Artificial Intelligence for Language Access in Surgical Care: Patient Preferences and an Implementation Framework.

Authors

Maria Alejandra Montoya Rubiano, Sarah S Zahakos, Jesus Mejia, Daysi Martinez, Abraham Alatorre, Alyssa N Jones, Michelle Ramirez-Martinez, Meyly Santiago, Rachel E Murphy, Olivia F Lynch, Amanda J Reich, Brittany M Dacier, Kirsten E Austad, Regan W Bergmark, Alicia Fernandez, Kevin B Johnson, David W Bates, Gezzer Ortega

Published in

NEJM catalyst innovations in care delivery. Volume 7. Issue 3. Pages CAT250445. Epub Feb 18, 2026.

Abstract

Language discordance in surgical care is a structural driver of inequity that affects patient safety, trust, and outcomes. Emerging interpreter technologies, including artificial intelligence (AI) and remote video interpretation (RVI), are rapidly entering clinical settings. However, implementation decisions are often made without understanding how patients themselves perceive these modalities or whether they view them as replacements or complementary tools within their care. To explore Spanish-speaking surgical patients' perceptions of AI- and RVI-based interpreter technologies, and to understand how clinical context influences modality preferences, the author team conducted a descriptive concurrent mixed-methods study within a U.S. academic health system, enrolling 23 adult patients with Spanish language preference across the surgical continuum. The patients did not choose a single preferred modality; instead, they expressed context-dependent needs. AI was viewed as advantageous for its speed, privacy, and literal translation in straightforward or time-sensitive scenarios. RVI was favored for emotionally complex conversations and cultural nuance. Across narratives, patient agency emerged as a dominant theme. These findings support the development of a multifaceted language access infrastructure in which AI and remote human interpreters are deployed synergistically based on clinical sensitivity, urgency, and patient preference.

PMID:
42418606
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.

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