Raisa Ladha, BPH, MSc, PhD(c)
McMaster University
"Purpose: As artificial intelligence (AI) is increasingly introduced to improve efficiency in healthcare, understanding how frontline providers perceive these tools is essential. This study examines the intersection of clinical governance and AI in nursing professionals’ triage-related decision-making, focusing on how perceptions of professional autonomy shape AI acceptance in emergency departments (EDs).
Methods: A cross-sectional mixed-methods survey was developed in REDCap with input from experts across hospital leadership, academia, and government. Distributed to nursing professionals across Ontario, the survey yielded 57 responses from 39 EDs. Quantitative data examined associations between perceived autonomy, experience and receptivity to AI, while qualitative responses underwent thematic analysis to capture nuanced perspectives.
Results: Triage was described as a complex, situational process shaped by both clinical urgency and patients’ broader circumstances. Many respondents noted that, because decisions reflect the moment they are made, they cannot be held accountable for eventual outcomes. Within this context, participants were cautious about AI: most questioned its reliability and reported using decision-support tools mainly to validate, rather than guide, clinical judgment.
Conclusions: AI adoption in EDs will require governance approaches that preserve professional autonomy and ensure tools complement, rather than undermine, nursing expertise."
Methods: A cross-sectional mixed-methods survey was developed in REDCap with input from experts across hospital leadership, academia, and government. Distributed to nursing professionals across Ontario, the survey yielded 57 responses from 39 EDs. Quantitative data examined associations between perceived autonomy, experience and receptivity to AI, while qualitative responses underwent thematic analysis to capture nuanced perspectives.
Results: Triage was described as a complex, situational process shaped by both clinical urgency and patients’ broader circumstances. Many respondents noted that, because decisions reflect the moment they are made, they cannot be held accountable for eventual outcomes. Within this context, participants were cautious about AI: most questioned its reliability and reported using decision-support tools mainly to validate, rather than guide, clinical judgment.
Conclusions: AI adoption in EDs will require governance approaches that preserve professional autonomy and ensure tools complement, rather than undermine, nursing expertise."
