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Informed consent is the gateway to research participation. We report on the results of the formative evaluation that follows the electronic informed consent process for the All of Us Research Program. Of the nearly 250,000 participants included in this analysis, more than 95% could correctly answer questions distinguishing the program from medical care, the voluntary nature of participation, and the right to withdraw; comparatively, participants were less sure of privacy risk of the program. We also report on a small mixed-methods study of the experience of persons of very low health literacy with All of Us informed consent materials. Of note, many of the words commonly employed in the consent process were unfamiliar to or differently defined by informants. In combination, these analyses may inform participant-centered development and highlight areas for refinement of informed consent materials for the All of Us Research Program and similar studies.

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Contains fulltext : 252104.pdf (Publisher’s version ) (Open Access) Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P

This paper focuses on the significant role of government in promoting precision medicine and public health and the potential intersection with healthy living (HL) and population health. Recent research has highlighted the interplay between genes, environments and different exposures individuals and populations experience over a lifetime. These interactions between longitudinal behaviors, epigenetics, and expression of the human genome have the potential to transform health and well-being, even within a single generation. Precision medicine can elucidate these longitudinal interactions with a granularity that has not been previously possible across the exposome. Understanding the interactions between genes, epigenetics, proteins, metabolites, and the exposome may inform more evidence-based, effective policy, system, and environmental change to optimize individual and population health. Government has an important role in helping to fund primary research in precision medicine and precision public health, as well as creating and enforcing standards related to food systems, air quality, and access to health care, defining and optimizing measures of health care quality and safety, and ensuring data privacy standards and protections, interoperability, and integration with surveillance systems. Government partnership and collaboration with the non-profit and private sectors can optimize precision medicine and precision public health for the benefit of the United States and global population.

Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.

Informed consent is the gateway to research participation. We report on the results of the formative evaluation that follows the electronic informed consent process for the

Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

ImportanceThe All of Us Research Program hypothesizes that accruing one million or more diverse participants engaged in a longitudinal research cohort will advance precision medicine and ultimately improve human health. Launched nationally in 2018, to date All of Us has recruited more than 345,000 participants. All of Us plans to open beta access to researchers in May 2020.ObjectiveTo demonstrate the quality, utility, and diversity of the All of Us Research Program’s initial data release and beta launch of the cloud-based analysis platform, the cloud-based Researcher Workbench.EvidenceWe analyzed the initial All of Us data release, comprising surveys, physical measurements (PM), and electronic health record (EHR) data, to characterize All of Us participants including self-reported descriptors of diversity. Data depth, density, and quality were evaluated using medication sequencing analyses for depression and type 2 diabetes. Replication of known oncologic associations with smoking exposure ascertained by EHR and survey data and calculation of population-based atherosclerotic cardiovascular disease risk scores demonstrated the utility of data and platform capability.FindingsThe beta launch of the All of Us Researcher Workbench contains data on 224,143 participants. Seventy-seven percent of this cohort were identified as Underrepresented in Biomedical Research (UBR) including over forty-eight percent self-reporting non-White race. Medication usage patterns in common diseases depression and type 2 diabetes replicated prior findings previously reported in the literature and showed differences based on race. Oncologic associations with smoking were replicated and effect sizes compared for EHR and survey exposures finding general agreement. A cardiovascular disease score was calculated utilizing multiple data elements curated across sources. The cloud-based architecture built in the Researcher Workbench provided secure access and powerful computational resources at a low cost. All analyses have been made available for replication and reuse by registered researchers.Conclusions and RelevanceThe All of Us Research Program’s initial release of cohort data contains longitudinal and multidimensional data on diverse participants that replicate known associations. This dataset and the cloud-based Researcher Workbench advance the mission of All of Us to make data widely and securely available to researchers to improve human health and advance precision medicine.

