Comparison of medical interpreting modalities: a narrative review

Introduction

Background

Language barriers significantly impact patient outcomes, necessitating effective linguistic support to ensure high-quality healthcare delivery (1,2). Patients facing language barriers have been reported to experience higher rates of misdiagnosis (3) and delays in medical treatment (4), creating substantial challenges for language-discordant individuals in medical settings. As globalization advances and immigrant populations continue to grow, linguistic barriers in healthcare are anticipated to become an increasingly pressing global issue.

Various tools, such as plain language (5) and pictograms (6), are used to aid communication. However, in situations requiring high levels of accuracy and nuance, medical interpreting remains the gold standard in healthcare. Medical interpreters are vital in healthcare settings, bridging the gap between patients and healthcare providers when they do not share a common language. Their role extends beyond language translation to include cultural mediation, patient advocacy, institutional navigation, information gathering, and explaining medical concepts in an understandable manner (7). They also coordinate communication, ensuring all parties can express themselves and have the opportunity to ask questions (8). The acceptance and use of medical interpreters in healthcare settings have grown over time, recognizing their importance in bridging communication gaps and improving patient outcomes (9).

The working conditions of medical interpreters were significantly affected during the coronavirus disease 2019 (COVID-19) pandemic. Hospitals imposed strict access restrictions, limiting entry for patients, their families, medical personnel, and trainees (10,11), and medical interpreters were no exception (12). While travel limitations made it difficult for foreign tourists to travel internationally, foreign-born residents facing language barriers continued to require medical care (13). Such circumstances promoted the utilization of remote (mobile) medical interpreting (5), as reported in studies highlighting increased use during the COVID-19 pandemic (12,14), which connects healthcare providers to off-site medical interpreters over telephone or video calls (15,16). Furthermore, advancements in digital device technology extended beyond improvements in communication, encompassing significant progress in machine translation technologies and interpreting (17-19).

Rationale, knowledge gap, and objective

This study reviews the literature on spoken-language medical interpreting and uniquely offers a three-way comparison of in-person, remote, and machine modalities. This review differs from prior reviews (2,20,21), which focused primarily on in-person and remote interpreting in English-speaking contexts. Our study incorporates both English and Japanese literature and provides a three-way comparison, including machine interpreting as an emerging modality, to address evolving trends in language services across diverse clinical settings. We present this article in accordance with the Narrative Review reporting checklist (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-16/rc).

Methods

A literature search was conducted in PubMed (MEDLINE) and Ichushi (Ichushi-Web: Japanese Medical Literature Database). Although our review is narrative in nature, we adopted the structured search strategy referring to a previously published literature review (20) to enhance the rigor and transparency of the literature identification process. The search was restricted to manuscripts published in English and Japanese up to June 29, 2022. Studies employing one or more modalities of medical interpreting—including in-person, telephone, video, or machine interpreting—within healthcare settings were included in this review. Studies focusing solely on sign language interpretation, rather than spoken language interpreting services, and manuscripts of which the full texts were unretrievable, were excluded. The detailed search strategy is outlined in Table 1.

Two authors independently screened the titles and abstracts of the retrieved studies for eligibility based on the predefined inclusion and exclusion criteria. Discrepancies were resolved through discussion between the authors. Full-text articles of preliminarily included studies were obtained and reviewed to determine the final eligibility, utilizing the Rayyan platform, a web-based application designed to streamline blinded title/abstract screening and facilitate reviewer collaboration and conflict resolution (22). Additional references were identified during the full-text review through backward citation tracking, forward citation alerts, and expert consultation. These included several relevant studies on machine interpreting published after the initial search date.

As the literature review progressed, an increasing number of references addressing machine interpreting, alongside in-person and remote medical interpreting, were identified. As a result, although this study was initially designed as a comparative analysis of in-person and remote medical interpreting, relevant insights on the evolving technology of machine interpreting were also extracted from the literature. Cross-sectional studies, randomized controlled trials, reviews and commentaries were included.

