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Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System
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. The objective of our study was to develop a precise and subdividable outpa...
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| Published in: | Journal of medical Internet research 2024-10, Vol.26 (3), p.e51711 |
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| creator | Yang, Lingrui Pang, Jiali Zuo, Song Xu, Jian Jin, Wei Zuo, Feng Xue, Kui Xiao, Zhongzhou Peng, Xinwei Xu, Jie Zhang, Xiaofan Chen, Ruiyao Luo, Shuqing Zhang, Shaoting Sun, Xin |
| description | 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.
The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care.
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.
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 |
| doi_str_mv | 10.2196/51711 |
| format | article |
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The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care.
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.
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).
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.</description><identifier>ISSN: 1438-8871</identifier><identifier>ISSN: 1439-4456</identifier><identifier>EISSN: 1438-8871</identifier><identifier>DOI: 10.2196/51711</identifier><identifier>PMID: 39476375</identifier><language>eng</language><publisher>Canada: Journal of Medical Internet Research</publisher><subject>Artificial Intelligence ; China ; Electronic Health Records ; Hospitals ; Humans ; Internet ; Medical care ; Medical colleges ; Medical records ; Nurses ; Original Paper ; Outpatient services ; Outpatients - statistics & numerical data ; Patient satisfaction ; Prospective Studies ; Quality management ; Technology application ; Triage (Medicine) ; Triage - methods</subject><ispartof>Journal of medical Internet research, 2024-10, Vol.26 (3), p.e51711</ispartof><rights>Lingrui Yang, Jiali Pang, Song Zuo, Jian Xu, Wei Jin, Feng Zuo, Kui Xue, Zhongzhou Xiao, Xinwei Peng, Jie Xu, Xiaofan Zhang, Ruiyao Chen, Shuqing Luo, Shaoting Zhang, Xin Sun. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024.</rights><rights>COPYRIGHT 2024 Journal of Medical Internet Research</rights><rights>Lingrui Yang, Jiali Pang, Song Zuo, Jian Xu, Wei Jin, Feng Zuo, Kui Xue, Zhongzhou Xiao, Xinwei Peng, Jie Xu, Xiaofan Zhang, Ruiyao Chen, Shuqing Luo, Shaoting Zhang, Xin Sun. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3096-e94fb9d4758aa0f5497a54b9cf344f11455a7340ba342f43080b5533b880eeb83</cites><orcidid>0009-0005-4902-1034 ; 0009-0006-9663-7285 ; 0000-0002-2011-5556 ; 0000-0003-1582-3221 ; 0009-0003-7223-5298 ; 0000-0001-5757-4804 ; 0000-0002-8719-448X ; 0009-0005-4262-692X ; 0000-0001-9868-0136 ; 0000-0001-9233-4363 ; 0000-0003-3999-1449 ; 0000-0003-4872-3315 ; 0009-0009-9601-5349 ; 0009-0003-0813-7626 ; 0000-0001-8423-1356</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,723,776,780,881,27901,27902,33589,33884,36990</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39476375$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Lingrui</creatorcontrib><creatorcontrib>Pang, Jiali</creatorcontrib><creatorcontrib>Zuo, Song</creatorcontrib><creatorcontrib>Xu, Jian</creatorcontrib><creatorcontrib>Jin, Wei</creatorcontrib><creatorcontrib>Zuo, Feng</creatorcontrib><creatorcontrib>Xue, Kui</creatorcontrib><creatorcontrib>Xiao, Zhongzhou</creatorcontrib><creatorcontrib>Peng, Xinwei</creatorcontrib><creatorcontrib>Xu, Jie</creatorcontrib><creatorcontrib>Zhang, Xiaofan</creatorcontrib><creatorcontrib>Chen, Ruiyao</creatorcontrib><creatorcontrib>Luo, Shuqing</creatorcontrib><creatorcontrib>Zhang, Shaoting</creatorcontrib><creatorcontrib>Sun, Xin</creatorcontrib><title>Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System</title><title>Journal of medical Internet research</title><addtitle>J Med Internet Res</addtitle><description>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.
The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care.
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.
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).
