ArticleJMIR medical informatics2022
Effect of Applying a Real-Time Medical Record Input Assistance System With Voice Artificial Intelligence on Triage Task Performance in the Emergency Department: Prospective Interventional Study.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Examining the Role of AI in Changing the Role of Nurses in Patient Care: Systematic Review.JMIR nursing · 2025Pooled it
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Pooled it
- Applications of Artificial Intelligence in Nursing Care: A Systematic Review.Journal of nursing management · 2023Pooled it
- The Use of Ambient Dictation Artificial Intelligence in Clinical Spaces in Surgery: A Scoping Review.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Development and Deployment of DeepSeek-Based Applications in Healthcare: A Chinese Perspective.Health care science · 2026Article
- Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review.Journal of evaluation in clinical practice · 2026Article
- Review
- Clinical Impact of Artificial Intelligence-Based Triage Systems in Emergency Departments: A Systematic Review.Cureus · 2025Review
- Artificial intelligence for severity triage based on conversations in an emergency department in Korea.Scientific reports · 2025Article
- Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review.Nursing reports (Pavia, Italy) · 2025Review
- Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.BMC medical informatics and decision making · 2025Article
- Enhancing Prehospital Decision-Making: Exploring User Needs and Design Considerations for Clinical Decision Support Systems.Research square · 2024Article
- Artificial Intelligence in Medicine.The Ulster medical journal · 2024Article
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
- Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review.JMIR medical informatics · 2023Article
- Data integration using information and communication technology for emergency medical services and systems.Clinical and experimental emergency medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNatural 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.
objectiveIn 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.
methodsFrom 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.
resultsThe 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.
conclusionsRMIS-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.
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