Results and discussion

Characteristics of each interpretation method

In-person medical interpreting

In-person interpreters are trained professionals who interpret while they are physically in the room with the patient and provider. They may be employed by the hospital or a service dispatching them on a shift or per appointment basis, and waiting time can vary depending on interpreter availability. They may follow the patient from check-in through payment or only interpret in particular settings (for example, interpreting interactions with medical staff but not clerical staff).

Bilingual providers and staff may be utilized, but they may not have training in medical interpreting. Neither do ad hoc interpreters, family or friends, who accompany the patient to the appointment and bridge the linguistic gap (23-25). Countries such as the United States and Japan have certification programs for medical interpreters. Professionals are certified and/or trained and can be assumed to be accurate (26) and follow standards of impartiality and professionalism (27), but their numbers are insufficient to meet the growing demand (27,28). This review focuses on certified and/or trained medical interpreters and does not explore the differences between them, bilingual providers, and ad hoc interpreters.

Remote medical interpreting

Remote interpreters are trained professionals who are not physically at the point of care. Remote interpreting may involve two-way communication (when the patient and provider are co-located and the interpreter is remote) or three-way communication (when all three parties are in separate locations, such as in telehealth scenarios).

Machine interpreting

Although not yet widely studied in clinical trials, machine interpreting is increasingly discussed in the literature as an emerging tool. It operates entirely without a human component. Systems convert audio input into text, translate it, and then convert the result back into audio. Artificial intelligence (AI) interpreting uses the same method with the addition of large language model (LLM) processing (29).

Strengths and weaknesses of each interpreting method

In-person medical interpreting

In-person medical interpreting has been associated with higher satisfaction levels than other modalities (21,30). Several factors may account for this preference. First, when scheduled in advance, as in outpatient clinics, in-person interpreters help minimize waiting times (31), and may also shorten total patient visit times (32). Second, face-to-face interactions facilitate rapport-building, often favored in medical encounters (33). Even when waiting is inevitable, in-person interpreters can engage with patients and healthcare providers before consultations, fostering a sense of trust and familiarity.

An additional advantage of in-person interpreting, particularly in comparison to remote modalities, is the communication environment. In-person interpreting facilitates non-verbal communication, such as eye contact, gestures, and body language, which may support better understanding in complex medical conversations.

However, a notable limitation of in-person medical interpreting is the scarcity of qualified interpreters. Many healthcare institutions do not have in-person interpreters, necessitating prior scheduling (31). As a result, in-person interpreting is often associated with longer scheduling delays prior to the appointment compared to remote interpreting (34). Furthermore, for less commonly spoken languages, there is an increased likelihood that the interpreter and patient may be personally acquainted (31), potentially affecting confidentiality and impartiality.

Remote medical interpreting

A key advantage of remote medical interpreting is its ability to provide language support in cases where in-person interpreters are unavailable, particularly for rare languages or during emergencies (34,35). Moreover, remote interpreters are typically certified professionals who adhere to rigorous standards of impartiality and professionalism, ensuring precise and reliable communication (31).

Remote interpreting provides several key advantages beyond accessibility. It helps preserve privacy by reducing the likelihood of prior acquaintance between the patient and interpreter (16,31). Another critical benefit is the elimination of direct physical contact between interpreters, patients, and healthcare providers, effectively removing the risk of transmitting disease. This aspect proved particularly advantageous during the COVID-19 pandemic (12,14).

However, remote medical interpreting is not without its limitations, with primary drawbacks due to the technological infrastructure it requires. Adopting the devices and technology required for remote interpreting may pose challenges for some staff members (28,36,37), and integrating third-party services into hospital workflows can be logistically complex for healthcare institutions (38). Positioning a tablet or screen for video interpretation may be cumbersome in clinical settings, particularly during medical procedures such as pelvic examinations (39). Background noise can interfere with the intelligibility of communication (39). When the interpreter is not physically present in the room, trust-building between them and patients may be hindered (39), and the loss of nonverbal communication cues in telephone-based interpretations can further diminish the effectiveness of the interaction (31,40), potentially impacting patient satisfaction, especially in three-way remote interactions such as telehealth, where the patient, provider, and interpreter are all in separate locations.

