Abstract: We have long envisioned integrated actionable knowledge in electronic health records (EHR). For those working at the cutting edge of biomedical informatics, this vision is still a major motivation. Yet there are common complaints of technology-induced provider burnout, of meaningless data, of inappropriate alarms, and of perfunctory discharge notes – all resulting in a failure to deliver upon the expected value proposition. The revitalized field of artificial intelligence (AI), especially in the form of adaptive machine learning, is promising anew to revolutionize medicine. Yet AI itself gives rise to ethical concerns: is it acceptable if the AI does not provide clear reasons for its decisions? Is it acceptable if its reasoning process is obscured by the “black box” in which it lies hidden?
The Learning Health System (LHS) movement has conceptualized the development of evidence through science and practice as an integral part of the delivery of healthcare and support for good health and wellbeing. Implicit in this concept is the idea that the knowledge developed, validated, curated and distributed throughout the LHS is not only actionable, but ideally is executable.
How do the issues of trust and policy impact the LHS movement and its diverse initiatives, including Mobilizing Computable Biomedical Knowledge (MCBK)? We wish to explore the fundamental characteristics of a knowledge commons that would warrant the trust of the many communities it would engage and serve: engineers who would develop the systems, informaticians who develop knowledge artifacts, providers and others who would use them, and patients—and their caregivers and advocates—who would be impacted. How will trust be established, how perceived, how maintained? Several frameworks for conceptualizing knowledge commons, establishing and maintaining trust, and incorporating executable knowledge will be explored and discussed.
Describe the new knowledge and additional skills the participant will gain after attending your presentation.: Having participated in this interactive panel, participants will be able to:
<!--[if !supportLists]-->§ <!--[endif]-->Define “knowledge commons” and provide examples from their own environment or experience.
<!--[if !supportLists]-->§ <!--[endif]-->Describe three fundamental characteristics of the Governing Knowledge Commons Framework.
<!--[if !supportLists]-->§ <!--[endif]-->Identify the four layers of knowledge abstraction in the Boxwala framework.
<!--[if !supportLists]-->§ <!--[endif]-->Provide examples of private and public knowledge commons and critically discuss them according to (a) exclusion and subtractability, and (b) the four layer framework of Boxwala et al.
<!--[if !supportLists]-->§ <!--[endif]-->Identify four essential characteristics to achieve trustworthiness in knowledge commons and use this framework to evaluate the trustworthiness of a source of shareable knowledge.
<!--[if !supportLists]-->§ <!--[endif]-->Contribute to continuing discussions of policy and trust issues for knowledge commons.
Anthony Solomonides (Presenter)
NorthShore University HealthSystem
Apurva Desai (Presenter)
Blackford Middleton (Presenter)
Jodyn Platt (Presenter)
University of Michigan
Joshua Richardson (Presenter)
Philip D Walker (Presenter)