Toward a science of learning systems: a research agenda for the high-functioning Learning Health System
By C. Friedman, J. Rubin, Jeffrey S. Brown, Melinda Buntin, M. Corn, L. Etheredge, Carl A. Gunter, Mark A. Musen, R. Platt, W. Stead and 2 more. J. Am. Medical Informatics Assoc., 2014
415 citations recorded by the source corpus.
Objective The capability to share data, and harness its potential to generate knowledge rapidly and inform decisions, can have transformative effects that improve health. The infrastructure to achieve this goal at scale—marrying technology, process, and policy—is commonly referred to as the Learning Health System (LHS). Achieving an LHS raises numerous scientific challenges. Materials and methods The National Science Foundation convened an invitational workshop to identify the fundamental scientific and engineering research challenges to achieving a national-scale LHS. The workshop was planned by a 12-member committee and ultimately engaged 45 prominent researchers spanning multiple disciplines over 2 days in Washington, DC on 11–12 April 2013. Results The workshop participants collectively identified 106 research questions organized around four system-level requirements that a high-functioning LHS must satisfy. The workshop participants also identified a new cross-disciplinary integrative science of cyber-social ecosystems that will be required to address these challenges. Conclusions The intellectual merit and potential broad impacts of the innovations that will be driven by investments in an LHS are of great potential significance. The specific research questions that emerged from the workshop, alongside the potential for diverse communities to assemble to address them through a ‘new science of learning systems’, create an important agenda for informatics and related disciplines.
Subfields: Computer Science Applications, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/85e87c1b96fbe70de48625cf50d62236f3c4a06a
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In Management Science and Operations Research
Citations in the corpus
- Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Without the needs of data movement, the speed of information flow can approach real time and make a rapid learning healthcare system possible (Slut-sky, 2007; Friedman et al., 2014; Abernethy et al., 2010).”
- Electronic Health Records: Then, Now, and in the Future (2016, neutral): “The infrastructure to achieve this goal at scale --marrying technology, process, and policy--is commonly referred to as the Learning Health System [212].”