Gyuchan Thomas Jun,1 Satheesh Gangadharan,2 Georgina Cosma,1 Panos Balatsoukas,1 Cecilia Landa-Avila,1 Francesco Zaccardi,3 Michelle O’Reilly,3 Ashley Akbari,4 Vasa Curcin,5 Rohit Shankar,6 Reza Kiani,2 Neil Sinclair,7 and Chris Knifton8
Loughborough University (1) | Leicestershire Partnership NHS Trust (2) | University of Leicester (3) | Swansea University (4) | King’s College London (5) | University of Plymouth (60 | University of Nottingham (7) | De Montfort University (8)
The aim of this presentation is to present a systemic design framework developed by a research team for a project funded by the UK National Institute for Health Research (NIHR), DECODE – Data-driven machine-learning aided stratification and management of multiple long-term conditions in adults with learning disabilities. DECODE will analyse healthcare data on people with learning disabilities from England and Wales to find out what multiple long-term conditions (MLTCs) are more likely to occur together and what happens to some of these MLTCs over time. The end goal of DECODE is to utilise the AI-enabled new knowledge and develop actionable insights for effective joined-up social and health care for people with learning disabilities. The framework we are proposing consists of four steps: i) context analysis to understand the context of AI application; ii) AI output visualisation to develop user-friendly visualisations to display the outputs of AI analysis in a meaningful and accessible way; iii) actionable insight exploration to explore leverage points to improve join-up care coordination; iv) change process planning to evaluate the feasibility and ethical/legal risk of the usage scenarios. This framework will be of interest to many systemic designers who aim to develop a safe, ethical and cost-effective AI in healthcare.
KEYWORDS: artificial intelligence, health and social care, people with learning disabilities, systemic design
