My infatuation with Learning Health Systems (LHS) began in 2009, soon after the concept
was first advanced by the organization then known as the Institute of Medicine, but
now known as the National Academy of Medicine. The definition of an LHS offered in
their 2012 report has been an inspirational guidepost for many:
A system in which science, informatics, incentives, and culture are aligned for continuous
improvement and innovation, with best practices seamlessly embedded in the care process,
patients and families as active participants in all elements, and new knowledge is
captured as an integral by‐product of the care experience.
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In the ensuing years, as the concept of learning health systems matured and a substantial
literature emerged, many definitions and elaborations have appeared.
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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.
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The cycle begins at “5 o’clock” with the establishment of a multi‐stakeholder 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.
FIGURE 1
Learning cycle marrying discovery to implementation
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.
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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.
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CONTINUITY OF COLLABORATIVE EXECUTION
Members of a learning community are driven by a shared passion to improve against
the health problem that brought them together. With reference to the left side of
Figure 2, after a learning community forms into a cohesive group, that same group
directs the cycle through the sequence of discovery (P2D and D2K), implementation
(K2P), and back to discovery again as the cycle continues. Individuals with specialized
expertise can enhance the execution of each phase of the cycle, augmenting the work
of the persistent core membership of the community.
FIGURE 2
Contrasting the continuous cycle under the aegis of a persistent community with an
interrupted cycle engaging different organizational entities
Continuity avoids the challenges of the “interrupted cycle” illustrated on the right
side of Figure 2. Interrupted cycles occur when entirely different, specialized professional
groups within an organization execute each component of the cycle. As shown in the
figure, a group specializing in P2D might be focused exclusively on program evaluation
or quality assessment; a group specializing in D2K might be focused on health services
research; a group specializing the K2P might be exclusively focused on change implementation.
The members of each of these relatively homogeneous groups will have similar educational
backgrounds and professional cultures, but the backgrounds and cultures of each group
may differ greatly from each other.
Interrupted cycles necessitate challenging handoffs between these different groups.
Less than perfect communication between the groups can, for example, lead to different
interpretations of the data at the handoff between P2D and D2K, and similarly, to
different interpretations of the results of analyses as the handoff between D2K and
K2P. Oftentimes, these elements report up to different senior leaders who may have
different priorities. A cycle can stall if the group receiving the handoff believes
the health problem has lower priority than the group initiating it. Moreover, the
execution of an interrupted cycle may largely engage individuals who are domain agnostic
methodologists lacking deep knowledge of and commitment to the health problem of interest.
As such, they may lack the passion necessary to attack and solve a challenging health
problem.
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EMBRACED UNCERTAINTY
Members of the multistakeholder learning community that drives a learning cycle begin
their work from a position of uncertainty, acknowledging that the intervention(s)
to achieve improvement later in the cycle are unknown to them at the outset. In the
culture of learning systems, uncertainty is a virtue; premature certainty is a threat
to success. The “performance to data” (P2D) and “data to knowledge” (D2K) components
of the learning cycle comprise a process of exploration, not confirmation. Inevitably,
members of the community will bring their own conjectures or hunches regarding the
pathway to improvement. These hunches and conjectures must be held in abeyance until
they are supported, or otherwise, by the data the community collects and analyzes.
Premature closure on an intervention strategy, especially if driven by community members
holding positions of authority or seniority, represents a threat to the success of
a learning cycle.
This differentiation becomes clearer when one considers where the learning cycle starts.
With reference to Figure 1, the initial iteration begins with formation of the learning
community at “5 o’clock” on the cycle, so that the initial activities undertaken by
a community are the discovery processes of P2D and D2K. It is also possible—but contrary
to the fundamental premise of a Learning Health System—to start the cycle at “12 o’clock”
with the implementation of an intervention purchased from a vendor, an approach derived
solely from the published literature with no local evidence, or one mandated by senior
leadership of an organization. Bypassing, in these ways, the systematic empirical
discovery aspects of the cycle (P2D and D2K) runs the risk of implementing an intervention
that is not fitted to the problem as it exists in local context, or failing to discover
a different strategy that might turn out to be superior.
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INFRASTRUCTURE PROVIDING SHARED SERVICES
A learning system minimally requires two components. The first is a collection of
simultaneously operating learning cycles of the type illustrated in Figure 1, with
each cycle addressing a unique identified health problem and operating under the aegis
of its own learning community. The second is an infrastructure that can be viewed
as an integrated set of services supporting all of the simultaneous cycles, as shown
in Figure 3.
Figure 3 illustrates the multiple cycles supported by an annular platform. Arrayed
along the platform are eight services that correspond to the stage of the cycle that
each service primarily supports.
FIGURE 3
Services provided by an infrastructure supporting multiple co‐occurring cycles
The infrastructure, if appropriately conceived and constructed, can support multiple
co‐occurring cycles because the cycles have similar structure. They share the sequence
of first establishing a learning community and then proceeding through the stages
of P2D, D2K, K2P, and back to P2D to begin the second iteration of the cycle. For
example, methods to stand up and support learning communities,
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grounded in a literature on collaboration
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are applicable to any health problem a cycle might be addressing. The services depicted
in Figure 3 imply that the infrastructure supporting an LHS is socio‐technical. These
supporting services consist of policies, processes and people, in addition to technologies.
Lacking an infrastructure, a collection of cycles is just that. With an infrastructure,
a collection of cycles becomes a learning system. The benefits of shared infrastructure
are myriad. Infrastructure enables economies of scale. If each learning cycle is independent
of the others and employs its own technologies, policies, processes and people, the
cost of implementing N cycles will be nearly equal to (N x the cost of one cycle).
An infrastructure providing shareable services, as depicted in Figure 3, will make
the cost of N cycles much less than (N x the cost of one cycle). Infrastructure also
enables economies of scope. A well‐designed infrastructure will provide services that
are largely invariant with respect to the health problem being addressed by each cycle.
Support of additional cycles will require minimal modification to the infrastructure.
Finally, infrastructure enables LHSs to “scale up.” For example, a group of organizations
with similarly architected infrastructures can, with relatively little effort, form
a learning network that functions at a higher level of scale.
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CONCLUSION
This brief essay has attempted to identify features that, taken together, characterize
and distinguish Learning Health Systems. In the interest of clarity, I have framed
as “all or nothing” a number of concepts that are, in reality, more graduated and
nuanced. For example, infrastructural services cannot be completely agnostic to the
health problems addressed by the learning cycles they are supporting. Moreover, in
asserting that Learning Health Systems exhibit all three of these characteristics,
I am not asserting that other approaches to health improvement exhibit none of them.
Indeed, the work of quality departments at many health care delivery organizations
may qualify them as Learning Health Systems by the criteria offered here. In the end,
what an entity does and achieves is far more important than what it is called.