UNSTRUCTURED Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

With decreasing cost of biomedical technologies, the scale of the genetic and healthcare data have exponentially increased and become available to wider audiences. Hence, privacy of patients and study participants has garnered the attention of researchers and regulators alike. Availability of genetic and health care information for uses not anticipated at the time of collection gives rise to privacy concerns such that people suffer dignitary harm when their data is used in ways they did not desire or intend, even if no concrete economic damage results. In this workshop, we explore the issues surrounding data use to advance human health from a privacy perspective. Broadly this field can be considered in two encompassing areas: (1) Ethics and regulation of privacy: The ethical and regulatory frames through which we can consider privacy, the existing regulations regarding privacy and what is on the horizon, and implementation of such ethical considerations for data with the new Common Rule. (2) Approaches to ensuring privacy using technology: The technologies that allow responsible use and sharing of data such as encryption and the quantification of privacy leakages in publicly available data through privacy attacks for better risk-assessment tools.

This chapter will address ethical issues in consumer informatics and online content, including quality assurance, privacy and security concerns, consent and governance, and the important role consumer information plays in shared decision-making. If privacy concerns have slowed the progress of research, new approaches such as a pool of freely available health and genomic data could accelerate innovation. This chapter will describe the range of ethical perspectives on data sharing and propose a new approach using free and open-source tools to support ethical transactions involving consumers, researchers, and health systems.

Sage Bionetworks (Sage) has developed a framework for qualifying solvers from around the world for accessing health and biomedical data within Synapse, Sage’s data sharing and collaboration platform. We identified three conceptual mechanisms – transactional cost, exposure, and openness – to enable the broadest researcher pool access to health and biomedical research data for discovery while still encoding basic contractual protections for research participants. Our qualified researcher framework was first piloted in 2015 with data gathered from mPower, an observational smartphone-based study of Parkinson disease symptoms. Here we describe the rationale and features of the framework and its application in the exemplar mPower study.

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research. Several remotely conducted studies have recruited thousands of participants over a span of a few months allowing researchers to collect real-world data at scale and at a fraction of the cost of traditional research. Unfortunately, remote studies have been hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of outcomes. We report the findings regarding recruitment and retention from eight remote digital health studies conducted between 2014–2019 that provided individual-level study-app usage data from more than 100,000 participants completing nearly 3.5 million remote health evaluations over cumulative participation of 850,000 days. Median participant retention across eight studies varied widely from 2–26 days (median across all studies = 5.5 days). Survival analysis revealed several factors significantly associated with increase in participant retention time, including (i) referral by a clinician to the study (increase of 40 days in median retention time); (ii) compensation for participation (increase of 22 days, 1 study); (iii) having the clinical condition of interest in the study (increase of 7 days compared with controls); and (iv) older age (increase of 4 days). Additionally, four distinct patterns of daily app usage behavior were identified by unsupervised clustering, which were also associated with participant demographics. Most studies were not able to recruit a sample that was representative of the race/ethnicity or geographical diversity of the US. Together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.

Tech giants moving into health may widen inequalities and harm research, unless people can access and share their data, warn John T. Wilbanks and Eric J. Topol.

The United States' All of Us Research Program is a longitudinal research initiative with ambitious national recruitment goals, including of populations traditionally underrepresented in biomedical research, many of whom have high geographic mobility. The program has a distributed infrastructure, with key programmatic resources spread across the US. Given its planned duration and geographic reach both in terms of recruitment and programmatic resources, a diversity of state and territory laws might apply to the program over time as well as to the determination of participants' rights. Here we present a listing and discussion of state and territory guidance and regulation of specific relevance to the program, and our approach to their incorporation within the program's informed consent processes.

Given the data-rich nature of modern biomedical research, there is a pressing need for a systematic, structured, computer-readable way to capture, communicate, and manage sharing rules that apply to biomedical resources. This is essential for responsible recording, versioning, communication, querying, and actioning of resource sharing plans. However, lack of a common “information model” for rules and conditions that govern the sharing of materials, methods, software, data, and knowledge creates a fundamental barrier. Without this, it can be virtually impossible for Research Ethics Committees (RECs), Institutional Review Boards (IRBs), Data Access Committees (DACs), biobanks, and end users to confidently track, manage, and interpret applicable legal and ethical requirements. This raises costs and burdens of data stewardship and decreases efficient and responsible access to data, biospecimens, and other resources. To address this, the GA4GH and IRDiRC organizations sponsored the creation of the Automatable Discovery and Access Matrix (ADA-M, read simply as “Adam”). ADA-M is a comprehensive information model that provides the basis for producing structured metadata “Profiles” of regulatory conditions, thereby enabling efficient application of those conditions across regulatory spheres. Widespread use of ADA-M will aid researchers in globally searching and prescreening potential data and/or biospecimen resources for compatibility with their research plans in a responsible and efficient manner, increasing likelihood of timely DAC approvals while also significantly reducing time and effort DACs, RECs, and IRBs spend evaluating resource requests and research proposals. Extensive online documentation, software support, video guides, and an Application Programming Interface (API) for ADA-M have been made available.

