What is unique about learning health systems?

In the ensuing years, as the concept of learning health systems matured and a substantial literature emerged, many definitions and elaborations have appeared. This has inevitably led to uncertainty regarding what lies at the core of the LHS and also to a blurring of its boundaries: how the LHS differs from other approaches to health and health care improvement. Not surprisingly, I have been frequently asked: What’s new here? Isn’t this the way to improve anything? How else would you do it? This short essay offers some answers, grounded in a conception of what lies at the core of the LHS, that I would now give to these questions. The differentiation begins with the concept of a learning or improvement cycle and the assertion that Learning Health Systems improve individual and population health by marrying discovery to implementation through cycles of the type illustrated in Figure 1. The cycle begins at “5 o’clock” with the establishment of a multistakeholder learning community. Guided by the community, the cycle proceeds with collection of data to capture what is happening now (performance to data), analysis of these data to generate evidence of how improvement might be effected (data to knowledge), integration of locally generated evidence with relevant evidence gathered by others, and intervention based on that evidence to engender improvement (knowledge to performance). From there, the cycle repeats in a series of iterations over which data-driven improvement can occur. There is nothing fundamentally new about cyclic improvement processes. This concept traces back to the seminal work of Deming and the subsequent widespread use of PDSA cycles in health care improvement. The differentiators of the LHS—three in number, in my estimation—rest on how these cycles are executed and how a set of cycles composes into a scalable system. These differentiators also assume that the health problem addressed by the improvement cycle is a complex, even “wicked,” problem that will prove resistant to unidimensional straightforward solutions. The three differentiators are: (1) at the beginning of the cycle, establishing a multistakeholder learning community that is focused on the problem and collaboratively executes the entire cycle; (2) embracing, at the outset, the uncertainty of how to improve against the problem by undertaking a rigorous discovery process before any implementation takes place; and (3) supporting multiple co-occurring cycles with a socio-technical infrastructure to create a learning system.