Perspectives on Referral Pathways for Timely Head and Neck Cancer Care.

Importance: Timely diagnosis and treatment are of paramount importance for patients with head and neck cancer (HNC) because delays are associated with reduced survival rates and increased recurrence risk. Prompt referral to HNC specialists is crucial for the timeliness of care, yet the factors that affect the referral and triage pathway remain relatively unexplored. Therefore, to identify barriers and facilitators of timely care, it is important to understand the complex journey that patients undertake from the onset of HNC symptoms to referral for diagnosis and treatment. Objective: To investigate the referral and triage process for patients with HNC and identify barriers to and facilitators of care from the perspectives of patients and health care workers. Design, Participants, and Setting: This was a qualitative study using semistructured interviews of patients with HNC and health care workers who care for them. Participants were recruited from June 2022 to July 2023 from HNC clinics at 2 tertiary care academic medical centers in Boston, Massachusetts. Data were analyzed from July 2022 to December 2023. Main Outcomes and Measures: Themes identified from the perspectives of both patients and health care workers on factors that hinder or facilitate the HNC referral and triage process. Results: In total, 72 participants were interviewed including 42 patients with HNC (median [range] age, 60.5 [19.0-81.0] years; 27 [64%] females) and 30 health care workers (median [range] age, 38.5 [20.0-68.0] years; 23 [77%] females). Using thematic analysis, 4 major themes were identified: the HNC referral and triage pathway is fragmented; primary and dental care are critical for timely referrals; efficient interclinician coordination expedites care; and consistent patient-practitioner engagement alleviates patient fear. Conclusions and Relevance: These findings describe the complex HNC referral and triage pathway, emphasizing the critical role of initial symptom recognition, primary and dental care, patient information flow, and interclinician and patient-practitioner communication, all of which facilitate prompt HNC referrals.

Gender Bias in Artificial Intelligence-Written Letters of Reference.

OBJECTIVE: Letters of reference (LORs) play an important role in postgraduate residency applications. Human-written LORs have been shown to carry implicit gender bias, such as using more agentic versus communal words for men, and more frequent doubt-raisers and references to appearance and personal life for women. This can result in inequitable access to residency opportunities for women. Given the known gendered language often unconsciously inserted into human-written LORs, we sought to identify whether LORs generated by artificial intelligence exhibit gender bias. STUDY DESIGN: Observational study. SETTING: Multicenter academic collaboration. METHODS: Prompts describing identical men and women applying for Otolaryngology residency positions were created and provided to ChatGPT to generate LORs. These letters were analyzed using a gender-bias calculator which assesses the proportion of male- versus female-associated words. RESULTS: Regardless of the gender, school, research, or other activities, all LORs generated by ChatGPT showed a bias toward male-associated words. There was no significant difference between the percentage of male-biased words in letters written for women versus men (39.15 vs 37.85, P = .77). There were significant differences in gender bias found by each of the other discrete variables (school, research, and other activities) chosen. CONCLUSION: While ChatGPT-generated LORs all showed a male bias in the language used, there was no gender bias difference in letters produced using traditionally masculine versus feminine names and pronouns. Other variables did induce gendered language, however. ChatGPT is a promising tool for LOR drafting, but users must be aware of potential biases introduced or propagated through these technologies.

Portrait of a Surgeon: Artificial Intelligence Reflections.

Text-to-image artificial intelligence (AI) programs are popular public-facing tools that generate novel images based on user prompts. Given that they are trained from Internet data, they may reflect societal biases, as has been shown for text-to-text large language model programs. We sought to investigate whether 3 common text-to-image AI systems recapitulated stereotypes held about surgeons and other health care professionals. All platforms queried were able to reproduce common aspects of the profession including attire, equipment, and background settings, but there were differences between programs most notably regarding visible race and gender diversity. Thus, historical stereotypes of surgeons may be reinforced by the public's use of text-to-image AI systems, particularly those without procedures to regulate generated output. As AI systems become more ubiquitous, understanding the implications of their use in health care and for health care-adjacent purposes is critical to advocate for and preserve the core values and goals of our profession.