G157(P) A toolkit to identify barriers to discharge for children with medical complexity

Background The Children with Medical Complexities (CMiC) team is a multidisciplinary team working in a tertiary children’s hospital. Our aim is to provide a pop up service for children with medical complexity (CMC) tailored to the individual child and family. We describe the co-production, development and implementation of our CANVAS toolkit which captures information about the barriers to discharge to enable us to work to a smoother and earlier discharge. Aim To develop a mechanism to categorize barriers to discharge faced by CMC to enable working towards a faster and better discharge. Method Since 2013 we have been part of a weekly meeting where we discuss barriers to discharge for patients with lengths of stay (LOS) over 30 days. This identified a number of areas including housing, care provision and training. In 2014 we started collaborating with colleagues from social care, occupational therapy and community nursing to develop a forum for continued conversation around this group of children. With these stakeholders, parents with lived experience and young people from the Young Persons Advisory Group, we ran a process mapping exercise exploring the themes around barriers to getting home for CMC. We used all these avenues of information to develop the CANVAS categories and expanded toolkit. Results CANVAS categories were identified as follows. We have also developed specific data points in each category. 1. Care and commissioning 2. Allied health professionals and equipment 3. Medical Needs 4. adVocacy and educations 5. Accomodation 6. Safeguarding. Discussion The CMiC team has been working to improve how we support this group of children since its creation in 2014. The categories above enable us to capture all the challenges that we have faced as a team in supporting discharge for CMC. Together they enable us to provide an easily captured and communicated view of the palette of colour-coded care each child and family needs at any given point in time. We hope that prolonged data capture may enable early recognition of potential blockers for the individual as well as larger scale system issues.