Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

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. KEYWORDS diagnostic error, learning community, learning health systems, machine learning, medical diagnosis, multidisciplinary

The three numbers you need to know about healthcare: the 60-30-10 Challenge

“…Examples of such learning systems, or those attempting to emulate the characteristics of a learning health system, are emerging at a rapid pace. Cases in point are studies redesigning the care of lung cancer patients (The Ottawa Health Transformation Model) [64], the changing roles of researchers in different US settings using learning health system principles to reduce diagnostic errors and near misses [65], UK policy initiatives to build the infrastructure and data backbone on which the progress of learning health [56] and Braithwaite et al [57]). Used with permission from Oxford University Press systems will be based [66], and data collaborations to reduce mortality associated with sepsis amongst 21 hospitals of the Kaiser Permanente North California system [67] following earlier Kaiser Permanente examples, such as the work on Vioxx, and the early detection of its long-term side effects [68].…”

The Science of Learning Health Systems: Scoping Review of Empirical Research

“…Five (6.6%) of 76 studies examined stakeholders’ perspectives on particular components of an LHS, including quality improvement [ 70 ], electronic prescribing and medicines administration systems [ 71 ] and diagnostic practices [ 72 ]. The studies were all qualitative and used either interviews [ 72 - 74 ] or focus groups [ 70 , 71 ].…”

“…Five (6.6%) of 76 studies examined stakeholders’ perspectives on particular components of an LHS, including quality improvement [ 70 ], electronic prescribing and medicines administration systems [ 71 ] and diagnostic practices [ 72 ]. The studies were all qualitative and used either interviews [ 72 - 74 ] or focus groups [ 70 , 71 ]. The participants in these studies were junior doctors [ 70 ], health system leaders [ 73 ], researchers [ 72 ], and other diverse health care system stakeholders [ 71 , 74 ].…”

“…The studies were all qualitative and used either interviews [ 72 - 74 ] or focus groups [ 70 , 71 ]. The participants in these studies were junior doctors [ 70 ], health system leaders [ 73 ], researchers [ 72 ], and other diverse health care system stakeholders [ 71 , 74 ]. Psek et al [ 73 ], for example, interviewed 41 senior leaders across an integrated health delivery system, identifying 10 themes related to operationalizing an LHS, such as “balancing learning and workflow” and “integrating cultural and operational silos.” Although not strictly implementation evaluation studies, all 5 studies under this category identified barriers and facilitators relevant to the realization of an LHS, including the usability of systems [ 71 ] and time constraints, such as time for participation in quality improvement activities [ 70 ].…”

Rethinking autism spectrum disorder assessment for children during COVID‐19 and beyond

“…Under these circumstances, quality improvement (QI) approaches offer some advantages over conventional research frameworks. For example, Learning Health Systems integrate quality improvement science with clinical practice as a collaborative method for addressing complex clinical challenges, including diagnostic practices (Satterfield et al, 2020 ). An essential element is the active partnering with patients and families in all aspects of work.…”