Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence. Issue 1 (2nd December 2019)

• Record Type: Journal Article
• Authors: Satterfield, Katherine; Rubin, Joshua C.; Yang, Daniel; Friedman, Charles P.
• Is Part Of: Learning health systems. Volume 4:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-02
• Subjects: diagnostic error -- learning community -- learning health systems -- machine learning -- medical diagnosis -- multidisciplinary; Medical care -- Research -- Periodicals; Medical informatics -- Periodicals; Health planning -- Periodicals; 362.1068
• DOI: 10.1002/lrh2.10204
• Languages: English
• ISSNs: 2379-6146

Abstract

Abstract: 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.