Loading…

Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis

PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service f...

Full description

Saved in:
Bibliographic Details
Published in:Internet research 2021-11, Vol.31 (6), p.2055-2075
Main Authors: Wan, Yan, Peng, Ziqing, Wang, Yalu, Zhang, Yifan, Gao, Jinping, Ma, Baojun
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73
cites cdi_FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73
container_end_page 2075
container_issue 6
container_start_page 2055
container_title Internet research
container_volume 31
creator Wan, Yan
Peng, Ziqing
Wang, Yalu
Zhang, Yifan
Gao, Jinping
Ma, Baojun
description PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.FindingsThe study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.Originality/valueThis research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.
doi_str_mv 10.1108/INTR-10-2020-0589
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1108_INTR_10_2020_0589</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2596182901</sourcerecordid><originalsourceid>FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73</originalsourceid><addsrcrecordid>eNptUU1LxDAQDaLguvoDvAU8VydJmzZHET8WREH0XMY0cbOkyZq0ir_BP23LehE8zcd7b2Z4Q8gpg3PGoLlYPTw_FQwKDhwKqBq1RxYcqrKoZFXvkwUDKQvOS3FIjnLeAABTqlyQ71WwfjRBu_BGLeohpkwxdLQ3eo3B5Z5GS7s4A1THkEc_4OBioB_Rj72hUxaDd8FMis5p9H9ZW4-DjanP9BWz6Wb6dv2VnXYYaDIfznxO69BPrXxMDiz6bE5-45K83Fw_X90V94-3q6vL-0ILyYdCgOQV75gyr5UoUTW14tBgbUrECbC1gU4IxRhKC6iYtFKI2tSiKSUvdS2W5Gw3d5vi-2jy0G7imKYjcssrJVnDFbCJxXYsnWLOydh2m1yP6atl0M6et7PnczF73s6eTxrYaUxvEvruX8mfN4kfmTWFgw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2596182901</pqid></control><display><type>article</type><title>Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis</title><source>Library &amp; Information Science Abstracts (LISA)</source><source>Social Science Premium Collection</source><source>ABI/INFORM Global</source><source>Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list)</source><source>Library &amp; Information Science Collection</source><source>Education Collection</source><creator>Wan, Yan ; Peng, Ziqing ; Wang, Yalu ; Zhang, Yifan ; Gao, Jinping ; Ma, Baojun</creator><creatorcontrib>Wan, Yan ; Peng, Ziqing ; Wang, Yalu ; Zhang, Yifan ; Gao, Jinping ; Ma, Baojun</creatorcontrib><description>PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.FindingsThe study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.Originality/valueThis research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.</description><identifier>ISSN: 1066-2243</identifier><identifier>EISSN: 2054-5657</identifier><identifier>DOI: 10.1108/INTR-10-2020-0589</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Algorithms ; Coronaviruses ; COVID-19 ; Data mining ; Disease ; Electronic commerce ; Feature extraction ; Internet ; Literature Reviews ; Medical research ; Medical Services ; Nouns ; Pandemics ; Patients ; Physicians ; Platforms ; Quality of service ; Regression models ; Research Problems ; Resource Allocation ; Search algorithms</subject><ispartof>Internet research, 2021-11, Vol.31 (6), p.2055-2075</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73</citedby><cites>FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73</cites><orcidid>0000-0002-8821-9925 ; 0000-0003-1404-2992 ; 0000-0002-2274-3089</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2596182901?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,777,781,11669,21359,21362,21375,27286,27905,27906,33592,33858,33887,34116,36041,43714,43861,43873,44344</link.rule.ids></links><search><creatorcontrib>Wan, Yan</creatorcontrib><creatorcontrib>Peng, Ziqing</creatorcontrib><creatorcontrib>Wang, Yalu</creatorcontrib><creatorcontrib>Zhang, Yifan</creatorcontrib><creatorcontrib>Gao, Jinping</creatorcontrib><creatorcontrib>Ma, Baojun</creatorcontrib><title>Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis</title><title>Internet research</title><description>PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.FindingsThe study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.Originality/valueThis research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.</description><subject>Algorithms</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Data mining</subject><subject>Disease</subject><subject>Electronic commerce</subject><subject>Feature extraction</subject><subject>Internet</subject><subject>Literature Reviews</subject><subject>Medical research</subject><subject>Medical Services</subject><subject>Nouns</subject><subject>Pandemics</subject><subject>Patients</subject><subject>Physicians</subject><subject>Platforms</subject><subject>Quality of service</subject><subject>Regression models</subject><subject>Research Problems</subject><subject>Resource Allocation</subject><subject>Search