a mobile platform into a regulated medical device. The intended use of a mobile app determines whether it meets the definition of a device. In general, the FDA has taken a hands-off approach to the regulation of low-risk products, particularly those that only promote a healthy lifestyle or promote a well-known association between a healthy lifestyle and a certain disease or condition. Furthermore, the FDA intends to exercise enforcement discretion (i.e., the FDA does not intend to enforce requirements under the FD&C Act) for mobile apps that might meet the definition of medical devices but pose lower risk to the public. These mobile apps might be intended for use in the diagnosis of diseases or in the cure, mitigation, treatment, and prevention of disease. The FDA primarily intends to apply its regulatory oversight to those mobile apps that are medical devices and whose functionality could pose a risk to patients’ safety if the mobile app were to not function as intended (Online Table 2).
The Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 will shift reimbursement from the traditional fee-for-service/episodic payment structure to a more value-based payment model (Public Law No. 114-10) (43,44). MACRA replaces the previous Medicare sustainable growth rate formula, which determined updates to the physician fee schedule with a new Merit-Based Incentive Payment System. The alternative payment model aims to encourage evidence-based medicine use, develop high-quality care, and improve cost saving and coordination of care (44). By 2018, the U.S. Centers for Medicare & Medicaid Services aims to have 50% of reimbursements tied to alternative payment models (44,45). A framework for the transition from fee-for-service to alternative payment model was outlined by the Health Care Payment Learning and Action Network, a Centers for Medicare & Medicaid Services network of private and public entities (Figure 3) (46).
A number of issues arise when evaluating digital technologies from the payer point-of-view. Most importantly, there is limited evidence on the effectiveness of most digital health technologies. Regulators need evidence on safety and effectiveness when a technology is used to diagnose or treat a condition. On the other hand, payers require evidence of value and cost-effectiveness for coverage decisions. Moving forward, the demonstration of health care value and cost-effectiveness will be necessary before any such technologies can be widely implemented.
Possible Solutions and Future Directions
Despite the myriad of complex challenges, several potential solutions emerge that could accelerate utilization of these technologies in health care delivery and clinical trials (Table 2, Central Illustration).
Development of innovation networks
The development of innovation networks to rapidly test new innovations, validate findings from other studies, and provide evidence of value and cost-effectiveness would significantly accelerate the pipeline from digital bench to bedside. An example of this type of network is the emerging innovation collaboration between Stanford University, Duke University, Scripps Translational Science Institute, Massachusetts General Hospital, and the University of California-San Francisco. As an example, Duke Forge and the University of California-San Francisco’s Eureka project enable researchers to rapidly access multiple cohorts of clinical data to prototype digital health technology. Initiatives such as the Patient-Centered Outcomes Research Institute and the Patient-Centered Research Foundation have also encouraged multidisciplinary collaboration between academic institutes, governmental agencies, and private initiatives. As highlighted by the American College of Cardiology Roadmap for Innovation, academic research can drive the development and implementation of digital health technologies by identifying priority problems to solve, conducting the testing and validation of digital technology in the clinical setting, recognizing barriers to implementation of such digital technologies once they are validated, and establishing system- and policy-level strategies to ensure appropriate knowledge translation of the digital health technologies. Such networks must be inclusive toward new members, be flexible, and encourage the testing of innovations and digital health technologies for use in routine health care and clinical trials.
Collaboration with regulatory agencies
The recently released FDA Digital Health Innovation Action Plan (47) highlights the broadening importance of regulatory, industry, and academic collaborations. As an example, the FDA Digital Health Software Precertification (PreCert) Pilot Program (48) is developing new approaches to how researchers and entrepreneurs can streamline digital health development. The Center for Devices and Radiological Health would pre-certify eligible digital health developers who demonstrate a culture of quality and organizational excellence. Pre-certified developers could then qualify to have lower-risk devices enter the market without additional FDA review or undergo a more streamlined pre-market review. Researchers and industry collaborations could leverage such programs to rapidly move digital health innovations from development into broad implementation. In addition, the FDA has encouraged continual engagement with patient groups, academic researchers, industry, and technology developers to ensure on-going development of evidence standards (49).
Additional opportunities to collaborate with regulatory agencies involve the development of endpoints through validated devices from patients at home. For example, the MyHeart Counts Cardiovascular Health Study app (17) has the ability to conduct a 6-min walk test, which patients can perform from the convenience of their home. The subsequent use of these apps in clinical trials can significantly streamline the costs of studies. Strategies to validate such apps can be discussed through early engagement with regulatory agencies.
Role for professional societies
Professional societies such as the American College of Cardiology and the American Heart Association have a critical role to play in the advancement of digital health technologies by identifying critical knowledge gaps and defining research standards. Professional societies can also play a significant role in establishing the framework and quality metrics for studies in the digital health space, defining the need for robust efficacy, safety, and cost-effectiveness data. Societies can help identify professional standards for the use of digital health innovations in clinical practice and delineate the expectations for health care providers. Furthermore, these societies can play a major role in dissemination of study results while providing education to patients and providers.
Expanding role of public-private partnerships
There is an increasing role for public-private partnerships to drive knowledge translation and generate evidence from clinical trials. The Clinical Trials Transformation Initiative (CTTI) is a public-private partnership created to develop and drive adoption of practices that will increase the quality and efficiency of clinical trials. CTTI is composed of more than 80 governmental, academic, and industry organizations in addition to patient advocacy groups, professional societies, investigator groups, and other interested parties. CTTI has launched the Mobile Clinical Trials program, which has publicly released recommendations regarding identifying, selecting, and developing novel endpoints generated by mobile technologies for use in clinical trials (50). Such public-private partnerships play a significant role in driving the clinical research needed to demonstrate the efficacy and safety of new digital health technologies.
