The Experts below are selected from a list of 1038 Experts worldwide ranked by ideXlab platform
Junghsien Chiang - One of the best experts on this subject based on the ideXlab platform.
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application of an artificial intelligence trilogy to accelerate processing of suspected patients with sars cov 2 at a smart Quarantine Station observational study
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang Huang, Yi Ching Yang, Junghsien ChiangAbstract:BACKGROUND: As the coronavirus disease (COVID-19) epidemic worsens, the burden of Quarantine Stations (Q Stations) outside of emergency rooms (ERs) at every hospital increases daily To prepare for the screening workload inside Q Stations, all staff with medical licenses are required to support the working shift Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary OBJECTIVE: To demonstrate how the NCKUH AI trilogy of smart Q Station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces Quarantine processing time METHODS: This observational study on the emerging COVID-19 pandemic included constitutively 643 patients The artificial intelligence (AI) trilogy, i e , 1) smart Q Station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the Quarantine survey and reduce processing time during the COVID-19 pandemic RESULTS: The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a 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 dataset The detection rates were 93 2% and 45 5% in posteroanterior and anteroposterior chest X-rays, respectively The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively To further analyze the effect of the AI application in the Q Station, we subdivided the Q Station into with or without AI groups Compared with the conventional ER track (n=281), the survey time at the clinical Q Station (n=1520) was significantly shortened [median survey time (95% confidence interval;CI) at the ER: 153 (108 5-205) min vs at the clinical Q Station: 35 (24-56) min;p<0 0001] Furthermore, the use of the AI application in the Q Station reduced the survey time in the Q Station [median survey time (95% CI) without AI: 100 5 (40 3-152 5) min vs with AI in the Q Station: 34 (24-53) min;p<0 0001] CONCLUSIONS: The AI trilogy improves medical care workflow safely by shortening the Quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease
Ping-yen Liu - One of the best experts on this subject based on the ideXlab platform.
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application of an artificial intelligence trilogy to accelerate processing of suspected patients with sars cov 2 at a smart Quarantine Station observational study
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang Huang, Yi Ching Yang, Junghsien ChiangAbstract:BACKGROUND: As the coronavirus disease (COVID-19) epidemic worsens, the burden of Quarantine Stations (Q Stations) outside of emergency rooms (ERs) at every hospital increases daily To prepare for the screening workload inside Q Stations, all staff with medical licenses are required to support the working shift Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary OBJECTIVE: To demonstrate how the NCKUH AI trilogy of smart Q Station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces Quarantine processing time METHODS: This observational study on the emerging COVID-19 pandemic included constitutively 643 patients The artificial intelligence (AI) trilogy, i e , 1) smart Q Station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the Quarantine survey and reduce processing time during the COVID-19 pandemic RESULTS: The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a 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 dataset The detection rates were 93 2% and 45 5% in posteroanterior and anteroposterior chest X-rays, respectively The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively To further analyze the effect of the AI application in the Q Station, we subdivided the Q Station into with or without AI groups Compared with the conventional ER track (n=281), the survey time at the clinical Q Station (n=1520) was significantly shortened [median survey time (95% confidence interval;CI) at the ER: 153 (108 5-205) min vs at the clinical Q Station: 35 (24-56) min;p<0 0001] Furthermore, the use of the AI application in the Q Station reduced the survey time in the Q Station [median survey time (95% CI) without AI: 100 5 (40 3-152 5) min vs with AI in the Q Station: 34 (24-53) min;p<0 0001] CONCLUSIONS: The AI trilogy improves medical care workflow safely by shortening the Quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study (Preprint)
2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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; <i>P</i><.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 (<i>P</i><.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.
Mu-shiang Huang - One of the best experts on this subject based on the ideXlab platform.
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application of an artificial intelligence trilogy to accelerate processing of suspected patients with sars cov 2 at a smart Quarantine Station observational study
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang Huang, Yi Ching Yang, Junghsien ChiangAbstract:BACKGROUND: As the coronavirus disease (COVID-19) epidemic worsens, the burden of Quarantine Stations (Q Stations) outside of emergency rooms (ERs) at every hospital increases daily To prepare for the screening workload inside Q Stations, all staff with medical licenses are required to support the working shift Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary OBJECTIVE: To demonstrate how the NCKUH AI trilogy of smart Q Station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces Quarantine processing time METHODS: This observational study on the emerging COVID-19 pandemic included constitutively 643 patients The artificial intelligence (AI) trilogy, i e , 1) smart Q Station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the Quarantine survey and reduce processing time during the COVID-19 pandemic RESULTS: The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a 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 dataset The detection rates were 93 2% and 45 5% in posteroanterior and anteroposterior chest X-rays, respectively The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively To further analyze the effect of the AI application in the Q Station, we subdivided the Q Station into with or without AI groups Compared with the conventional ER track (n=281), the survey time at the clinical Q Station (n=1520) was significantly shortened [median survey time (95% confidence interval;CI) at the ER: 153 (108 5-205) min vs at the clinical Q Station: 35 (24-56) min;p<0 0001] Furthermore, the use of the AI application in the Q Station reduced the survey time in the Q Station [median survey time (95% CI) without AI: 100 5 (40 3-152 5) min vs with AI in the Q Station: 34 (24-53) min;p<0 0001] CONCLUSIONS: The AI trilogy improves medical care workflow safely by shortening the Quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study (Preprint)
2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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; <i>P</i><.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 (<i>P</i><.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.
