Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors (2023)

Abstract: A major challenge in the practical use of Machine Translation (MT) is that users lack information on translation quality to make informed decisions about how to rely on outputs. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use. This paper evaluates quality estimation feedback in vivo with a human study in realistic high-stakes medical settings. Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.

## introduction empowering people to decide when and how to rely on nlp systems appropriately is a critical, albeit challenging, endeavor. appropriate reliance is a moving target because it is difficult to operationalize, and depends on context and application domain. research in this space has so far relied on evaluations that are abstracted from real world use cases @xcite @xcite . we build on this work, and study appropriate reliance close to the actual decision that users have to make on the ground: whether or not to rely on a model output. we study this question in the context of physicians deciding when to rely on machine translation (mt) in an emergency room when communicating discharge instructions to a patient who does not speak their language @xcite ). mt represents an example of an nlp system used by millions of people in daily life @xcite , in-cluding in high-stakes contexts such as hospitals and courtrooms @xcite . mt errors in those settings can be particularly harmful. in our setting, incorrect discharge instructions could lead to a patient misunderstanding their diagnosis or taking medications incorrectly, with potentially life-threatening consequences. research shows that people tend to over-rely on systems @xcite , and that explainability techniques that aim to address this issue can instead increase blind trust in incorrect predictions @xcite . in the case of mt, most user studies have focused on human translators @xcite @xcite , who have the expertise to evaluate mt faithfulness and to correct outputs when needed. deciding how to rely on mt is much more challenging for people who use it to communicate in a language that they do not know. @xcite find that providing quality feedback to people using mt in travel scenarios has mixed effects, and can make them feel more confident in their decisions without improving their actual task performance. this work evaluates the impact of quality estimation (qe) feedback on physicians' reliance on mt, building on decades of mt research on automatically estimating the quality of mt without reference translations @xcite @xcite @xcite . however, qe systems are primarily trained to score overall translation quality outside of a specific context of use. it is unclear whether people will know how to interpret qe scores given prior evidence that they struggle to use probability and confidence estimates in explanations @xcite . additionally, even when interpreted correctly, it is not clear whether seeing a qe score will make users better at deciding when to rely on an mt output and when not to, e.g. due to clinically relevant errors. we compare qe with figure 1 : physicians who participate in the study are shown a sentence from a real discharge instruction, and its translation to chinese via google translate. they are asked to decide whether they would hand this translation to a patient who only speaks chinese. participants are randomly assigned to one of two conditions: a) quality estimation: a quality estimation model provides an evaluation of the translation, or b) backtranslation: the participant is shown the backtranslation of the text to english using google translate. we found that participants who were provided the quality estimation were better at detecting when to rely on a translation. for the most severe errors, backtranslation provided more reliable assistance. the sentence shown was marked as not adequate and life-threatening by bilingual physician annotators. the human reference translation for the google translate output states: your diagnosis is seizure of seizure, suggest we resume zonisamide and carbamazepine and one week of clonazepam-am-bridge. a method commonly used by lay users, including physicians, to estimate the quality of a translation: backtranslation into the source language using the same system @xcite . we conduct a randomized experiment with 65 english-speaking physicians to test how each of these interventions impacts their ability to decide when to rely on imperfect mt outputs (figure 1 ). we find: quality estimation quality estimation (qe), the task of automatically assessing the quality of mt outputs without access to human references, is a long-standing area of research in mt @xcite . state-of-the-art qe systems devel-oped for evaluating mt such as openkiwi @xcite , transquest @xcite , or comet-src @xcite are built on top of large scale multilingual models like bert @xcite or xlm-roberta @xcite . they are trained to predict direct assessment scores collected by crowdsourced workers or post-editing efforts as measured by hter @xcite . qe systems have been primarily evaluated "in vitro" by measuring the correlation of their scores with generic human judgments of quality collected independently from the context of use. qe has also proved successful at guiding human translation workflows @xcite . however, it remains unclear how useful qe is for non-professional end-users in practice. in this work, we present assessments derived from the state-of-the-art comet-src to physicians who do not speak chinese to help them decide when to rely on english-chinese mt. backtranslation we focus on scenarios where the input text is translated into a language unknown to the text's author. the onus to decide whether the output is acceptable therefore falls on the author, even though they do not have the expertise to assess translation quality. in these settings, people routinely use an intuitive feedback mechanism: backtranslation, which consists of using mt again to translate the original mt output in the input language, so they can compare it with the original in a language they understand. this practice has been decried in the mt literature @xcite as backtranslation provides a very noisy signal by lumping together errors from the forward and backward mt pass, and potentially hiding errors that the backward pass recovers from. nevertheless, people