Digital health technologies have significant potential to revolutionize healthcare delivery, transform clinical trials, and improve health outcomes. There are numerous challenges that hinder the rapid adoption of these technologies, including data quality and robustness, patient safety, ease of use, privacy concerns, and accessibility. Congruency is limited between tech/medtech (social media, and so on) and medical society communication principles (guidelines, peer-reviewed publishing, and so on). Despite these challenges, conversations surrounding digital health technologies represent a rare alignment of stakeholders including patients, academic researchers, industry, payers, and regulators. Close and early collaborations between stakeholders will be required to ensure that digital health technologies not only improve outcomes but add value to healthcare systems, decrease cost, and improve quality of care. As digital technologies become less expensive and more readily available, establishing a framework for their appropriate use will be required. Furthermore, despite the rapid turnover of technology and the increasing push from technology developers and investors to implement these technologies in routine clinical care, rigorous standards of evidence are needed. As health care continues to move from the industrial age to the information age, researchers, scientists, clinician, payers, and regulators must ensure that we remain cognizant of our ultimate goal, which is to help patients live longer and feel better.
The authors thank Marelle Molbert for her contributions in organization of the think tank; Carolyn Moore Arias, MPH, for her project management contributions; and Erin Campbell, MS, and Peter Hoffmann, BA, for their editorial contributions. Ms. Molbert, Ms. Arias, Ms. Campbell, and Mr. Hoffmann did not receive compensation for their assistance, apart from their employment at the institution where this study was conducted.
This manuscript was funded internally by the Duke Clinical Research Institute (Durham, North Carolina). Funding support for the think tank meeting was provided through registration fees from Amgen, AstraZeneca, Bayer AG, Janssen Pharmaceutical Companies of Johnson & Johnson, MyoKardia, Sanofi, St. Jude Medical, Medtronic, and Qualcomm Life. No government funds were used for this meeting. Dr. Sharma has received research grant support from the American Heart Association (AHA) Strategically Focused Research Network–Heart Failure (16SFRN30180010), Alberta Innovates Health Solution Clinician Scientist fellowship, the European Society of Cardiology Young Investigator research grant, Roche Diagnostics and the Canadian Cardiovascular Society Bayer Vascular award, BMS-Pfizer, and Takeda. Dr. Harrington has a research relationship with Apple (Apple Watch Study); has received consulting fees from Amgen, Bayer, Gilead, Merck, MyoKardia, and The Medicines Company; has received fees for consulting and educational programs from WebMD; has received grant funding from AstraZeneca, Bristol-Myers Squibb, CSL Limited, GlaxoSmithKline, Janssen, Merck, Novartis, Portola, Sanofi, and The Medicines Company; holds equity in Element Science and Scanadu; and holds an unpaid seat on the board of directors of the AHA. Dr. McClellan has received personal fees from Johnson & Johnson. Dr. Turakhia has received research support from Amazon, AstraZeneca, Bristol-Myers Squibb, Medtronic, the AHA, Apple, Janssen, Cardiva Medical, and Boehringer Ingelheim; and holds equity in iBeat, AliveCor, Metrica Health, Zipline Medical, and CyberHeart; has received consulting fees from Abbott, Medtronic, iRhythm, Precision Health and Boehringer Ingelheim; and has received honoraria for presentations from Medscape. Dr. Steinhubl has received grant support from the National Institutes of Health/National Center for Advancing Translational Sciences grant UL1TR001114 and a grant from the Qualcomm Foundation; and has served as a medical advisor for DynoSense, EasyG, Spry Health, and FocusMotion. Dr. Mault is an employee of Qualcomm Life. Dr. Majmudar has received consulting fees from AliveCor, HUINNO, MC10, and Nokia; holds ownership in BioFourmis, Cardiogram, and HiLabs; and has conducted personal research with Echosense. Dr. Majmudar has received research grants from GE Healthcare. Dr. Roessig is a full-time employee of Bayer AG, Wuppertal, Germany. Dr. Chandross is a full-time employee of Sanofi. Dr. Green has an equity stake in MyoKardia. Mr. Patel owns shares in Boston Scientific. Dr. Hamer is an employee of Amgen Inc.; and owns Amgen shares. Dr. Olgin has received research support from ZOLL and the National Institutes of Health. Dr. Roe has received research grants from AstraZeneca, Eli Lilly & Co., Janssen Pharmaceuticals, Sanofi, Daiichi-Sankyo, Ferring Pharmaceuticals, and The Familial Hypercholesterolemia Foundation; speaker honoraria from Amgen and Bristol-Myers Squibb; and consultant/advisory board fees from AstraZeneca, Eli Lilly & Co., Daiichi-Sankyo, Amgen, PriMed, Myokardia, Boehringer Ingelheim, and Merck & Co. Dr. Peterson has received grants from Amgen Inc.; grants and personal fees from AstraZeneca, Merck & Co., and Sanofi; and has served as a consultant for SignalPath. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- Clinical Trials Transformation Initiative
- electronic health records
- U.S. Food and Drug Administration
- Federal Food, Drug, and Cosmetics Act
- Health Information Technology for Economic and Clinical Health Act
- randomized clinical trial
- Received November 29, 2017.
- Revision received February 8, 2018.
- Accepted March 13, 2018.
- 2018 American College of Cardiology Foundation
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