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TER062

AI Chatbots for Navigation of Healthcare System

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Project Information
Study complete - Work Published
Recover Code
TER062
Title
AI Chatbots for Navigation of Healthcare System
Recover Lead
Joshua Simmich
Co-investigators
Megan Ross, Trevor Russell
Status
Study complete - Work Published
Stream
Technology-enabled rehabilitation
CTP Relevance
Optimising service models
STARS
STARS-Facility Only (FO)
Ethical Clearance Number
--
Plans
No plans

Synopsis

To explore the effectiveness of an advanced AI chatbot using a large language model in helping users navigate the Australian healthcare system, when compared to traditional search engines (eg Google, Bing). This study will expand existing knowledge by providing empirical evidence on the comparative effectiveness of AI chatbots versus traditional search engines in real-world healthcare navigation. It will enhance our understanding of how AI can be integrated into healthcare systems to improve accessibility, reduce the burden on healthcare professionals, and ultimately lead to better patient outcomes.

Milestones

No milestones recorded yet.

KT Plan

No KT plan activities recorded yet.

Project Progress
15 Mar 2026 1:21 AM

This study compared the performance of a generative artificial intelligence (AI) search tool, Microsoft Copilot, with a conventional search engine, Google Web Search, for assisting Australians in navigating health care information. Consumers at the 2024 Dalby Roundtable identified navigating the healthcare system as a particular challenge. Large language model (LLM)–powered tools may improve access by generating direct, conversational answers. Ninety-seven adults from Queensland, Australia, completed a web-based survey involving scenario-based health care navigation tasks. Participants used either Microsoft Copilot or Google Web Search, and their responses were evaluated for accuracy using binary, graded, and numerical scoring systems. User experience was assessed using the Technology Rating Questionnaire (TRQ). Microsoft Copilot outperformed Google Web Search on two of eight tasks—identifying aged care application services and listing mobility allowance eligibility criteria—but showed no clear advantage in the others. Participants rated Google Web Search higher for willingness to adopt, perceived impact on quality of life, and ease of learning, while both tools received similar ratings for confidence, perceived value, and privacy concerns. Overall, generative AI achieved comparable accuracy to traditional search engines but did not improve user experience. Further research is needed as AI tools evolve and users become more familiar with them. Citation: JMIR AI 2025;4:e76203. doi:10.2196/76203

Linked Grants

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Linked Presentations
Linked KT Activities

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Linked CCI Activities

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