JMIR Publications

ABSTRACT

Background:

Natural language processing (NLP) 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 NLP to the conversion of raw voice data generated in the clinical field into text using speech-to-text (STT) algorithms.

Objective:

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.

Methods:

From June 4, 2021 to September 12, 2021, RMIS-AI, using a machine learning engine trained with 1,717 triage cases over six months, was prospectively applied in clinical practice in a triage unit. We analyzed a total of 1,063 triage tasks performed by 19 triage nurses who agreed to participate. The primary outcome was the time for participants to perform the triage task.

Results:

The median time for participants to perform the triage task was 204 (155,277) s by RMIS-AI and 231 (180,313) s using manual method; this difference was statistically significant (P < 0.001). 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 complaints and past medical history, RMIS-AI showed a higher record completion rate than the manual method. Categorical variables entered by RMIS-AI showed less agreement compared with continuous variables, such as vital signs.

Conclusions:

RMIS-AI improves the promptness in performing triage tasks as compared to that using the manual input method. However, to make it a reliable alternative to the conventional method, technical supplementation and additional research should be pursued.