A Learning Health System is an integrated health system in which progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care (Charles Friedman, 2015). Learning health systems are those that use every patient encounter as an opportunity to learn and improve. Learning health systems embrace research, evaluation, quality improvement and change at every level of aggregation, from wards to institutions, regions, provinces and beyond. Learning Health Systems require people, processes, technology and governance to achieve their objectives. These structures support ongoing learning at scale in three broad areas:
- Data to Knowledge: Qualitative and quantitative data are used to understand where the system is working well and where there are gaps. This can range from fundamental research (e.g. development of a new device or intervention) to hot-spot identification of geographic areas with high costs, to engagement with patients and family members on where the system is and is not meeting needs. Learning systems cannot function without routine, organized and available data.
- Knowledge to Practice: The use of data in its many forms produces knowledge that then needs to be put into practice to make change. These changes, again, can range from new remuneration policies to support primary care, to changes in list management to decrease waiting times for services, to new care delivery models or human resource deployment. These changes might take place in smaller or larger areas depending on the strength of evidence and extent of the gap or care issue being addressed. Learning systems acknowledge that there is little use of knowledge if it is not translated to action.
- Practice to Data: Changes in policy and practice need to be monitored to understand whether what was implemented had its desired effect. The ability to evaluate, or to peruse quality improvement in the process of change, depends on systems that organize and support ongoing data capture, and the feedback of those most involved. Transparency is important as are collective and agreed measures of success. Learning systems understand that not all changes will be successful, and that continual monitoring and adaptation are critical.