A survey of computable biomedical knowledge repositories

Introduction

While data repositories are well-established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studied.

Methods

We conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and Technology.

Results

All 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine-readable formats, include implementation guidelines, and adhere to standards to convey Responsibility. Repositories report having Technology functions that enable end-users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability, less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision-making.

Conclusion

It is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolution.

Learning Health Care and the Obligation to Participate in Research

This commentary responds to the article "Compulsory Research in Learning Health Care: Against a Minimal Risk Limit," by Robert Steel. Steel acknowledges that our ethics framework for a learning health care system, published in the 2013 special report Ethical Oversight of Learning Health Care Systems , includes an obligation on the part of patients to participate in learning activities, but he argues that this obligation does not go far enough. Steel, who provides an interesting justification for compulsory research participation in learning health care, claims that our obligation is limited to only minimal risk research. We take exception to that characterization insofar as it detracts from what we believe is most relevant for assessing learning activities, which is how much additional risk and burden a learning activity poses compared to clinical care alone. We also clarify that the level of additional risk is not the only morally relevant consideration in determining if a learning activity should be compulsory. Also important is whether the learning activity includes interventions or choices that engage values of importance to patients.

Compulsory Research in Learning Health Care: Against a Minimal Risk Limit

Existing calls to implement learning health care systems have tended to stipulate a minimal or near-minimal risk limit for compulsory learning activities. I argue to the contrary that such a limit cannot be defended. So long as the way in which patients are compelled to participate in learning activities is solely through the withholding of nonresearch options for receiving care, compelling participation does not violate any individual's rights and can be both efficient and adequately fair. Because the decision to compel participation in this way is relevantly similar to the decision to ration care, theories of justice in cost-effectiveness rationing can be used to further specify when it is appropriate to use this method to compel research participation. When applied, these theories will not support a minimal risk limit for compulsory research.

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider

Learning healthcare systems have recently emerged as a strategy to continuously use experiences and outcomes of clinical care for research purposes in precision medicine. Although it is known that learning healthcare transitions in general raise important ethical challenges, the ethical ramifications of such transitions in the specific context of precision medicine have not extensively been discussed. Here, we describe three levers that institutions can pull to advance learning healthcare systems in precision medicine: (1) changing testing of individual variability (such as genes); (2) changing prescription of treatments on the basis of (genomic) test results; and/or (3) changing the handling of data that link variability and treatment to clinical outcomes. Subsequently, we evaluate how patients can be affected if one of these levers are pulled: (1) patients are tested for different or more factors than before the transformation, (2) patients receive different treatments than before the transformation and/or (3) patients’ data obtained through clinical care are used, or used more extensively, for research purposes. Based on an analysis of the aforementioned mechanisms and how these potentially affect patients, we analyze why learning healthcare systems in precision medicine need a different ethical approach and discuss crucial points to consider regarding this approach.

Ethics and Learning Health Care: The Essential roles of engagement, transparency, and accountability

Extraordinary innovation in medicine promises vast improvements to the health of individuals and communities. Yet it is a lost opportunity that data from most medical care is never aggregated or analyzed. Even when data are aggregated and analyzed, most of this "learning" is never translated into improved practice. The Learning Health Care System (LHCS) is a response to both of these challenges. Ethically, the LHCS relies on a foundational understanding between patients and their health systems in which patients endorse the use of their data for ongoing learning, and health systems commit to improving care based on what is learned. We have outlined elsewhere a set of seven ethical obligations for Learning Health Care, including the obligation to respect the rights and dignity of patients. In this paper, we suggest that three specific respect-promoting actions are morally required in a LHCS: engagement with patients about ongoing learning activities, transparency with patients about ongoing learning activities, and accountability in implementing what is learned.