Sense Making
Make sense in context: explore contributory factors to identify and understand issues. 
Invest time to understand what is and is not working, and explore the influencing factors. There are a variety of tools and techniques to help us understand an issue or problem. For instance:
- The Scottish Approach to Service Design (SAtSD) focusses on "discovering and defining" the problem before developing and delivering the solution.
- The QI Journey emphasises the importance of understanding how a system is currently working before making decisions about required changes.
Segmenting data and understanding variation
Data Segmentation is the process of dividing data, then grouping it together based on specific parameters. Interrogation of the data in this way, may enhance understanding of different groups:
- at a population
- sub-population; or
- high-risk population level
Enhanced understanding through data analysis will aid identification of potential opportunities and/or challenges to be addressed, improve efficiency and reduce waste, support the design of appropriate interventions and may also help to identify and address potential inequalities
A key strategy in learning systems is the continuous monitoring and control of variation; analysing variation to understand what is intended, unintended, common or special cause will help with decisions about appropriate action.
There is an enormous amount of literature dedicated to understanding variation. The NES QI Zone may be a helpful place to start.
Consider the following questions:
- what data are available to enable effective assessment of system performance?
- what additional data may be required to enhance the learning?; and
- how can this data be collated and analysed without excessive additional burden?
Drawing out generalizable themes
Compare the very different adoption trajectories of two 19th century medical discoveries - surgical anaesthesia (7 years to adoption) versus antisepsis (30+ years to adoption).
There are many similar examples of effective or evidence-based practice not rapidly adopted. Generalizability is the extent that new knowledge can apply in other settings. When making sense of new information (evidence, data, innovation and/or methodologies), it is important that we take the time to understand whether it is general to all contexts, or if it needs to be tailored for a specific context.
The spread of new ideas relies on the characteristics of both the idea and the people adopting it. Everett Rogers' Diffusion of Innovation Theory highlights these two characteristics:
- people related characteristics of adoption; and
- idea related characteristics of adoption.
A common failure in spread of new ideas results from taking a new idea and applying it in a new context and expecting it to be accepted/applied. A range of tools to improve adoption of ideas or methods exist, such as Kaiser Permanente's checklist for scaling up.
Source: Scaling up improvements more quickly and effectively, Appendix 2, academic.oup.com
Consider the following questions:
- what is the improvement/idea/method identified within the learning system that merits scale-up or spread?
- what is the system/service?
- what is the context?
- how might you identify the appropriate “scaleable unit” – the smallest representative of the system targeted for full-scale implementation?; and
- what is your compelling story that will energise others to invest their time and efforts?
Understanding system enablers and barriers
Once we understand a system it is possible to maximise enablers and address barriers, to support learning and improve outcomes. Gain stakeholder intelligence through shared findings, facilitated events and Learning System networking.
Participant experience can be used to identify compelling examples worth spreading and to harness approaches that may help to reduce barriers.
Consider the following questions:
- what are the enablers and barriers pertinent to the purpose of the learning system?
Identifying key cultural and behavioural conditions
Do not underestimate the importance of cultural and behavioural conditions to learning. The Quality Management System Framework emphasises the importance of leadership beliefs, attitudes, skills and behaviours.
Leaders should promote a focus on issue analysis without blame, recognise and celebrate success and embed coaching and compassionate leadership into management practice.
The IHI White Paper A Framework for Safe, Reliable and Effective Care identifies 9 components of culture and the learning system and how they interact.
Leadership – Facilitating and mentoring teamwork, improvement, respect and psychological safety
Psychological safety – Creating an environment where people feel comfortable and have opportunities to raise concerns and ask questions
Accountability – Being held to act in a safe and respectful manner, given the training and support to do so
Teamwork and communication – Developing a shared understanding, anticipation of needs and problems and agreed methods to manage these and address conflict situations
Negotiation – gaining genuine agreement on matters of importance to team members and service-users
Transparency – openly sharing data and other information
Reliability – applying best evidence, minimising variation with the goal of failure-free service delivery over time
Improvement and measurement – improving work processes and patient outcomes using standard improvement tools including measurement over time
Continuous learning – regular collection of intelligence and learning from successes, challenges and defect/failure
Understand if this is a new innovation or change in methodology
To determine best next steps and to support turning knowledge into action, it is important that learning system participants take the time to analyse and make sense of new information/data/evidence. Clarity about what the learning and data suggests will support decisions related to further promotion and dissemination.
Consider the following questions:
- how do we harness the collective intelligence to identify key learning and inform next steps?
- is this an innovation or does it impact a process? Is there data to show this?; and
- can this intelligence further inform our approach to data, be it collection, collation, analysis or dissemination?