Cho Ara, Min In Kyung, Hong Seungkyun, Chung Hyun Soo, Lee Hyun Sim, Kim Ji Hoon
Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College, Seoul, Republic of Korea.
JMIR Med Inform. 2022 Aug 31;10(8):e39892. doi: 10.2196/39892.
Natural language processing has been established as an important tool when using unstructured text data; however, most studies in the medical field have been limited to a retrospective analysis of text entered manually by humans. Little research has focused on applying natural language processing to the conversion of raw voice data generated in the clinical field into text using speech-to-text algorithms.
In this study, we investigated the promptness and reliability of a real-time medical record input assistance system with voice artificial intelligence (RMIS-AI) and compared it to the manual method for triage tasks in the emergency department.
From June 4, 2021, to September 12, 2021, RMIS-AI, using a machine learning engine trained with 1717 triage cases over 6 months, was prospectively applied in clinical practice in a triage unit. We analyzed a total of 1063 triage tasks performed by 19 triage nurses who agreed to participate. The primary outcome was the time for participants to perform the triage task.
The median time for participants to perform the triage task was 204 (IQR 155, 277) seconds by RMIS-AI and 231 (IQR 180, 313) seconds using manual method; this difference was statistically significant (P<.001). Most variables required for entry in the triage note showed a higher record completion rate by the manual method, but in the recording of additional chief concerns and past medical history, RMIS-AI showed a higher record completion rate than the manual method. Categorical variables entered by RMIS-AI showed less accuracy compared with continuous variables, such as vital signs.
RMIS-AI improves the promptness in performing triage tasks as compared to using the manual input method. However, to make it a reliable alternative to the conventional method, technical supplementation and additional research should be pursued.
在使用非结构化文本数据时，自然语言处理已成为一项重要工具；然而，医学领域的大多数研究仅限于对人工手动输入文本的回顾性分析。很少有研究专注于将自然语言处理应用于使用语音转文本算法将临床领域生成的原始语音数据转换为文本。
在本研究中，我们调查了具有语音人工智能的实时病历输入辅助系统（RMIS-AI）的及时性和可靠性，并将其与急诊科分诊任务的手动方法进行比较。
从2021年6月4日至2021年9月12日，使用经过6个月1717例分诊病例训练的机器学习引擎的RMIS-AI在前瞻性地应用于分诊单元的临床实践中。我们分析了19名同意参与的分诊护士执行的总共1063项分诊任务。主要结果是参与者执行分诊任务的时间。
RMIS-AI使参与者执行分诊任务的中位时间为204（IQR 155，277）秒，手动方法为231（IQR 180，313）秒；这种差异具有统计学意义（P<.001）。分诊记录中输入所需的大多数变量通过手动方法显示出更高的记录完成率，但在记录额外的主要问题和既往病史时，RMIS-AI显示出比手动方法更高的记录完成率。与生命体征等连续变量相比，RMIS-AI输入的分类变量准确性较低。
与使用手动输入方法相比，RMIS-AI提高了执行分诊任务的及时性。然而，要使其成为传统方法的可靠替代方案，应进行技术补充和进一步研究。
