- This case study was provided by Cardiff and Vale University Health Board.
- The action learning sets were built in to the MAGIC programme to facilitate collaboration with primary care teams.
- The project set out to cascade learning and build stronger quality improvement methodology into the MAGIC programme in Cardiff.
- At an action learning set event, a short presentation on quality improvement was given, and teams worked together to apply learning to their activities.
Early in 2011, Cardiff’s core MAGIC team members participated in intensive training in quality improvement (QI) methods, and this highlighted the need for building strong methodology into programme work.
The MAGIC programme has four action learning sets for facilitating cross-practice collaboration with primary care teams. At these events, practices can review and reflect on their activities and share learning. The March 2011 action learning set was chosen as a suitable forum for cascading the QI training.
The lead team from each practice attended – if available, this included a GP, nurse and practice manager. The agenda focused on the first five steps of improvement work: forming the team, setting aims, establishing measures, selecting changes, and testing changes. After a short QI presentation, teams worked together to complete a table that covered the five steps.
The QI approach was well received. All teams appreciated its relevance and thought it helped frame ideological change in smaller practical steps. Discussions revealed common work areas and shared challenges. This allowed teams to share learning and materials, and consider activities that could apply to their setting.
Prior to the action learning set, some teams struggled to see how their work fitted with the broader programme aims. Many felt overwhelmed by what they wanted or thought they should do. QI training enabled greater clarity and the confidence to take the work forward independently. Understanding measurement helped teams move forward, while recognising the need to test changes and document learning.
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