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The rationale for developing a Learning Health System often includes some or all of the following, which were explored in more detail in the earlier Learning Healthcare Project report [1]:

To improve patient outcomes and experience:

While most health systems seek to improve the quality and safety of care, many fail even to measure comprehensive and robust outcomes. A Learning Health System with the ability to deliver actionable knowledge to the point of care could enable improvements to patient outcomes and experience.

To provide better value healthcare:

Patient outcome and patient-level costing data can enable Value Based Healthcare Delivery [12]. This may reduce the cost for a given outcome – in other words, doing things right. Perhaps more fundamentally, a Learning Health System can inform priorities for resource allocation – doing the right things.

To reduce unjustified variation:

A Learning Health System can identify variations in outcomes and in the availability of health interventions by geography or by subpopulation. It can highlight health inequalities or positive deviants, and apply behaviour change methods to address such variations [13].

To generate generalisable knowledge:

A Learning Health System can empower research. It can help identify potential participants for traditional randomised controlled trials. It can enable low-cost monitoring or long-term follow-up of participants, by tracking when they interact with the health system. It can host prospective pragmatic trials or retrospective observational studies. It can generate evidence that is relevant to small sub-populations or those with comorbidities or polypharmacy [14], and can deliver evidence for policymaking at the population-level.

To optimise the use of knowledge and evidence for decision making:

A Learning Health System can improve the use of research evidence, staff knowhow, learning from experience and organisational memory. It can close the loop by delivering knowledge back to the front-line, in a form that is likely to be acted upon. It can also monitor the impact of that action.

To identify and track epidemiological phenomena in near real-time:

The Covid-19 pandemic has highlighted the importance of real-time health surveillance systems. These can be developed on top of the infrastructure required to support other Learning Health System functions [15].

To maximise the benefits of technological innovation and investment:

Many health systems have invested vast resources into Electronic Health Records (EHR) and other Health IT infrastructure. Without investment in something like a Learning Health System, EHRs often represent little more than a clunky version of paper notes. A Learning Health System can exploit the newly available data and increase the value of existing Health IT infrastructure.

To expand the education, training and performance of clinicians:

A Learning Health System can enable performance feedback and personalised professional development using routinely collected clinical data [16].

Marketing (Because it sounds good):

In some cases, the Learning Health System concept has been used as a marketing label, applied to existing systems. This is due to the vagueness of the definitions above. It risks diluting and discrediting the concept, but it can have benefits. If it provides a set of organising principles for existing activities, it can point the way to more strategic future developments.

Stakeholders should review their current systems before establishing a Learning Health System, identifying how it may address any existing issues. This could take the form of a simple SWOT (Strengths, Weaknesses, Opportunities, Threats) [17] or PESTLE (Political, Economic, Sociological, Technological, Legal, Environmental) [18] analysis, or it could use the more comprehensive NASSS Framework (outlined later). This will provide the basis for a strategy to deliver the right Learning Health System.