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TRUVACE RECORD VERSION
record: TRV-2026-2200
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-23T06:45:19.201966Z
status: published
lens: p_space
sector: policy
headline: Governing artificial intelligence in Australian healthcare organisations - a qualitative study of staff perspectives
dek: Objective: This study aims to examine how healthcare staff conceptualise artificial intelligence (AI) governance and to develop an understanding of organisational requirements to support safe and responsible AI use within health services.
Methods: This qualitative study was conducted within a major Australian healthcare organisation delivering care across three hospital campuses and community-based services. Twenty-three semi-structured interviews were undertaken with staff and representatives across strategic l…
gain_title: (none)
problem_title: Staff at a major Australian health organisation described existing AI governance processes as fragmented, ad hoc and poorly tailored to AI's ethical, technical and operational challenges.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Staff at a major Australian health organisation described existing AI governance processes as fragmented, ad hoc and poorly tailored to AI's ethical, technical and operational challenges.
problem_evidence: Existing processes were commonly described as fragmented, ad hoc, and insufficiently tailored to the ethical, technical, and operational challenges posed by AI.
quick_read: Researchers interviewed 23 staff and representatives at a major Australian healthcare organisation to learn how they understand AI governance. Participants came from leadership, clinical implementation, research, regulation, legal oversight, operations and consumer engagement, and they described governance as an ongoing organisational function rather than a single approval step.

The findings matter because health services adopting AI need practical ways to keep it safe and accountable, and staff reported that current arrangements fall short. The study draws on one organisation and on staff perceptions, so it shows what participants think is needed rather than measured effects of any governance model on patient safety.
limitation: The study is a qualitative interview study at one major Australian healthcare organisation, so its findings reflect staff perspectives there and do not demonstrate outcomes of any governance approach.
tag: Evidence-backed problem
key_points: The study involved 23 semi-structured interviews, each lasting 30 to 70 minutes, using a grounded theory approach. | Five domains were identified: risk assessment, infrastructure and data, ethics, capability and culture, and governance structures. | Participants asked for AI-specific risk assessment pathways and lifecycle monitoring. | Staff also called for clearer approaches to consent, fairness and transparency, and for stronger data governance. | Participants wanted dedicated governance structures with defined oversight responsibilities.
rundown: The research took place in an Australian healthcare organisation delivering care across three hospital campuses and community-based services. Two investigators coded the interview transcripts independently, and themes were refined through team review and consensus.

Participants wanted internal capability and system navigation support so staff can work through AI adoption. They also pointed to the need for governance that continues after deployment instead of ending at initial approval.

The authors conclude that staff see AI governance as organisation-wide, needing clear pathways, defined accountabilities and sustained oversight. They present the five domains as a foundation for Australian health services seeking to operationalise safe and responsible AI.
sources:
- peer_reviewed | Australian Health Review | https://doi.org/10.1071/ah26110 | 2026-11-05
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