TY  - JOUR
AU  - Yang, Lingrui
AU  - Pang, Jiali
AU  - Zuo, Song
AU  - Xu, Jian
AU  - Jin, Wei
AU  - Zuo, Feng
AU  - Xue, Kui
AU  - Xiao, Zhongzhou
AU  - Peng, Xinwei
AU  - Xu, Jie
AU  - Zhang, Xiaofan
AU  - Chen, Ruiyao
AU  - Luo, Shuqing
AU  - Zhang, Shaoting
AU  - Sun, Xin
PY  - 2024
DA  - 2024/10/30
TI  - Evolution of the “Internet Plus Health Care” Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System
JO  - J Med Internet Res
SP  - e51711
VL  - 26
KW  - artificial intelligence
KW  - triage system
KW  - all department recommendation
KW  - subspecialty department recommendation
KW  - “internet plus healthcare”
KW  - “internet plus health care”
AB  - Background: Although new technologies have increased the efficiency and convenience of medical care, patients still struggle to identify specialized outpatient departments in Chinese tertiary hospitals due to a lack of medical knowledge. Objective: The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care. Methods: We collected 395,790 electronic medical records (EMRs) and 500 medical dialogue groups. The EMRs were divided into 3 data sets to design and train the triage model (n=387,876, 98%) and test (n=3957, 1%) and validate (n=3957, 1%) it. The triage system was altered based on the current BERT (Bidirectional Encoder Representations from Transformers) framework and evaluated by recommendation accuracies in Xinhua Hospital using the cancellation rates in 2021 and 2022, from October 29 to December 5. Finally, a prospective observational study containing 306 samples was conducted to compare the system’s performance with that of triage nurses, which was evaluated by calculating precision, accuracy, recall of the top 3 recommended departments (recall@3), and time consumption. Results: With 3957 (1%) records each, the testing and validation data sets achieved an accuracy of 0.8945 and 0.8941, respectively. Implemented in Xinhua Hospital, our triage system could accurately recommend 79 subspecialty departments and reduce the number of registration cancellations from 16,037 (3.83%) of the total 418,714 to 15,338 (3.53%) of the total 434200 (P<.05). In comparison to the triage system, the performance of the triage nurses was more accurate (0.9803 vs 0.9153) and precise (0.9213 vs 0.9049) since the system could identify subspecialty departments, whereas triage nurses or even general physicians can only recommend main departments. In addition, our triage system significantly outperformed triage nurses in recall@3 (0.6230 vs 0.5266; P<.001) and time consumption (10.11 vs 14.33 seconds; P<.001). Conclusions: The triage system demonstrates high accuracy in outpatient triage of all departments and excels in subspecialty department recommendations, which could decrease the cancellation rate and time consumption. It also improves the efficiency and convenience of clinical care to fulfill better the usage of medical resources, expand hospital effectiveness, and improve patient satisfaction in Chinese tertiary hospitals. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e51711
UR  - https://doi.org/10.2196/51711
DO  - 10.2196/51711
ID  - info:doi/10.2196/51711
ER  -