TY - JOUR
T1 - Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients with SARS-CoV-2 at a Smart Quarantine Station
T2 - Observational Study
AU - Liu, Ping Yen
AU - Tsai, Yi Shan
AU - Chen, Po Lin
AU - Tsai, Huey Pin
AU - Hsu, Ling Wei
AU - Wang, Chi Shiang
AU - Lee, Nan Yao
AU - Huang, Mu Shiang
AU - Wu, Yun Chiao
AU - Ko, Wen Chien
AU - Yang, Yi Ching
AU - Chiang, Jung Hsien
AU - Shen, Meng Ru
N1 - Funding Information:
This study was supported by grants 109-2745-B-006-003, 109-2327-B-006-005,109-2634-F-006-023, and 108-2314-B-006-098-MY3 from the Ministry of Science and Technology of Taiwan and grants D108-G2512 D109-G4803, D109-G4804, and D109-G2512 from Higher Education Sprout Project, Ministry of Education, to the Headquarters of University Advancement at National Cheng Kung University. We would like to thank all the NCKUH faculty members who participated in the quarantine station during this emerging infectious disease pandemic. We would also like to thank our technicians Chun-Sheng Yeh, I-Ting Lin, and Li-Rong Wang for their kind technical support and suggestions for the ex vivo study. Finally, we would like to thank Prof Michael W Hughes for his help with this English edition. We are grateful to Prof Pei-Fan Su for discussing and providing statistical consulting services from the Center for Quantitative Sciences, Clinical Medicine Research Center, NCKUH.
Publisher Copyright:
©Ping-Yen Liu, Yi-Shan Tsai, Po-Lin Chen, Huey-Pin Tsai, Ling-Wei Hsu, Chi-Shiang Wang, Nan-Yao Lee, Mu-Shiang Huang, Yun-Chiao Wu, Wen-Chien Ko, Yi-Ching Yang, Jung-Hsien Chiang, Meng-Ru Shen.
PY - 2020/10
Y1 - 2020/10
N2 - Background: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. Objective: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. Methods: This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. Results: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). Conclusions: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.
AB - Background: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. Objective: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. Methods: This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. Results: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). Conclusions: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.
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U2 - 10.2196/19878
DO - 10.2196/19878
M3 - Article
C2 - 33001832
AN - SCOPUS:85092945532
SN - 1439-4456
VL - 22
JO - Journal of medical Internet research
JF - Journal of medical Internet research
IS - 10
M1 - e19878
ER -