Remote interpreting also presents practical limitations. Managing conversations with multiple speakers, such as family meetings, can also present challenges (39). Remote interpreting does not necessarily result in significant reductions in costs, time, or space requirements (39), and securing interpreters for less commonly spoken languages may require a considerable portion of the consultation time (31).

Machine interpreting

Machine interpreting in healthcare settings presents distinct advantages alongside notable limitations. One of its primary strengths lies in its accessibility, as it facilitates immediate language support without needing an in-person or remote medical interpreter (29). Provided that the patient or healthcare provider possesses the necessary device, interpretation services can be readily accessible, even for rare languages, potentially reducing both the waiting times and financial burden of traditional medical interpreting.

Despite its accessibility, substantial concerns persist regarding the reliability and accuracy of machine interpreting in medical contexts. Authoritative organizations such as the National Health Service (NHS, United Kingdom) have explicitly advised against its use in clinical settings (31). Users may also confront technical issues when utilizing the device and experience difficulties in fostering a mutual dialogue between patients and healthcare providers, potentially leading to anxiety about possible translation errors (26). Machine interpreting may be difficult in complex, high-context languages such as Japanese, which includes honorifics (41) and subject omissions (42), as they require the machine to interpret context that is not necessarily described explicitly in the dialogue. When AI-driven language models are employed, there is an inherent risk that patient information may be utilized in the machine learning process, raising privacy concerns regarding protecting sensitive personal health information (29).

Evidence comparing the utilization of remote and in-person medical interpreting

Evidence comparing the utilization of remote and in-person medical interpreting suggests that in-person interpreting is generally preferred and associated with better patient and provider satisfaction, although previous studies remain inconsistent.

Several studies have examined the comparative effectiveness of interpreting modalities. A systematic review indicated that in-person interpretation is rated higher than other modalities, with specific advantages in patient experience and consultation efficiency (21). Additionally, while telephone and ad hoc interpreters were associated with longer visit times, hospital-based in-person interpreters did not contribute to increased consultation duration, highlighting their efficiency in clinical workflows (43). Spanish-speaking patients in South Carolina also preferred in-person interpreting to remote interpreting (30). However, in a primary care setting in California, patient satisfaction with video interpretation was higher than with in-person and ad hoc methods, though clinicians reported no significant differences between the modalities (35), and a study in a pediatric emergency department in the United States, remote interpreting was preferred, while the quality of remote interpreting was also deemed non-inferior (44). A study during postpartum visits, based on provider perspectives, also favored remote interpreting over in-person interpreting (45). One study in Hawaii, reflecting patient perspectives, also favored ad hoc interpreters overall, with telephone interpreting favored the least (25).

Accuracy and quality are other critical factors in evaluating interpreting modalities. Overall, professional in-person and remote interpreters exhibit similar accuracy rates, significantly outperforming ad hoc interpreters, therefore reducing the risk of miscommunication and potential clinical errors (35). Some studies rate video interpreting as higher than in-person interpreting in accuracy, while telephone interpreting was rated lower than in-person interpreting (46). A study also reported remote interpreting had fewer errors compared to ad hoc and in-person interpreting (47).

Regarding remote interpreting, several studies have compared video and telephone interpreting in healthcare settings. Both video and telephone interpreting services proved feasible in Europe (48,49), while patient understanding seemed to be promoted with the use of video interpreting (50).

Despite these findings, the comparative clinical effectiveness of different interpreting methods remains underexplored. A study on diabetes patients found no significant differences in follow-up rates, time to follow-up, or HbA1c values between in-person and remote interpretation; however, the study was limited to a single visit, restricting its generalizability (51).

This section focuses on studies comparing in-person and remote interpreting. While machine interpreting was described in several references, none provided comparative clinical outcomes. Therefore, it is not included in this section.

