Currently submitted to: Journal of Medical Internet Research
Date Submitted: Nov 24, 2025
Open Peer Review Period: Nov 25, 2025 - Jan 20, 2026
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Transforming Cancer Care Qualitative Data Analytics: Leveraging NLP to Uncover Insights from Big Qualitative Data
ABSTRACT
Background:
Qualitative research methods offer vital insights into how patients make treatment decisions, but these approaches are labor-intensive, limited by small samples, and difficult to scale. Natural Language Processing (NLP) provides a promising solution by automating the analysis of large volumes of unstructured clinical text, improving efficiency and enabling deeper understanding of complex interactions in cancer care.
Objective:
The objective of this study was to develop, test, and validate a Natural Language Processing (NLP) application capable of transforming large-scale qualitative clinical communication data into structured formats, thereby reducing the need for manual coding.
Methods:
Using 434 transcripts of physician–patient encounters collected from a prior study, we evaluated the feasibility of advanced NLP methods to analyze cancer care communication.
Results:
Transformer-based models demonstrated strong performance in extracting clinically relevant information, with RoBERTa achieving the highest F1 score (76%), outperforming both BERT (71%) and the rule-based SpaCy baseline (36%).
Conclusions:
These findings underscore the advantages of context-aware transformer architectures, which are better suited to capturing the complexity of medical dialogues than traditional rule-based approaches. Notably, while transformers provided the greatest accuracy, results also suggest the value of hybrid systems that integrate rule-based precision with the contextual depth of transformer models. Such approaches may be especially useful for capturing longer conversational sequences, such as emotional expressions, question–answer exchanges, and multi-topic utterances. Overall, this study demonstrates the potential of NLP to improve the efficiency and scalability of clinical communication analysis, expand institutional capacity to deliver standardized feedback, and enable large-scale, multi-site research on communication processes in cancer care. Clinical Trial: N/A
Citation
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