Designing for Control in Nurse-AI Collaboration During Emergency Medical Calls

Honorable Mention
AI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingPhysicians, Nurses & CliniciansPsychiatrists & Psychotherapists

AI-powered symptom checkers are automating the work of telephone triage nurses in assessing patient urgency. Yet, these systems exclude several vulnerable patient groups and overlook telenurses' competent interaction with their patients. This study, conducted in collaboration with telenurses, examines how AI can support their clinical assessment and was carried out in four phases: 1) interviews that revealed telenurses' challenge of juggling decision-support and documentation interfaces, 2) a co-design workshop that conceptualized continuous nurse-AI interaction, 3) development of a prototype that suggested questions for nurses to ask callers, and 4) a role-play workshop that demonstrated nurse-AI interaction in practice. The study addresses how we can design for control in human-AI collaboration in order to enhance, rather than replace, human decision-making processes.

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https://hci.top/en/papers/dis/118204/2023

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At a Glance

Paper Snapshot

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dataset
Source
DIS
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Year
2023
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Award
Honorable Mention
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Authors
6 authors
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Subtopics
AI-Assisted Decision-Making & Automation, AI Ethics, Fairness & Accountability, User Research Methods (Interviews, Surveys, Observation), Prototyping & User Testing
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Professions
Physicians, Nurses & Clinicians, Psychiatrists & Psychotherapists
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Content Status
Abstract only
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Related Papers
1 related papers