Learning & improvement platforms

‘Engaged requesters’ within learning and improvement platforms, such as guideline-implementation programs and change-management initiatives, are another key demand-side component of the evidence-support system. Ideally they, like requesters within advisory and decision-making processes, are supported by structures and processes to:

  1. enable the incorporation of evidence into their learning and improvement platforms
  2. build and sustain an evidence culture
  3. strengthen capacity for evidence use.

Such enablers, culture and capacity can move us beyond individual champions and towards the routinized use of evidence in implementation efforts.

The McMaster Health Forum, in its role as the secretariat for Rapid-Improvement Support and Exchange (RISE) alongside its role as the ESN-CA secretariat, has contributed to a number of resources related to operationalizing a ‘learning health systems’ (LHS) approach, particularly the LHS action framework and our pico and nano courses on learning health systems. The framework introduces five ‘gears’ in a learning and improvement cycle it supports: 1) analytics and population insights; 2) evidence synthesis and support; 3) patient, family, caregiver and provider co-design; 4) implementation; and 5) evaluation, feedback and adaptation.

In gear 1 we use data analytics – the focus of ESN-CA’s data analytics hub – among other forms of evidence to address questions like: Where are system gaps & what’s driving them? Where are the inequities? What priorities are we addressing (or what problems are we solving)? In gear 3 we use evidence syntheses – the focus of ESN-CA’s evidence-synthesis hub – alongside several local forms of evidence to address questions such as: What evidence-informed solutions exist? How will solutions be adapted/designed with input from system users and communities? In gears 4 and 5 – the focus of ESN-CA’s besci hub – we use behavioural and implementation research to answer questions like: Does this model work? How & for whom? What adaptions are needed to cement & scale?

Many of the tools, as well as the misinformation support and ‘AI for policy’ work, developed to help ‘engaged requesters’ within advisory and decision-making processes can also help engaged requesters in learning and improvement platforms.

For additional perspectives on better supporting ‘engaged requesters’ in learning and improvement platforms, explore ESN-CA brief 2. It lists five possible next steps, with two being enablers and three relating to capacity:

  1. create a ‘front door’ to the learning and improvement platform through which evidence-support providers can learn about evidence needs and ‘windows of opportunity’ to meet these needs
  2. document and report on how different forms of evidence are being used in learning and improvement efforts
  3. provide learning opportunities to help staff keep pace with the rapid changes happening in the evidence-support system and broader global evidence architecture (e.g., ultra-rapid evidence support and AI-enabled, policy-scale living evidence syntheses)
  4. build capacity among staff who play evidence-intermediary roles to work with timely, demand-driven evidence-support units
  5. build capacity among staff who play evaluation roles to monitor the implementation and evaluate the impacts of AI tools being introduced into service delivery.