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.</description><subject>Artificial Intelligence</subject><subject>China</subject><subject>Electronic Health Records</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Internet</subject><subject>Medical care</subject><subject>Medical colleges</subject><subject>Medical records</subject><subject>Nurses</subject><subject>Original Paper</subject><subject>Outpatient services</subject><subject>Outpatients - statistics & numerical data</subject><subject>Patient satisfaction</subject><subject>Prospective Studies</subject><subject>Quality management</subject><subject>Technology application</subject><subject>Triage (Medicine)</subject><subject>Triage - methods</subject><issn>1438-8871</issn><issn>1439-4456</issn><issn>1438-8871</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNptkt1uFCEUgCdGY2vtKxhSY6IXW2GA-fHGbNbVblKtSes1gZnDLA0D48Bs3LfwkWW7bdNNDBf8feeDAyfLTgk-z0ldfOSkJORZdkwYrWZVVZLnT8ZH2asQbjHOMavJy-yI1qwsaMmPs7_LjbdTNN4hr1FcAzpbuQijg4h-2imgC5A2rtFCjnCGvvsW0NJJZaFFaovmYzTaNEZatIuy1nTgGviEvsAGrB96cBFJ16L5MFjTyIdzpENXUxzSfAfcjEZ2gK63IUL_OnuhpQ1wet-fZL--Lm8WF7PLq2-rxfxy1lBcFzOomVZ1y0peSYk1Z3UpOVN1oyljmhDGuSwpw0pSlmtGcYUV55SqqsIAqqIn2Wrvbb28FcNoejluhZdG3C34sRMyZddYEEwzSBIFSnEmQdUVNGWVlDkDkJQm1-e9a5hUD22TkhqlPZAe7jizFp3fCEJ4kT6pSIb394bR_54gRNGb0KQHlQ78FAQleZ4-rCR5Qt_u0U6muxmnfVI2O1zMK8JyXlDME3X-Hyq1FnrTeAfapPWDgA8HAYmJ8Cd2cgpBrK5_HLLv9mwz-hBG0I-pEix21SjuqjFxb56-yyP1UH70H3eF19Y</recordid><startdate>20241030</startdate><enddate>20241030</enddate><creator>Yang, Lingrui</creator><creator>Pang, Jiali</creator><creator>Zuo, Song</creator><creator>Xu, Jian</creator><creator>Jin, Wei</creator><creator>Zuo, Feng</creator><creator>Xue, Kui</creator><creator>Xiao, Zhongzhou</creator><creator>Peng, Xinwei</creator><creator>Xu, Jie</creator><creator>Zhang, Xiaofan</creator><creator>Chen, Ruiyao</creator><creator>Luo, Shuqing</creator><creator>Zhang, Shaoting</creator><creator>Sun, Xin</creator><general>Journal of Medical Internet Research</general><general>JMIR Publications</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0005-4902-1034</orcidid><orcidid>https://orcid.org/0009-0006-9663-7285</orcidid><orcidid>https://orcid.org/0000-0002-2011-5556</orcidid><orcidid>https://orcid.org/0000-0003-1582-3221</orcidid><orcidid>https://orcid.org/0009-0003-7223-5298</orcidid><orcidid>https://orcid.org/0000-0001-5757-4804</orcidid><orcidid>https://orcid.org/0000-0002-8719-448X</orcidid><orcidid>https://orcid.org/0009-0005-4262-692X</orcidid><orcidid>https://orcid.org/0000-0001-9868-0136</orcidid><orcidid>https://orcid.org/0000-0001-9233-4363</orcidid><orcidid>https://orcid.org/0000-0003-3999-1449</orcidid><orcidid>https://orcid.org/0000-0003-4872-3315</orcidid><orcidid>https://orcid.org/0009-0009-9601-5349</orcidid><orcidid>https://orcid.org/0009-0003-0813-7626</orcidid><orcidid>https://orcid.org/0000-0001-8423-1356</orcidid></search><sort><creationdate>20241030</creationdate><title>Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System</title><author>Yang, Lingrui ; Pang, Jiali ; Zuo, Song ; Xu, Jian ; Jin, Wei ; Zuo, Feng ; Xue, Kui ; Xiao, Zhongzhou ; Peng, Xinwei ; Xu, Jie ; Zhang, Xiaofan ; Chen, Ruiyao ; Luo, Shuqing ; Zhang, Shaoting ; Sun, Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3096-e94fb9d4758aa0f5497a54b9cf344f11455a7340ba342f43080b5533b880eeb83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial Intelligence</topic><topic>China</topic><topic>Electronic Health Records</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Internet</topic><topic>Medical care</topic><topic>Medical colleges</topic><topic>Medical records</topic><topic>Nurses</topic><topic>Original