The study, published in the Journal of General Internal Medicine, was led by Aubrey Samost-Williams of McGovern Medical School at the University of Texas Health Science Center at Houston, together with a multidisciplinary team spanning team science, anthropology, patient safety, and resilience engineering. The stakes are high: patients with a non-English language preference suffer harmful medical errors at higher rates than English-speaking patients, and those who experience diagnostic errors report more difficulty understanding follow-up instructions and more trouble finding interpreters when they need them. Language barriers are associated with disparities in clinical testing, delayed or missed diagnoses, and unplanned hospital readmissions.

To understand what interpreters actually contribute to safety, the team conducted three virtual focus groups with 17 current or former certified medical interpreters recruited from a large company providing interpretation services at more than 30,000 sites across the United States, Canada, and the United Kingdom. The focus group participants were a seasoned group, with between 5 and 24 years of experience, all fluent in English and Spanish, and all experienced with in-person, video remote, and phone interpretation. Five additional former interpreters reviewed the findings and checked that the researchers’ interpretations matched their lived experience. The analysis followed COREQ qualitative research guidelines and was approved by an institutional review board.

The analytical framework came from an unexpected corner of safety science: resilience engineering. Erik Hollnagel’s definition of a resilient healthcare system describes one that can adjust its functioning before, during, or after disturbances so that it sustains required performance under both expected and unexpected conditions. Hollnagel identifies four resilience potentials—the potential to respond, to monitor, to learn, and to anticipate. Rather than asking interpreters simply what their job involves, the researchers asked them to describe their experiences during ambulatory diagnostic visits for new or worsening symptoms, then mapped what they heard onto these four potentials.

The results revealed eleven behaviors clustered within the four potentials. Under the potential to respond, interpreters described managing the flow of communication during appointments, using verbal and nonverbal cues to keep patients and clinicians speaking in short phrases that allow more accurate interpretation. They described strengthening the patient’s voice, ensuring that tone and emotion survived the crossing between languages, and even mirroring a patient’s hesitancy in their own delivery when they sensed the patient was holding something back. They saw their role as extending from the moment a patient arrives until the moment they leave, helping patients schedule follow-up appointments and understand next steps. Some even described stepping in when they overheard inaccurate machine-generated Spanish drifting out of a clinic room they were merely walking past.

That last detail is a striking one, because it shows interpreters already acting as human quality-control agents for the very technologies meant to replace them. The study also found that interpreters monitor three things continuously during an encounter: the patient’s emotions and understanding, the dynamics of the clinical team, and the impact of technology on communication quality. Interpreters noted that patients often tell the clinician they have no questions, only to turn to the interpreter afterward to ask about a clinical detail—a disconnect that a word-for-word translation tool would never flag. Interpreters also reported miscommunications stemming directly from newer AI translation tools, and vulnerabilities in video-based interpretation such as poor connections and badly placed screens.

The potential to learn emerged from two sources. Interpreters frequently share a cultural background with the patient even when the clinician does not, positioning them to notice when a cultural misunderstanding could derail the diagnostic process. They described extracting sensitive information a patient would not otherwise disclose and navigating culturally mismatched diagnostic tests that could have profoundly affected the outcome. Over time, they also grew more confident speaking up and advocating for patients within the boundaries of company policies and national interpreter standards of practice.

The potential to anticipate proved perhaps the most sophisticated. Interpreters used cultural knowledge to predict which patients might not return for follow-up testing. In some cultures, they explained, medicine is viewed as infallible—so if symptoms persist after a first appointment, the patient assumes the fault must be their own. By surfacing that belief to the clinical team, an interpreter could prompt the clinician to address the concern preemptively, before it caused a patient to disappear from follow-up. Interpreters also proposed system-level improvements, such as formal guidelines for when virtual versus in-person interpretation is appropriate, and gender-matched interpreters for sensitive appointments in urology, gynecology, or mental health.

Crucially, the researchers found that these potentials do not operate in isolation. Monitoring behaviors feed responding behaviors: noticing a patient’s confusion allows the interpreter to intervene before a miscommunication hardens into a diagnostic error. Learning feeds anticipating: cultural knowledge gained over years lets an interpreter predict which patients need closer safety monitoring. This interlocking web of perception, judgment, and action is precisely what current AI interpretation tools lack. Those tools focus on rendering the words being exchanged, in written or spoken form, and do not account for the cultural brokering, emotional surveillance, team monitoring, and threat anticipation that human interpreters perform as a matter of course.

The implications for the healthcare industry are uncomfortable but clear. Interpreters are a finite and expensive resource, and institutions are understandably eager to swap them for cheap, always-available AI systems. But the study warns that measuring an AI tool purely by language accuracy threshold would miss the safety functions that human interpreters quietly provide. The authors suggest two paths forward. One is to design AI tools that integrate additional data sources, such as video or electronic health record data, to flag when a patient is at risk of misunderstanding or when cultural differences may be relevant. The other is a model of AI as teammate, in which AI handles raw language conversion while a human interpreter supervises and takes on the broader advocate and navigator role. The researchers also identified barriers on the human side: medical hierarchies can make it hard for an interpreter to challenge a clinician, and interpreters often noticed safety threats—poor team dynamics, patients unlikely to return—without feeling empowered to act. Short pre-appointment briefings, in which clinicians explicitly invite interpreters to voice safety concerns, emerged as a simple, powerful remedy. The study’s limitations are real: it captures only interpreters’ self-reported experiences, not the perspectives of patients or clinicians, and future work will need to test AI tools against human interpreters in real-world settings. But the core message is already actionable. As hospitals race to deploy voice-to-voice machine translation and even real-time in-ear interpretation devices, the quiet safety work of human interpreters—responding, monitoring, learning, and anticipating—must be designed into the workflow, or the resilience that protects millions of patients will simply vanish with the interpreter.

Subject of Research: The safety roles of medical interpreters beyond language translation and their implications for AI interpretation tools

Article Title: The Role of Interpreters in Supporting Resilience and Implications for Artificial Intelligence

Article References: Samost-Williams, A., Wermuth, P., Fernández Castillo, G., Zipkin, R., Hanley, K., Xie, Y., Newton, M. E., Salas, E., Tannenbaum, S., Wiig, S., Thomas, E. J., & Bell, S. K. (2026). The Role of Interpreters in Supporting Resilience and Implications for Artificial Intelligence. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10850-4

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10850-4

Keywords: medical interpreters, patient safety, resilience engineering, diagnostic error, artificial intelligence, language barriers, ambulatory care, qualitative research, healthcare disparities, machine translation, cultural brokering, Hollnagel resilience potentials