Strengths and limitations

This review provides a synthesized comparison of three interpreting modalities—in-person, remote (telephone/video), and machine interpreting—based on diverse literature.

In-person interpreting consistently demonstrated higher levels of patient satisfaction and rapport, particularly in emotionally sensitive or complex cases. However, logistical limitations and interpreter shortages remain persistent barriers.

Remote interpreting, especially via video, has been shown to improve accessibility and maintain high accuracy, though it can present technological challenges and hinder non-verbal communication. The effectiveness of remote modalities appears to vary by clinical setting—some studies report equivalence to in-person services, while others note reduced patient engagement.

Machine interpreting offers immediate access and potential cost reduction but lacks accuracy in complex interactions and raises ethical concerns regarding data privacy. Notably, current literature consists mostly of policy statements and feasibility reports rather than comparative clinical trials.

Table 2 summarizes the practical strengths and limitations of each modality, providing a reference for clinicians and administrators. Discrepancies across studies may stem from differences in methodology, setting, and interpreter type.

A key strength of this study is its multilingual scope and its inclusion of emerging interpreting technologies. In contrast to prior reviews (2,20,21), which primarily addressed clinical outcomes of in-person and remote interpreting using English-language sources, this narrative review broadens the scope by including Japanese literature and discussing machine interpreting as a developing frontier. To the best of the authors’ knowledge, this is the first manuscript to present a three-way comparative analysis of in-person, remote, and machine medical interpreting modalities.

A notable limitation of this study pertains to the methodology employed for including machine interpreting in the review. The search for literature on machine interpreting was not conducted comprehensively, as it was not a primary focus at the study’s inception. Given the rapid advancements in machine interpreting technology, particularly with the integration of AI, further systematic and extensive reviews are warranted to attain a more thorough understanding of the current state of the literature.

Conclusions

Our study conducted a comparative analysis of medical interpreting modalities based on existing literature. While certain limitations impede the widespread implementation of machine interpreting in clinical settings, both remote and in-person medical interpreting remain dependable options for delivering linguistic support in healthcare. Each modality possesses distinct advantages and limitations. Careful consideration is needed when selecting the most suitable approach.

Based on our review, each interpreting modality offers distinct advantages depending on the clinical context. In-person interpreting is recommended for emotionally sensitive consultations, complex discussions, and situations requiring strong rapport, such as palliative care or mental health encounters. Remote interpreting, especially video, is well-suited to emergency departments, rural clinics, or infection-control scenarios where interpreter access is limited. Machine interpreting may serve as a supplementary tool for basic communication in low-risk settings, such as administrative interactions or triage, though caution is warranted due to concerns about accuracy and privacy. These best-practice recommendations are summarized in Figure 1. Given the limited evidence on clinical outcomes, further studies are essential to develop evidence-based guidelines for optimizing language services in healthcare environments.

Figure 1 Characteristics of each interpreting modality and suitable utilization settings. The characteristics, strengths and weaknesses, the suitable situation of each modality is presented.

Acknowledgments

The authors extend their gratitude to their colleagues, especially those in the Department of Emergency Medicine and Critical Care, as well as the International Healthcare Center (ICC) of the National Center for Global Health and Medicine (NCGM), for their insightful discussions on this topic. Additionally, the authors acknowledge the use of Grammarly (Grammarly Inc., San Francisco, USA) for preliminary language refinement. The icons used in the figure were derived from Icon-rainbow (http://icon-rainbow.com/), a free image source.

An abstract of this manuscript was presented at the 28^th Annual Meeting of the Japanese Society of Travel and Health (July 27, 2024, Yonago, Tottori, Japan), and selected components of the study were also disseminated at the 9^th Annual Meeting of the International Society of Clinical Medicine (November 23, 2024, Osaka, Japan).

The views expressed in this manuscript are solely those of the authors and do not necessarily reflect the positions of the affiliated institutions.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-16/rc

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-16/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-16/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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