RT Journal Article SR Electronic T1 Using a network organisational architecture to support the development of Learning Healthcare Systems JF BMJ Quality & Safety JO BMJ Qual Saf FD BMJ Publishing Group Ltd SP bmjqs-2017-007219 DO 10.1136/bmjqs-2017-007219 A1 Maria T Britto A1 Sandra C Fuller A1 Heather C Kaplan A1 Uma Kotagal A1 Carole Lannon A1 Peter A Margolis A1 Stephen E Muething A1 Pamela J Schoettker A1 Michael Seid YR 2018 UL http://qualitysafety.bmj.com/content/early/2018/02/04/bmjqs-2017-007219.abstract AB The US National Academy of Sciences has called for the development of a Learning Healthcare System in which patients and clinicians work together to choose care, based on best evidence, and to drive discovery as a natural outgrowth of every clinical encounter to ensure innovation, quality and value at the point of care. However, the vision of a Learning Healthcare System has remained largely aspirational. Over the last 13 years, researchers, clinicians and families, with support from our paediatric medical centre, have designed, developed and implemented a network organisational model to achieve the Learning Healthcare System vision. The network framework aligns participants around a common goal of improving health outcomes, transparency of outcome measures and a flexible and adaptive collaborative learning system. Team collaboration is promoted by using standardised processes, protocols and policies, including communication policies, data sharing, privacy protection and regulatory compliance. Learning methods include collaborative quality improvement using a modified Breakthrough Series approach and statistical process control methods. Participants observe their own results and learn from the experience of others. A common repository (a ‘commons’) is used to share resources that are created by participants. Standardised technology approaches reduce the burden of data entry, facilitate care and result in data useful for research and learning. We describe how this organisational framework has been replicated in four conditions, resulting in substantial improvements in outcomes, at scale across a variety of conditions.