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Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study
Bridget Nicholson1, Elizabeth A Sloss1, Aref Smiley2
1College of Nursing, University of Utah, 10 2000 E, Salt Lake City, UT, United States.
JMIR Nursing
|February 4, 2026
Summary
Oncology nurses believe artificial intelligence (AI) symptom models can improve cancer care by enabling early intervention. Key factors for adoption include model transparency, nurse involvement, and evident benefits for patient symptom management.
Area of Science:
- Oncology nursing
- Artificial intelligence in healthcare
- Symptom management
Background:
- Cancer patients face significant symptom burden impacting treatment.
- Current symptom management relies on patient reporting and clinical response.
- Predictive symptom models using AI offer early risk identification for timely intervention.
Purpose of the Study:
- To explore oncology nurses' perceptions of predictive symptom models in cancer care.
- To identify factors influencing the adoption of AI-driven symptom care innovations.
Main Methods:
- Study guided by Rogers Diffusion of Innovation Theory.
- Oncology nurses rated AI symptom model perceptions using Likert scales and provided qualitative comments.
- Data analyzed using descriptive statistics and content analysis with inductive coding.
Main Results:
- Nurses agreed AI models could enhance symptom management and early intervention (67%-86%).
- All nurses found symptom information helpful; 73% believed it would save time.
- Key adoption themes included model compatibility, perceived patient benefit, improved clinical processes, accuracy concerns, and implementation factors.
Conclusions:
- Oncology nurses support predictive symptom models for improved cancer symptom management.
- Essential for adoption: AI model transparency, nurse involvement in development, and observable benefits.
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