Peta Longhurst - One of the best experts on this subject based on the ideXlab platform.
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contagious objects artefacts of disease transmission and control at north head Quarantine Station australia
World Archaeology, 2018Co-Authors: Peta LonghurstAbstract:ABSTRACTFrom 1828 to 1984, North Head Quarantine Station was the first port of call for many immigrants seeking a new life in Australia. The institution was intended to confine disease, via the bodies and objects that conveyed it, and prevent it from spreading throughout the Sydney populace. Despite being a public health institution, an initial functional analysis found that only a small subset of artefacts associated with the site were medical in nature. This article draws on the assemblage of North Head to consider how the material culture of Quarantine extends beyond medical instruments. By re-evaluating the assemblage through a disease-centred, ‘epidemiological’ lens, the author demonstrates how disease permeates throughout the Quarantine assemblage, enmeshing artefacts, bodies and contagions within a complex web of relations.
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Quarantine Matters: Colonial Quarantine at North Head, Sydney and Its Material and Ideological Ruins
International Journal of Historical Archaeology, 2016Co-Authors: Peta LonghurstAbstract:Australia’s Quarantine regulations have their roots in colonial practice. This paper is concerned with the “matter” of Quarantine—its location, spatialization, and materialization—and the ways in which it contributed to the colonial agenda. Through an exploration of Sydney’s North Head Quarantine Station, Quarantine is shown to be a technology through which the colony and the continent were framed as simultaneously pure and vulnerable. These colonial roots of Quarantine practice are then brought back to the present, drawing on Stoler’s ( 2008 ) concept of “imperial debris” to contemplate the contemporary ruins, both material and ideological, of colonial Quarantine practice.
Yi-shan Tsai - One of the best experts on this subject based on the ideXlab platform.
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application of an artificial intelligence trilogy to accelerate processing of suspected patients with sars cov 2 at a smart Quarantine Station observational study
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang Huang, Yi Ching Yang, Junghsien ChiangAbstract:BACKGROUND: As the coronavirus disease (COVID-19) epidemic worsens, the burden of Quarantine Stations (Q Stations) outside of emergency rooms (ERs) at every hospital increases daily To prepare for the screening workload inside Q Stations, all staff with medical licenses are required to support the working shift Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary OBJECTIVE: To demonstrate how the NCKUH AI trilogy of smart Q Station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces Quarantine processing time METHODS: This observational study on the emerging COVID-19 pandemic included constitutively 643 patients The artificial intelligence (AI) trilogy, i e , 1) smart Q Station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the Quarantine survey and reduce processing time during the COVID-19 pandemic RESULTS: The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a 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 dataset The detection rates were 93 2% and 45 5% in posteroanterior and anteroposterior chest X-rays, respectively The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively To further analyze the effect of the AI application in the Q Station, we subdivided the Q Station into with or without AI groups Compared with the conventional ER track (n=281), the survey time at the clinical Q Station (n=1520) was significantly shortened [median survey time (95% confidence interval;CI) at the ER: 153 (108 5-205) min vs at the clinical Q Station: 35 (24-56) min;p<0 0001] Furthermore, the use of the AI application in the Q Station reduced the survey time in the Q Station [median survey time (95% CI) without AI: 100 5 (40 3-152 5) min vs with AI in the Q Station: 34 (24-53) min;p<0 0001] CONCLUSIONS: The AI trilogy improves medical care workflow safely by shortening the Quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.
Journal of Medical Internet Research, 2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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
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Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study (Preprint)
2020Co-Authors: Ping-yen Liu, Yi-shan Tsai, Po-lin Chen, Huey-pin Tsai, Ling-wei Hsu, Chi-shiang Wang, Nan-yao Lee, Mu-shiang HuangAbstract: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; <i>P</i><.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 (<i>P</i><.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.