Partnership development of the COVID-19 Front Door: a best evidence resource.

This project describes the creation of a single searchable resource during the pandemic, called the COVID-19 Best Evidence Front Door, with a primary goal of providing direct access to high-quality meta-analyses, literature syntheses, and clinical guidelines from a variety of trusted sources. The Front Door makes relevant evidence findable and accessible with a single search to aggregated evidence-based resources, optimizing time, discovery, and improved access to quality scientific evidence while reducing the burden of frontline health care providers and other knowledge-seekers in needing to separately identify, locate, and explore multiple websites.

Respect, justice and learning are limited when patients are deidentified data subjects.

Introduction: Critical for advancing a Learning Health System (LHS) in the U.S., a regulatory safe harbor for deidentified data reduces barriers to learning from care at scale while minimizing privacy risks. We examine deidentified data policy as a mechanism for synthesizing the ethical obligations underlying clinical care and human subjects research for an LHS which conceptually and practically integrates care and research, blurring the roles of patient and subject. Methods: First, we discuss respect for persons vis-a-vis the systemic secondary use of data and tissue collected in the fiduciary context of clinical care. We argue that, without traditional informed consent or duty to benefit the individual, deidentification may allow secondary use to supersede the primary purpose of care. Next, we consider the effectiveness of deidentification for minimizing harms via privacy protection and maximizing benefits via promoting learning and translational care. We find that deidentification is unable to fully protect privacy given the vastness of health data and current technology, yet it imposes limitations to learning and barriers for efficient translation. After that, we evaluate the impact of deidentification on distributive justice within an LHS ethical framework in which patients are obligated to contribute to learning and the system has a duty to translate knowledge into better care. Such a system may permit exacerbation of health disparities as it accelerates learning without mechanisms to ensure that individuals' contributions and benefits are fair and balanced. Results: We find that, despite its established advantages, system-wide use of deidentification may be suboptimal for signaling respect, protecting privacy or promoting learning, and satisfying requirements of justice for patients and subjects. Conclusions: Finally, we highlight ethical, socioeconomic, technological and legal challenges and next steps, including a critical appreciation for novel approaches to realize an LHS that maximizes efficient, effective learning and just translation without the compromises of deidentification.

Summary of fourth annual MCBK public meeting: Mobilizing computable biomedical knowledge-metadata and trust.

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK.

Addressing the Covid-19 pandemic and future public health challenges through global collaboration and a data-driven systems approach.

Covid-19 has already taught us that the greatest public health challenges of our generation will show no respect for national boundaries, will impact lives and health of people of all nations, and will affect economies and quality of life in unprecedented ways. The types of rapid learning envisioned to address Covid-19 and future public health crises require a systems approach that enables sharing of data and lessons learned at scale. Agreement on a systems approach augmented by technology and standards will be foundational to making such learning meaningful and to ensuring its scientific integrity. With this purpose in mind, a group of individuals from Spain, Italy, and the United States have formed a transatlantic collaboration, with the aim of generating a proposed comprehensive standards-based systems approach and data-driven framework for collection, management, and analysis of high-quality data. This framework will inform decisions in managing clinical responses and social measures to overcome the Covid-19 global pandemic and to prepare for future public health crises. We first argue that standardized data of the type now common in global regulated clinical research is the essential fuel that will power a global system for addressing (and preventing) current and future pandemics. We then present a blueprint for a system that will put these data to use in driving a range of key decisions. In the context of this system, we describe and categorize the specific types of data the system will require for different purposes and document the standards currently in use for each of these categories in the three nations participating in this work. In so doing, we anticipate some of the challenges to harmonizing these data but also suggest opportunities for further global standardization and harmonization. While we have scaled this transnational effort to three nations, we hope to stimulate an international dialogue with a culmination of realizing such a system.

Reconsidering hospital EHR adoption at the dawn of HITECH: implications of the reported 9% adoption of a "basic" EHR.

OBJECTIVE: In 2009, a prominent national report stated that 9% of US hospitals had adopted a "basic" electronic health record (EHR) system. This statistic was widely cited and became a memetic anchor point for EHR adoption at the dawn of HITECH. However, its calculation relies on specific treatment of the data; alternative approaches may have led to a different sense of US hospitals' EHR adoption and different subsequent public policy. MATERIALS AND METHODS: We reanalyzed the 2008 American Heart Association Information Technology supplement and complementary sources to produce a range of estimates of EHR adoption. Estimates included the mean and median number of EHR functionalities adopted, figures derived from an item response theory-based approach, and alternative estimates from the published literature. We then plotted an alternative definition of national progress toward hospital EHR adoption from 2008 to 2018. RESULTS: By 2008, 73% of hospitals had begun the transition to an EHR, and the majority of hospitals had adopted at least 6 of the 10 functionalities of a basic system. In the aggregate, national progress toward basic EHR adoption was 58% complete, and, when accounting for measurement error, we estimate that 30% of hospitals may have adopted a basic EHR. DISCUSSION: The approach used to develop the 9% figure resulted in an estimate at the extreme lower bound of what could be derived from the available data and likely did not reflect hospitals' overall progress in EHR adoption. CONCLUSION: The memetic 9% figure shaped nationwide thinking and policy making about EHR adoption; alternative representations of the data may have led to different policy.

