Organizing for collaboration: An actor-oriented architecture in ImproveCareNow.

BACKGROUND: Collaborative learning health systems (CLHSs) enable patients, clinicians, researchers, and others to collaborate at scale to improve outcomes and generate new knowledge. An organizational framework to facilitate this collaboration is the actor-oriented architecture, composed of (a) actors (people, organizations, and databases) with the values and abilities to self-organize; (b) a commons where they create and share resources; and (c) structures, protocols, and processes that facilitate multiactor collaboration. CLHSs may implement a variety of changes to strengthen the actor-oriented architecture and enable more actors to create and share resources. OBJECTIVE: To describe and measure implementation of elements of the actor-oriented architecture in an existing Collaborative Learning Health System. METHODS: We used the case of ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease, founded in 2006. We traced several network-level indicators of actor-oriented architecture between 2010 and 2016. RESULTS: We identified measures of actors, the commons, and ways that have made it easier for network member sites to participate. These indicators show ImproveCareNow has made changes in the three elements of the actor-oriented architecture over time. CONCLUSION: It is possible to measure the implementation of an actor-oriented architecture in a CLHS. The elements of the actor-oriented architecture may provide a conceptual framework for their development and optimization. Metrics such as those described here may be actionable indicators of the "health of the system."

Co-Designing a Collaborative Chronic Care Network (C3N) for Inflammatory Bowel Disease: Development of Methods.

BACKGROUND: Our health care system fails to deliver necessary results, and incremental system improvements will not deliver needed change. Learning health systems (LHSs) are seen as a means to accelerate outcomes, improve care delivery, and further clinical research; yet, few such systems exist. We describe the process of codesigning, with all relevant stakeholders, an approach for creating a collaborative chronic care network (C3N), a peer-produced networked LHS. OBJECTIVE: The objective of this study was to report the methods used, with a diverse group of stakeholders, to translate the idea of a C3N to a set of actionable next steps. METHODS: The setting was ImproveCareNow, an improvement network for pediatric inflammatory bowel disease. In collaboration with patients and families, clinicians, researchers, social scientists, technologists, and designers, C3N leaders used a modified idealized design process to develop a design for a C3N. RESULTS: Over 100 people participated in the design process that resulted in (1) an overall concept design for the ImproveCareNow C3N, (2) a logic model for bringing about this system, and (3) 13 potential innovations likely to increase awareness and agency, make it easier to collect and share information, and to enhance collaboration that could be tested collectively to bring about the C3N. CONCLUSIONS: We demonstrate methods that resulted in a design that has the potential to transform the chronic care system into an LHS.

What's in a name generator? Choosing the right name generators for social network surveys in healthcare quality and safety research.

BACKGROUND: Interest in the use of social network analysis (SNA) in healthcare research has increased, but there has been little methodological research on how to choose the name generators that are often used to collect primary data on the social connection between individuals for SNA. OBJECTIVE: We sought to determine a minimum set of name generators sufficient to distinguish the social networks of a target population of physicians active in quality improvement (QI). METHODS: We conducted a pilot survey including 8 name generators in a convenience sample of 25 physicians active in QI to characterize their social networks. We used multidimensional scaling to determine what subset of these name generators was needed to distinguish these social networks. RESULTS: We found that some physicians maintain a social network organized around a specific colleague who performed multiple roles while others maintained highly differentiated networks. We found that a set of 5 of the 8 name generators we used was needed to distinguish the networks of these physicians. CONCLUSIONS: Beyond methodology for selecting name generators, our findings suggest that QI networks may require 5 or more generators to elicit valid sets of relevant actors and relations in this target population.