Exploring Cancer Patient Decision-Making Journeys: Insights from Investigators and Large Language Models

The study involved 33 cancer patients (22 women, 11 men) aged 28-83, primarily diagnosed with breast cancer (42.4%), and examined their cancer care journeys through three analytical approaches: investigator-led, ChatGPT-4o, and Gemini Advance Pro 1.5. Emotional coping, healthcare system experiences, support networks, information-seeking, and quality of life emerged as key themes from the investigator-led analysis. Contrastingly, ChatGPT-4o focused on emotional/physical challenges and healthcare interactions, highlighting systematic delays in care. Gemini Advance Pro emphasized decision-making and the discrepancies between patient expectations and reality. All three methods identified common themes, particularly around emotional challenges and decision-making, but differed in depth and focus. The investigator-led analysis provided nuanced emotional insights, whereas LLMs revealed structured patterns in patients’ healthcare navigation. This comparative analysis illustrates how different approaches can enrich understanding of the cancer experience, emphasizing the importance of both emotional and procedural dynamics in patient care.

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