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence.

Inaccurate, untimely, and miscommunicated medical diagnoses represent a wicked problem requiring comprehensive and coordinated approaches, such as those demonstrated in the characteristics of learning health systems (LHSs). To appreciate a vision for how LHS methods can optimize processes and outcomes in medical diagnosis (diagnostic excellence), we interviewed 32 individuals with relevant expertise: 18 who have studied diagnostic processes using traditional behavioral science and health services research methods, six focused on machine learning (ML) and artificial intelligence (AI) approaches, and eight multidisciplinary researchers experienced in advocating for and incorporating LHS methods, ie, scalable continuous learning in health care. We report on barriers and facilitators, identified by these subjects, to applying their methods toward optimizing medical diagnosis. We then employ their insights to envision the emergence of a learning ecosystem that leverages the tools of each of the three research groups to advance diagnostic excellence. We found that these communities represent a natural fit forward, in which together, they can better measure diagnostic processes and close the loop of putting insights into practice. Members of the three academic communities will need to network and bring in additional stakeholders before they can design and implement the necessary infrastructure that would support ongoing learning of diagnostic processes at an economy of scale and scope.

Advantages of large medical record database for outcomes research: Insights into post-menopausal hormone therapy.

Approximately 25 years ago, our team initiated studies to determine whether outcome results from a large medical record database would yield valid results. We utilized the data in the United Kingdom (UK) General Practice Research Database (GPRD) to replicate the randomized controlled trial (RCT) study result and compared them to confirm the database results. The initial studies compared favorably, but some subsequent studies did not. This prompted development of a new strategy to determine and correct for unrecognized confounding in the database. This strategy divided outcome rates prior to initiation of therapy in the database study (which should include both identified and unidentified confounders) into the outcome rates during the treatment interval. When they differed from Cox-adjusted results, it reflected unrecognized confounding. We called this strategy Prior Event Rate Ratio (PERR)-adjusted outcome. One of our previously published observational studies replicated the Women's Health Initiative (WHI) RCT study of hormone therapy in post-menopausal women. Our study results replicated the WHI RCT results except it did not exhibit an increase in heart attack in contrast to the RCT. Furthermore, we could not evaluate death reliably since our analytic approach to overcome unrecognized confounding does not work for this outcome. In Volume 1, Issue 1 of the Learning Health Systems open access journal, we published a new study (titled "A new method to address unmeasured confounding of mortality in observational studies") that reported a novel death method, based on our prior methodology, that could analyze unrecognized confounding of the death outcome. This new methodology, termed Post Treatment Event Rate Ratio (PTERR), permitted a reliable examination of mortality in post-menopausal women undergoing hormone therapy. These results are reported in this manuscript. The study used the data from our previous observational study. It demonstrates that estrogen therapy markedly reduced death in post-menopausal women. This work also illuminates principles of database construction and correspondingly demonstrates the use of novel methodologies for obtaining valid results, which can be applied to enable learning from such databases. Work to advance such methodologies is essential to advancing the scientific integrity Core Value underpinning learning health systems (LHSs). Indeed, in the absence of such efforts to develop and refine methodologies for learning trustworthy lessons from real-world data, we risk inadvertently creating mis-learning systems.

Transforming the future of health together: The Learning Health Systems Consensus Action Plan.

The Learning Health Community is an emergent global multistakeholder grassroots incipient movement bonded together by a set of consensus Core Values Underlying a National-Scale Person-Centered Continuous Learning Health System developed at the 2012 Learning Health System (LHS) Summit. The Learning Health Community's Second LHS Summit was convened on December 8 to 9, 2016 building upon LHS efforts taking shape in order to achieve consensus on actions that, if taken, will advance LHSs and the LHS vision from what remain appealing concepts to a working reality for improving the health of individuals and populations globally. An iterative half-year collaborative revision process following the Second LHS Summit led to the development of the Learning Health Systems Consensus Action Plan.

Ensuring public health's future in a national-scale learning health system.

Data and information are fundamental to every function of public health and crucial to public health agencies, from outbreak investigations to environmental surveillance. Information allows for timely, relevant, and high-quality decision making by public health agencies. Evidence-based practice is an important, grounding principle within public health practice, but resources to handle and analyze public health data in a meaningful way are limited. The Learning Health System is a platform that seeks to leverage health data to allow evidence-based real-time analysis of data for a broad range of uses, including primary care decision making, public health activities, consumer education, and academic research. The Learning Health System is an emerging endeavor that is gaining support throughout the health sector and presents an important opportunity for collaboration between primary care and public health. Public health should be a key stakeholder in the development of a national-scale Learning Health System because participation presents many potential benefits, including increased workforce capacity, enhanced resources, and greater opportunities to use health information for the improvement of the public's health. This article describes the framework and progression of a national-scale Learning Health System, considers the advantages of and challenges to public health involvement in the Learning Health System, including the public health workforce, gives examples of small-scale Learning Health System projects involving public health, and discusses how public health practitioners can better engage in the Learning Health Community.