Open AccessUnderstanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence
Author(s) -
Satterfield Katherine,
Rubin Joshua C.,
Yang Daniel,
Friedman Charles P.
Publication year - 2020
Publication title -
learning health systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.501
H-Index - 9
ISSN - 2379-6146
DOI - 10.1002/lrh2.10204
Subject(s) - excellence , medical diagnosis , scope (computer science) , multidisciplinary approach , knowledge management , scale (ratio) , psychology , health care , artificial intelligence , data science , computer science , medical education , medicine , sociology , political science , pathology , social science , physics , quantum mechanics , law , programming language
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.