Optimizing Growth and Development of Collaborative Learning Health Systems
Published November 2019 | Learning Health Systems
Solving the puzzle of improving health outcomes requires participation by everyone that matters—from patients, clinicians, and researchers to health systems and other community organizations. Collaborative learning health systems (CLHSs) bring people and organizations together, facilitating collaboration and generating new knowledge. But what is the best way to develop and optimize these systems?
According to a study from researchers in the Division of Pulmonary Medicine and the James M. Anderson Center for Health Systems Excellence, it is by using an actor-oriented organizational architecture. This organizational framework is composed of three elements—actors (people, organizations, and databases); a commons where they create and share resources; and structures, protocols, and processes that facilitate collaboration.
Researchers led by Michael Seid, PhD, David Hartley, PhD, MPH, and Peter Margolis, MD, PhD, aimed to describe and measure implementation of these elements in ImproveCareNow, an existing CLHS. The team traced changes in actor-oriented organizational architecture between 2010 and 2016, identifying measures of actors, the commons, and methods that have streamlined member participation. Resulting data showed how ImproveCareNow made changes in the three elements over time—the first quantitative description of an actor-oriented organizational scheme in a CLHS. The findings demonstrate that this small set of factors can provide a conceptual framework for development and optimization.
Since publication, the research team has developed a computational model that can be used to simulate the effects of different interventions.
“Next, we are embarking on a program of research designed to increase the effectiveness, efficiency, and scale of CLHSs,” Seid says. “Learning networks hold great promise for transforming the health system and improving outcomes. With 7,000 rare diseases, we need to scale this model and increase its effectiveness.”
