Department of Radiological Sciences, University of California, Irvine, Irvine, CA 92868, United States.
Amazon Web Services, East Palo Alto, CA 94303, United States.
J Am Med Inform Assoc. 2024 Nov 1;31(11):2660-2667. doi: 10.1093/jamia/ocae223.
Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM).
Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability.
Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices.
We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.
越来越多的患者被赋予直接访问其医疗记录的权限。然而，放射学报告是为临床医生编写的，通常包含医学术语，这可能会令人困惑。一种解决方案是让放射科医生提供一种通俗易懂的版本，供非专业人士使用。由于手动生成这些通俗翻译对放射科医生来说代表着巨大的负担，因此需要一种能够自动生成准确、易懂的面向患者的报告的方法。我们提出了一种通过向大型语言模型 (LLM) 提供专门提示来生成放射学报告通俗翻译的新方法。
我们的方法自动提取和定义医学术语，并在 LLM 提示中包含其定义。使用我们的方法和一种简单策略，为来自学术医疗中心的 100 份去识别神经放射学报告在 4 个不同的阅读水平生成了翻译。翻译由一组放射科医生评估准确性、可接受性、潜在危害和可读性。
我们的方法将“Findings”和“Impression”部分翻译为 8 年级水平，准确性分别为 88%和 93%。在所有阅读水平上，我们的方法比基线方法准确 20%。总体而言，使用标准可读性指数评估时，翻译比原始报告更具可读性。
我们发现，我们的 8 年级水平的翻译在准确性和可读性之间取得了最佳平衡。值得注意的是，这与面向患者的健康沟通的国家认可建议相对应。我们相信，使用这种方法起草面向患者的可访问报告将使患者受益，而不会显著增加放射科医生的负担。
