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A Systemic Design Framework for AI-enabled Healthcare: Improving health and wellbeing of people with learning disabilities

Format: Papers, RSD11, RSD11: Methods and the worlds they make, Topic: Health & Well-being, Topic: Sociotechnical Systems

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

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Citation Data

Author(s): Gyuchan Thomas Jun, Satheesh Gangadharan, Georgina Cosma, Panos Balatsoukas, Cecilia Landa-Avila, Francesco Zaccardi, Michelle O'Reilly, Ashley Akbari, Vasa Curcin, Rohit Shankar, Reza Kiani, Neil Sinclaire, and Chris Knifton
Year: 2022
Title: A Systemic Design Framework for AI-enabled Healthcare: Improving health and wellbeing of people with learning disabilities
Published in: Proceedings of Relating Systems Thinking and Design
Volume: RSD11
Article No.: 085
URL: https://rsdsymposium.org/a-systemic-design-framework-for-ai-enabled-healthcare-improving-health-and-wellbeing-of-people-with-learning-disabilities
Host: University of Brighton
Location: Brighton, UK
Symposium Dates: October 3–16, 2022
First published: 23 September 2022
Last update: 30 April 2023
Publisher Identification: ISSN 2371-8404