Electronic informed consent represents an opportunity to redesign the way that participants understand and elect to enroll in clinical research studies. However, electronic consent faces certain barriers common to all informed consent processes and other barriers specific to the technical environment. At Sage Bionetworks, we designed an electronic consent process as a software product and released it as an open source tool. We believe that using contemporary design processes to intentionally create cognitive friction, where potential study participants are confronted with interfaces that require them to slow down and contemplate study concepts, offers a significant opportunity for ethical design as research increasingly uses smartphones and digital methodologies.

The United States’ All of Us Research Program is a longitudinal research initiative with ambitious national recruitment goals, including of populations traditionally underrepresented in biomedical research, many of whom have high geographic mobility. The program has a distributed infrastructure, with key programmatic resources spread across the US. Given its planned duration and geographic reach both in terms of recruitment and programmatic resources, a diversity of state and territory laws might apply to the program over time as well as to the determination of participants’ rights. Here we present a listing and discussion of state and territory guidance and regulation of specific relevance to the program, and our approach to their incorporation within the program’s informed consent processes.

To upend current barriers to sharing clinical data and insights, we need a framework that not only accounts for choices made by trial participants but also qualifies researchers wishing to access and analyze the data.

National and international public–private partnerships, consortia, and government initiatives are underway to collect and share genomic, personal, and healthcare data on a massive scale. Ideally, these efforts will contribute to the creation of a medical information commons (MIC), a comprehensive data resource that is widely available for both research and clinical uses. Stakeholder participation is essential in clarifying goals, deepening understanding of areas of complexity, and addressing long-standing policy concerns such as privacy and security and data ownership. This article describes eight core principles proposed by a diverse group of expert stakeholders to guide the formation of a successful, sustainable MIC. These principles promote formation of an ethically sound, inclusive, participant-centric MIC and provide a framework for advancing the policy response to data-sharing opportunities and challenges.

In an Essay, Matthew Todd and colleagues discuss an open source approach to drug development.

Background: To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent) processes. As we do so, we have an ethical obligation to ensure that core tenants of informed consent—informedness, comprehension, and voluntariness—are upheld. Furthermore, we should be wary of recapitulating the pitfalls of “traditional” informed consent processes. Objective: Our objective was to describe the essential qualities of participant experience, including delineation of common and novel themes relating to informed consent, with a self-administered, smartphone-based eConsent process. We sought to identify participant responses related to informedness, comprehension, and voluntariness as well as to capture any emergent themes relating to the informed consent process in an app-mediated research study. Methods: We performed qualitative thematic analysis of participant responses to a daily general prompt collected over a 6-month period within the Parkinson mPower app. We employed a combination of a priori and emergent codes for our analysis. A priori codes focused on the core concepts of informed consent; emergent codes were derived to capture additional themes relating to self-administered consent processes. We used self-reported demographic information from the study’s baseline survey to characterize study participants and respondents. Results: During the study period, 9846 people completed the eConsent process and enrolled in the Parkinson mPower study. In total, 2758 participants submitted 7483 comments; initial categorization identified a subset of 3875 germane responses submitted by 1678 distinct participants. Respondents were more likely to self-report a Parkinson disease diagnosis (30.21% vs 11.10%), be female (28.26% vs 20.18%), be older (42.89 years vs 34.47 years), and have completed more formal education (66.23% with a 4-year college degree or more education vs 55.77%) than all the mPower participants (P

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

Sage Bionetworks has developed a novel multi-media approach to addressing transparency and comprehension within electronic informed consent (eConsent) for app-mediated research studies. We deployed this eConsent process in minimal-risk research studies. Here we describe the rationale for the framework selected and best practice for application of the framework in other clinical studies.

