Breaking up data-enabled design: expanding and scaling up for the clinical context

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Abstract

Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed eval- uative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.
Original languageEnglish
Article numbere19
Pages (from-to)1-13
Number of pages13
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing
Volume36
Issue number1
DOIs
Publication statusPublished - 19 May 2022

Keywords

  • clinical context
  • clinical innovation
  • data-enabled design
  • design automation

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