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

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 evaluative 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.

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit

“…Healthcare is a complex product-service ecosystem consisting of many stakeholders (e.g., clinicians, patients, healthcare managers, insurance providers, regulators, etc) [72,90,121]. A large body of research has explored the iterative design of healthcare products and services with a focus on stakeholder engagement in the early design stages [8,26,57,99,104,145].…”

“…Recent HCI research has developed healthcare AI systems with special attention to challenges around workflow integration [5,16], calibrating clinician trust [59,109,133], transparency and setting mental models [19,53], and risks of biases and harm [129]. Relatively little work engaged healthcare stakeholders in the early stages of AI development to envision concepts that leverage AI capabilities or explore data requirements with an eye for downstream applications [90,133]. Our work aims to address this gap, specifically within the context of intensive care.…”

Four Patterns of Data-Driven Design Activities in New Product Development

“…In contrast, explore the usage patterns of different personal health devices through an in-situ case study. Similarly, Noortman et al (2022) introduce recommendations on how to scale up the data infrastructure and data handling processes in the clinical context, which can conflict with the explorative character of the data-enabled design. Wilberg et al (2017) propose a data strategy development process for data-driven design.…”

Exploring Health and Design Evidence Practices in Ehealth Systems’ Development

“…The complexity arising from the interrelation of the multiple aspects to generate evidence about eHealth systems (e.g., clinical outcomes, usability) calls for a constructivist approach to their development. However, due to its exploratory nature, this approach presents shortcomings for required procedures (e.g., medical ethical approval) in clinical development (Noortman et al, 2022); hence, this approach needs adjustment. Yet, an entirely logical-positivism approach to evidence generation will not satisfy either due to eHealth complexity and the difficulty this represents to formulate and test predictions about the system's intended (and unintended) effects in the real world.…”