"One of the prerequisites for sustainable development is knowledge, in order to inform coping with sustainability threats and to support innovative sustainability pathways. Transferring knowledge is therefore a fundamental challenge for sustainability, in a context where external knowledge must be integrated with local knowledge in order to promote user-driven action. But effective local co-production of knowledge requires ongoing local access to existing scientific and technical knowledge so that users start on a level playing field. The information technology revolution can be a powerful enabler of such access if intellectual property obstacles can be overcome, with a potential to transform prospects for sustainability in many parts of the world."

First paragraph: Public online databases supporting life sciences research have become valuable resources for researchers depending on data for use in cheminformatics, bioinformatics, systems biology, translational medicine, and drug repositioning efforts, to name just a few of the potential end user groups (Williams et al. 2009). Worldwide funding agencies (governments and not-for-profits) have invested in public domain chemistry platforms. In the United States these include PubChem, ChemIDPlus, and the Environmental Protection Agency’s ACToR, while the United Kingdom has funded ChEMBL and ChemSpider, among others, and new databases continue to appear annually (National Center for Biotechnology Information, n.d.; US National Library of Medicine, n.d.; Judson et al. 2008; EMBL-EBI, n.d.; Pence & Williams 2010; Galperin & Cochrane 2011).

In February, the White House Office of Science and Technology Policy (OSTP) released a memo directing each US funding agency with over $100 million in annual research expenditure to develop a plan to support public access to the results of research funded by the federal government, including results published in scholarly journals. How has the OSTP memo impacted scholarly publishing so far? What exactly has been achieved so far, and what will it mean for the future of U.S.-published research? This interactive session features a panel of speakers who will be discussing the recent developments and emerging issues from the librarian, funder, researcher, and publisher perspectives. Among the questions to be addressed are: What is the US position on implementing open access? What steps have been taken to implement OSTP objectives? What role do institutional repositories play in open access? How will funder’s open access policies impact universities and researchers? What role are publishers playing in the implementation of open access? How will open access content be linked and measured? Will it affect usage?

Introduction What frameworks are available to permit data reuse? How can legal and technical systems be structured to allow people to donate their data to science? What are appropriate methods for repurposing traditional consent forms so that user-donated data can be gathered, de-identified, and syndicated for use in computational research environments? This chapter will examine how traditional frameworks for permitting data reuse have been left behind by the mix of advanced techniques for re-identification and cheap technologies for the creation of data about individuals. Existing systems typically depend on the idea that de-identification is robust and stable, despite significant evidence that re-identification is regularly possible on at least some portion of a de-identified cohort. The promise that privacy can always be protected, that data can always be de-identified or made anonymous, is at odds with many of the emerging realities of our world. At issue here is a real risk to scientific progress. If privacy concerns block the redistribution of data on which scientific and policy conclusions are based, then those conclusions will be difficult to justify to the public who must understand them. We must find a balance between our ability to make and produce identifiable data, the known failure rates of de-identification systems, and our need for policy and technology supported by ‘good’ data. If we cannot find this balance we risk a tragedy of the data commons in which the justifications for social, scientific, and political actions are available only to a select few.

A diverse set of stakeholders within health care came together in the fall of 2013 to brainstorm potential new commercial paths for repurposed drugs for the treatment of rare diseases. This report highlights some of the identified solutions that potentially could create the right set of incentives for the efficient and accelerated development and delivery of needed therapies for patients suffering from rare diseases.

This is an open letter concerning the recent launch of the new open access journal, eNeuro. We welcome the diversification of journal choices for authors looking for open access venues, as well as the willingness of eNeuro to accept negative results and study replications, its membership in the Neuroscience Peer Review Consortium, the publication of peer review syntheses alongside articles, and the requirement that molecular data be publicly available. As strong supporters of open access, we welcome the commitment of the Society to making the works it publishes freely and openly available. However, we are concerned with several aspects of the specific approach, and outline herein a number of suggestions that would allow eNeuro to provide the full benefits of open access to the communities the journal aims to serve...