Articulating this framework and potential mechanisms of action should facilitate research to test and refine hypotheses as well as guide practice to develop and optimize this promising approach to improving healthcare systems.A learning health system aims to create value in health systems using data-driven innovations, quality improvement techniques, and collaborations between health system partners. Although the concept is mobilized through cycles of learning, most instantiations of the learning health system overlook the importance of formalized learning in educational settings. Social accountability in health professional education focuses on measurably improving people's health and health care, specifically through education and training activities. In this commentary, we argue that the idea of social accountability clearly articulates a rationale and a broad range of aspirations, whereas the learning health system offers an approach to achieve these goals. Lonidamine manufacturer With a similar aim to a learning health system, social accountability promotes partnerships between health professional education, the health system, and communities in a way that allows for co-designed and contextualized interventions. On the other hand, learning health systems prioritize data, research, and analytic capacities to facilitate quality improvement. An integrative framework could enhance learning cycles by collectively designing interventions and innovations with people and communities from health, research, and education systems. As well as aspiring to improve population health and health equity, such a framework will consider broader impacts, including the degree of participation amongst a range of partners and the level of responsiveness to partners' priorities. Quality improvement and implementation science practitioners identify relational issues as important obstacles to success. Relational interventions may be important for successful performance improvement and fostering Learning Health Systems. This case report describes the experience and lessons learned from implementing a relational approach to organizational change, informed by Relational Coordination Theory, in a health system. Structured interviews were used to obtain qualitative participant feedback. Relational Coordination was measured serially using a validated seven-item survey. An initial, relational intervention on one unit promoted increased participant engagement, self-efficacy, and motivation that led to the spontaneous, emergent dissemination of relational change, and learning into other parts of the health system. Staff involved in the intervention reported increased systems thinking, enhanced focus on communication and relationships as key drivers for improvement and learning, and greater awareness of organizational change as something co-created by staff and executives. This experience supports the hypothesis that relational interventions are important for fostering the development of Learning Health Systems.This experience supports the hypothesis that relational interventions are important for fostering the development of Learning Health Systems. Collaborative Learning Health Systems (CLHS) improve outcomes in part by facilitating collaboration among all stakeholders. One way to facilitate collaboration is by creating conditions for the production and sharing of medical and non-medical resources (information, knowledge, and knowhow [IKK]) so anybody can get "what is needed, when it's needed" (WINWIN) to act in ways that improve health and healthcare. Matching resources to needs can facilitate accurate diagnosis, appropriate prescribing, answered questions, provision of emotional and social support, and uptake of innovations. We describe efforts in ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease (IBD), to increase the number of patients and families creating and accessing IKK, and the challenges faced in that process. We applied tactics such as outreach through trusted messengers, community organizing, and digital outreach such as sharing resources on our website, via social media, and email to increase the number of people producing, able to access, and accessing IKK. We applied an existing measurement system to track our progress and supplemented this with community feedback. In August of 2017 we identified and began measuring specific actions to track community growth. The number of patients and families producing IKK has increased by a factor of 2.7, using resources has increased by a factor of 4.1 and aware of resources as increased by a factor of 4.0. We identified challenges to measurement and scaling. It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.Building Learning Health Systems requires the combination of information, regulatory, and cultural infrastructures that create communities focused on changing health outcomes through the application of quality improvement methodology, focused data collection, closed feedback loops, and community-participatory techniques. Accomplishing the vision of the Learning Health System relies on building robust infrastructures, and teaching a wide variety of stakeholders to participate in these novel socio-technical systems. In this commentary, I draw on empirical examples from fieldwork with Learning Networks to describe how social scientists view culture and what this concept might hold for learning health sciences. Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity. CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes.