Data to knowledge to improvement: creating the learning health system

Learning communities

The foundation of a learning health system, as exemplified by the MQ Health and ImproveCareNow cases, is a learning community. A learning community comprises a group of collaborators with appropriate expertise to define and tackle a health system problem, at whatever level. Learning communities should co-design their terms of reference to suit the specific purpose, and membership commonly includes administrators, researchers, data experts, clinicians, policy makers, and patients who have knowledge of the problem. Members are invested in exploring and solving a given healthcare problem and are empowered to set the direction, agree on priorities, reflect on performance, discuss failure, and drive action.14 They engage in learning cycles in which they collect and analyse data to generate knowledge that will inform changes to improve care.15 Learning systems can vary in scope, and the problems addressed can range from local care delivery challenges and individual patient engagement to disease surveillance or creating new standards of patient care through multi-institutional collaborative networks.

Learning communities must have the appropriate expertise and skills to engage in learning cycles. For example, data analysts can be available to support data collection and analysis, targeted to specific problems.7 Alternatively, as in the case of MQ Health, an embedded research capability with training in data collection, analysis, and implementation can be valuable to a learning community as it can help identify specific data required to investigate problems, develop research methods for studying the problems, and define outcome and process measures to indicate improvement and change. Healthcare professionals with specific knowledge of a health or practice challenge are essential to reflect on current practice and evaluate opportunities for behaviour change.7 Patients and carers enable learning systems by allowing their data to be used for the greater good, beyond their direct care.16 Ideally, they also contribute to learning cycles as members of learning communities. For example, MQ Health invited patients to participate in focus groups and to sit within governance structures.10 The role of patients in learning communities and the methods available to enable their involvement continue to evolve. There is no one size fits all. To participate fully, all members of the learning community must be provided with appropriate information and training.7 Clinical educators are essential in preparing the future workforce with the knowledge and skills to engage in these roles.

Technical building blocks

Learning health systems depend on learning cycles in which data are captured from practice, turned into knowledge, and then put back into practice.15 Every care event or episode is digitally captured through routine data collection in electronic health records. Technology platforms such as patient registries or data warehouses generated by healthcare organisations, professional bodies, or research institutions (such as the registry and learning exchange platform of ImproveCareNow) are required for data collection, analysis, and sharing to support continuous improvement and learning.111517 Routinely recorded data from these systems can be linked with other data sources to inform learning cycles. For example, ImproveCareNow uses structured clinical encounter forms to standardise data collection across all patients in the registry.11

Data must be stored, accessed, and protected in a way that promotes trust. To achieve this, databases must be fit for purpose, be interoperable with other systems, and show provenance, transparency, adherence to the rule of law, and public interest.181920212223 Improvements are only achieved when practice related data become useful knowledge, when that knowledge is applied in practice, and when resulting outcomes are captured as new data.24

Different study designs and artificial intelligence approaches have emerged that enable advanced quantitative analysis to transform data into knowledge.25262728293031323334 More traditional quantitative, qualitative, or mixed-methods research can also be used within learning cycles.3536 For example, qualitative data can provide insight into the “why” and “how,” such as MQ Health’s use of focus groups to get patient feedback on improvements to an app designed to increase patient engagement with health records.810

Depending on the scale of the system and defined problem, knowledge can be used to inform practice through traditional means such as published papers, evidence based guidelines, or updated training curriculums.37 New technological capabilities can also enable rapid dissemination through clinical decision support systems, often embedded within electronic health record systems.24 Knowledge can also influence strategy and policy development and system level incentives such as pay-for-performance models. Existing institutional channels, such as librarians and knowledge management specialists, can coordinate and improve knowledge dissemination.38

Strategy, structure, and process

In a learning health system, the strategic direction, purpose, goals, priorities, processes, and tools are co-designed by the learning community, which then directs the learning cycles.39 Achieving and sustaining learning systems also requires organisational structures and governance capable of delivering the strategy.40 For example, the ImproveCareNow community determined its purpose as transforming “the health, care and costs for all children and adolescents with Crohn’s disease and ulcerative colitis.”13 Programme participants then encouraged collaboration between clinicians, researchers, patients, and parents to achieve that purpose, including creating and implementing the data collection tool at the heart of their learning activities. In learning systems, the organisational arrangements are designed to support engagement and collaboration among all stakeholders. Policies and processes are aligned to the strategic direction, provide guidance on how to participate in improvement and learning cycles, and identify measures of success.

Culture

A learning health system requires a culture that values curiosity, continuous learning, improvement, and innovation.41 In the ImproveCareNow initiative, a shared value of learning from every patient within the network drove the development and implementation of a standardised data collection tool to facilitate data sharing and analysis. A shared value related to patient engagement drove MQ Health’s piloting of an app to increase engagement and the use of focus groups with patients to identify lessons.11

Achieving this type of culture requires leadership to motivate staff to engage in learning cycles and to embrace the potential for behaviour and process change; this, in turn, is anchored in shared commitment to tackling the identified problem. Leadership can also ensure that the workforce has requisite knowledge and skills and that patients and families are included as important participants in building culture. However, leadership alone cannot achieve a learning health system; there is also a grassroots component collaboratively driven by individuals across the system with a desire for better knowledge and decisions in support of continuously improving care. Within such cultures, transparency and effective communication can build trust, respect, and affective commitment. Recognition systems, outcome measures, policies, and procedures become visible indications of how learning is valued.42

Behaviour change

Effective learning health systems adopt an evidence based approach to changing the behaviour of individuals and organisations towards data sharing, continuous learning, improvement, and innovation. This approach requires an understanding of the drivers of existing behaviour, as well as what has impeded these drivers previously.43 Systematic and evidence based approaches to behaviour change are necessary to ensure that knowledge makes it into practice.44 For example, the behaviour change wheel framework can be helpful for designing behaviour change interventions. It starts by developing an understanding of the factors driving the behaviour to be changed and links that to evidence based interventions likely to affect those drivers in the particular situation.43

Complexity

The results of traditional linear approaches to implementing practice change or innovation are often not sustainable or scalable because they do not take account of the complexity of the health system or the proposed intervention.45 Learning communities enacting learning cycles must appreciate the complexity of the problem under investigation as well as the constantly evolving systems into which any resulting intervention would be introduced. Identifying and managing complexity is critical to successful implementation of change. Fortunately, there are tools to help identify and manage complexity.46 For example, the NASSS (non-adoption, abandonment, scale-up, spread, sustainability) framework provides a systematic, evidence based model to guide organisations in understanding and managing such complexity.4546