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Graphical User Interface Development for a Hospital-Based Predictive Risk Tool: Protocol for a Co-Design Study.

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Summary
This summary is machine-generated.

This study used co-design to develop recommendations for an artificial intelligence (AI)-enabled risk prediction tool's graphical user interface (GUI). Engaging health professionals ensures the AI tool is usable and aligns with clinical workflows for better adoption.

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co-designhealth information technologyhealth professionaluser interfaceuser-centered design

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Artificial Intelligence in Healthcare

Background:

  • Co-design research is crucial for developing user-centered health technologies.
  • Engaging end-users in the design process of AI-enabled tools promotes adoption and effective use.
  • The aesthetic and functional design choices of health interventions are best determined by intended users.

Purpose of the Study:

  • To identify health professional recommendations for the graphical user interface (GUI) of an AI-enabled risk prediction tool.
  • To gather design and uptake recommendations from healthcare professionals for an AI risk prediction tool's GUI.

Main Methods:

  • A 3-phase co-design research method involving 36 health professionals.
  • Six iterative workshop activities (context of use, notifiers, format, AI survey-Likert, prototype, AI survey-written) repeated across workshops.
  • Qualitative (thematic analysis, framework of interactivity) and quantitative (descriptive statistics) data analysis, guided by the NASSS framework.

Main Results:

  • Project registration requested with the Australia and New Zealand Clinical Trial Registry (#384098).
  • Human research ethics approval obtained from South Australian Department of Health and Wellbeing and the University of South Australia.
  • Finalized design recommendations are anticipated in early to mid-2024; manuscript submission planned for mid-2024.

Conclusions:

  • Designing interventions for user adoption and integration into existing work practices is paramount for their success.
  • Co-design ensures AI tools meet end-user needs for aesthetics and functionality, aligning with clinical workflows.
  • Failure to align interventions with user practices can lead to dislike or even dangerous outcomes.