use backtranslation routinely, perhaps encouraged by interfaces that let them switch mt translation direction with a single click. however, little is known about the usefulness of backtranslation in these practical settings. @xcite conduct a user study evaluating the impact of backtranslation by lay users for the practical task of booking a hotel room in a language that they do not speak. they hypothesize that backtranslation feedback can help people craft the content of a message so it is correctly interpreted by the recipient. backtranslation feedback was found to greatly increase user confidence in a translation, without improving the actual translation quality. we study the impact of backtranslation in a clinical decision-making context and compare it to a qe model output. ## mt for cross-lingual communication between patients and physicians cross-lingual communication is imperative in the presence of language barriers between physicians and patients. we focus on a specific high-stakes context: helping physicians communicate discharge instructions to patients in the emergency department (ed). while medical interpreters often facilitate conversations between clinicians and limited english proficiency (lep) patients, this has been found to be insufficient in helping patients recall what they are supposed to do after getting discharged @xcite . further, comprehension of emergency discharge instructions is known to be an important contributor to patient non-adherence @xcite . our prior work has found that mt is frequently used in practice for tasks such as automatically translating discharge instructions @xcite , and thus provides a written record for patients to aid in comprehension at discharge time as well as recall and adherence. a key challenge is that it is difficult for physicians to ensure that patients comprehend written discharge instructions when they cannot verify the accuracy of a machinegenerated translation. as an added complication, limited health literacy and discharge plan complexity can lead patients to overestimate comprehension @xcite . designing mt for effective physician-patient communication involves many stakeholders. this work focuses on physicians as a starting point, as findings can inform their training and strategies for cross-lingual communication to maximize impact in the short-term. we leave to future work the equally important question of helping diverse lep patient populations rely on mt output adequately. ## methods we conducted a randomized controlled experiment to test how quality estimation and backtranslation impact physicians' appropriate reliance on mt. ## discussion we found that qe and bt may play a complementary role: qe can assist physicians in their decision to provide translated written discharge instructions to patients, while bt was more effective in detecting critical errors in mt systems. combining aspects of bt and qe feedback may therefore benefit mt users in high-stakes settings. our results show that, contrary to common mt wisdom, bt should not be entirely discounted as a quality feedback method for people, aligning with recent (a) correct identification of adequate translations (b) correct identification of inadequate translations figure 4: the qe treatment group had an overall higher cwa in correctly relying on mt systems when (a) translations were adequate whereas the bt treatment group had a higher cwa in judging (b) inadequate translations. ns: not significant; * * : significant with p-value of 0.01; * * * * : p-value of 0.0001. in the post-survey, we asked physicians how they would want translation quality estimates to be provided. respondents reported that they would most want a binary indicator of whether the translation was correct, an explanation of where the errors occurred, and error categories relevant for medical purposes. our study showed the potential of qe using one specific scheme, and motivates future work refining the presentation of qe feedback to best support physician needs. our study took place in the midst of many discussions around the use of large language models (llms) in clinical settings, including their potential to assist physicians in writing discharge instructions @xcite , and the development of dedicated clinical language models, such as gatortron which was trained on over 90 billion words from electronic health records @xcite . our survey asked physician respondents how they perceived the use of chatgpt in their workflows @xcite @xcite . we found 77% of physicians would use chatgpt in their clinical workflows, barring regulatory requirements and legal implications. more specifically, 78% responded they would use chatgpt to summarize patient notes, including symptoms and treatment plans, 60% responded they would use it to answer patient questions, 32% for clinical decision support and to make evidencebased recommendations, and 12% vowed to never use it in their actual clinical workflows. this further highlights the urgent need for nlp work that de-velops appropriate mechanisms for people to make appropriate use of language generation tools beyond mt in clinical settings. ## conclusion we conducted a randomized experiment with 65 english-speaking physicians to test how quality estimation and backtranslation interventions impact their ability to decide when to rely on imperfect mt outputs. we found that the qe group had a significantly higher accuracy in their overall decision to give or not give a translation to a patient, while the bt group detected critical translation errors more effectively. this study paves the way for future work designing methods that combine the strengths of qe and bt, and contributes a human-centered evaluation design that can be used to further improve mt in clinical settings. more broadly, this work provides support for the usefulness of explanations for helping people rely on ai tools effectively in a real-world task. our explanations focus on providing actionable feedback rather than explaining the internal workings of the mt model, aligning with recent calls to rethink explainable ai beyond interpretability @xcite . it remains to be seen how to provide feedback that gives users more agency in appropriately using imperfect mt outputs, and how to design for appropriate reliance on other nlp tools in clinical settings, including large language models.