Paper</topic><topic>Outpatient services</topic><topic>Outpatients - statistics & numerical data</topic><topic>Patient satisfaction</topic><topic>Prospective Studies</topic><topic>Quality management</topic><topic>Technology application</topic><topic>Triage (Medicine)</topic><topic>Triage - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Lingrui</creatorcontrib><creatorcontrib>Pang, Jiali</creatorcontrib><creatorcontrib>Zuo, Song</creatorcontrib><creatorcontrib>Xu, Jian</creatorcontrib><creatorcontrib>Jin, Wei</creatorcontrib><creatorcontrib>Zuo, Feng</creatorcontrib><creatorcontrib>Xue, Kui</creatorcontrib><creatorcontrib>Xiao, Zhongzhou</creatorcontrib><creatorcontrib>Peng, Xinwei</creatorcontrib><creatorcontrib>Xu, Jie</creatorcontrib><creatorcontrib>Zhang, Xiaofan</creatorcontrib><creatorcontrib>Chen, Ruiyao</creatorcontrib><creatorcontrib>Luo, Shuqing</creatorcontrib><creatorcontrib>Zhang, Shaoting</creatorcontrib><creatorcontrib>Sun, Xin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of medical Internet research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Lingrui</au><au>Pang, Jiali</au><au>Zuo, Song</au><au>Xu, Jian</au><au>Jin, Wei</au><au>Zuo, Feng</au><au>Xue, Kui</au><au>Xiao, Zhongzhou</au><au>Peng, Xinwei</au><au>Xu, Jie</au><au>Zhang, Xiaofan</au><au>Chen, Ruiyao</au><au>Luo, Shuqing</au><au>Zhang, Shaoting</au><au>Sun, Xin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System</atitle><jtitle>Journal of medical Internet research</jtitle><addtitle>J Med Internet Res</addtitle><date>2024-10-30</date><risdate>2024</risdate><volume>26</volume><issue>3</issue><spage>e51711</spage><pages>e51711-</pages><issn>1438-8871</issn><issn>1439-4456</issn><eissn>1438-8871</eissn><abstract>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.
The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care.
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.
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).
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.</abstract><cop>Canada</cop><pub>Journal of Medical Internet Research</pub><pmid>39476375</pmid><doi>10.2196/51711</doi><orcidid>https://orcid.org/0009-0005-4902-1034</orcidid><orcidid>https://orcid.org/0009-0006-9663-7285</orcidid><orcidid>https://orcid.org/0000-0002-2011-5556</orcidid><orcidid>https://orcid.org/0000-0003-1582-3221</orcidid><orcidid>https://orcid.org/0009-0003-7223-5298</orcidid><orcidid>https://orcid.org/0000-0001-5757-4804</orcidid><orcidid>https://orcid.org/0000-0002-8719-448X</orcidid><orcidid>https://orcid.org/0009-0005-4262-692X</orcidid><orcidid>https://orcid.org/0000-0001-9868-0136</orcidid><orcidid>https://orcid.org/0000-0001-9233-4363</orcidid><orcidid>https://orcid.org/0000-0003-3999-1449</orcidid><orcidid>https://orcid.org/0000-0003-4872-3315</orcidid><orcidid>https://orcid.org/0009-0009-9601-5349</orcidid><orcidid>https://orcid.org/0009-0003-0813-7626</orcidid><orcidid>https://orcid.org/0000-0001-8423-1356</orcidid><oa>free_for_read</oa></addata></record> |
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| source | PubMed (Medline); Publicly Available Content Database; Social Science Premium Collection; Library & Information Science Collection |
| subjects | Artificial Intelligence China Electronic Health Records Hospitals Humans Internet Medical care Medical colleges Medical records Nurses Original Paper Outpatient services Outpatients - statistics & numerical data Patient satisfaction Prospective Studies Quality management Technology application Triage (Medicine) Triage - methods |
| title | Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System |
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