Advancing Education, Research, and Quality of Care for the Head and Neck oncology patient.
Introduction: Head and neck cancer presents complex physical, emotional, social, and financial challenges that can persist throughout a patient’s life. Although healthcare providers aim to address these issues during clinic visits, infrequent appointments combined with shorter visit times may leave gaps in ongoing patient support and survivorship care. Patients are increasingly turning to social media for accessible advice and peer support, with platforms like Reddit becoming prominent resources. Subreddits such as r/HeadandNeckCancer (1,246 members) and r/HNSCC (383 members) provide safe, anonymous spaces for individuals to share experiences, seek advice, and connect with others in similar situations. This anonymity allows for open, candid discussions that may not arise in clinical settings, capturing aspects of the patient experience that are often overlooked. By examining patient-generated data, we aim to capture deeper insights into this multifaceted community, using AI-driven findings to enhance patient-provider communication, healthcare policies, and survivorship care strategies.
Methods: We systematically collected posts, comments, and metadata from the r/HeadandNeckCancer and r/HNSCC Subreddits from December 2018 to October 2024, using Python and Reddit’s API. This study leverages coding platforms, including Python, Natural Language Processing (NLP), and Large Language Models (LLMs) such as ChatGPT, to analyze data from these Reddit communities. All text was then pre-processed. To facilitate Near Entity Recognition (NER), we employed a Bidirectional Encoder Representations from Transformers (BERT)-based NLP model, specifically SciBERT, alongside a custom medical term dictionary to identify and tag relevant medical terminology. Entries with fewer than ten words were excluded to maintain contextual relevance. A final categorization of themes was performed using AI-driven processes, assisted by ChatGPT, to organize content into specific categories and subcategories. All data remained anonymous, and we adhered to ethical guidelines to ensure user confidentiality and respectful engagement with patient narratives.
Results: From 826 posts and 8,507 comments, thematic analysis identified “Side Effects and Complications” (2,067 entries, 22.2%) and “Symptom Management” (1,671 entries, 17.9%) as the most frequently discussed topics. Posts seeking emotional support and advice constituted 454 (4.86%) entries, while 1,533 entries (16.4%) focused on personal journeys through diagnosis and treatment, capturing a wide array of unique patient experiences.
Conclusion: This study illustrates how AI and coding tools can capture the lived experiences of head and neck cancer survivors, offering critical insights beyond what is typically addressed in traditional clinical settings. Themes such as managing chronic side effects, fear of recurrence, anxiety stemming from biopsies and imaging, and the essential role of community support highlight the psychological and physical complexities of survivorship. These findings underscore areas where providers can enhance support throughout the patient journey, from initial work-up and treatment phases to ongoing follow-up care. By streamlining data collection and thematic analysis, our AI-driven approach enables a more comprehensive view of patient experiences, bridging gaps in understanding and fostering a patient-centered care model that better addresses the needs of individuals in head and neck oncology. Thus, providers can use these approaches to enhance the patient experience through their care continuum.