algorithms</subject><issn>1066-2243</issn><issn>2054-5657</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ALSLI</sourceid><sourceid>CJNVE</sourceid><sourceid>CNYFK</sourceid><sourceid>F2A</sourceid><sourceid>M0C</sourceid><sourceid>M0P</sourceid><sourceid>M1O</sourceid><recordid>eNptUU1LxDAQDaLguvoDvAU8VydJmzZHET8WREH0XMY0cbOkyZq0ir_BP23LehE8zcd7b2Z4Q8gpg3PGoLlYPTw_FQwKDhwKqBq1RxYcqrKoZFXvkwUDKQvOS3FIjnLeAABTqlyQ71WwfjRBu_BGLeohpkwxdLQ3eo3B5Z5GS7s4A1THkEc_4OBioB_Rj72hUxaDd8FMis5p9H9ZW4-DjanP9BWz6Wb6dv2VnXYYaDIfznxO69BPrXxMDiz6bE5-45K83Fw_X90V94-3q6vL-0ILyYdCgOQV75gyr5UoUTW14tBgbUrECbC1gU4IxRhKC6iYtFKI2tSiKSUvdS2W5Gw3d5vi-2jy0G7imKYjcssrJVnDFbCJxXYsnWLOydh2m1yP6atl0M6et7PnczF73s6eTxrYaUxvEvruX8mfN4kfmTWFgw</recordid><startdate>20211112</startdate><enddate>20211112</enddate><creator>Wan, Yan</creator><creator>Peng, Ziqing</creator><creator>Wang, Yalu</creator><creator>Zhang, Yifan</creator><creator>Gao, Jinping</creator><creator>Ma, Baojun</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>0U~</scope><scope>1-H</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CJNVE</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L.0</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M0P</scope><scope>M1O</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQEDU</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-8821-9925</orcidid><orcidid>https://orcid.org/0000-0003-1404-2992</orcidid><orcidid>https://orcid.org/0000-0002-2274-3089</orcidid></search><sort><creationdate>20211112</creationdate><title>Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis</title><author>Wan, Yan ; Peng, Ziqing ; Wang, Yalu ; Zhang, Yifan ; Gao, Jinping ; Ma, Baojun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Data mining</topic><topic>Disease</topic><topic>Electronic commerce</topic><topic>Feature extraction</topic><topic>Internet</topic><topic>Literature Reviews</topic><topic>Medical research</topic><topic>Medical Services</topic><topic>Nouns</topic><topic>Pandemics</topic><topic>Patients</topic><topic>Physicians</topic><topic>Platforms</topic><topic>Quality of service</topic><topic>Regression models</topic><topic>Research Problems</topic><topic>Resource Allocation</topic><topic>Search algorithms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wan, Yan</creatorcontrib><creatorcontrib>Peng, Ziqing</creatorcontrib><creatorcontrib>Wang, Yalu</creatorcontrib><creatorcontrib>Zhang, Yifan</creatorcontrib><creatorcontrib>Gao, Jinping</creatorcontrib><creatorcontrib>Ma, Baojun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection【Remote access available】</collection><collection>Global News &amp; ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Education Collection</collection><collection>Library &amp; Information Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Library &amp; Information Sciences Abstracts (LISA)</collection><collection>Library &amp; Information Science Abstracts (LISA)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Education Database</collection><collection>Library Science Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Education</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Internet research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wan, Yan</au><au>Peng, Ziqing</au><au>Wang, Yalu</au><au>Zhang, Yifan</au><au>Gao, Jinping</au><au>Ma, Baojun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis</atitle><jtitle>Internet research</jtitle><date>2021-11-12</date><risdate>2021</risdate><volume>31</volume><issue>6</issue><spage>2055</spage><epage>2075</epage><pages>2055-2075</pages><issn>1066-2243</issn><eissn>2054-5657</eissn><abstract>PurposeThis paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.Design/methodology/approachIn Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.FindingsThe study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.Originality/valueThis research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/INTR-10-2020-0589</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-8821-9925</orcidid><orcidid>https://orcid.org/0000-0003-1404-2992</orcidid><orcidid>https://orcid.org/0000-0002-2274-3089</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 1066-2243
ispartof Internet research, 2021-11, Vol.31 (6), p.2055-2075
issn 1066-2243
2054-5657
language eng
recordid cdi_crossref_primary_10_1108_INTR_10_2020_0589
source Library & Information Science Abstracts (LISA); Social Science Premium Collection; ABI/INFORM Global; Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list); Library & Information Science Collection; Education Collection
subjects Algorithms
Coronaviruses
COVID-19
Data mining
Disease
Electronic commerce
Feature extraction
Internet
Literature Reviews
Medical research
Medical Services
Nouns
Pandemics
Patients
Physicians
Platforms
Quality of service
Regression models
Research Problems
Resource Allocation
Search algorithms
title Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T18%3A17%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Influencing%20factors%20and%20mechanism%20of%20doctor%20consultation%20volume%20on%20online%20medical%20consultation%20platforms%20based%20on%20physician%20review%20analysis&rft.jtitle=Internet%20research&rft.au=Wan,%20Yan&rft.date=2021-11-12&rft.volume=31&rft.issue=6&rft.spage=2055&rft.epage=2075&rft.pages=2055-2075&rft.issn=1066-2243&rft.eissn=2054-5657&rft_id=info:doi/10.1108/INTR-10-2020-0589&rft_dat=%3Cproquest_cross%3E2596182901%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c362t-306252d19eb534a9879208a7e4aa252f7e0d33911a6f0a916f6337e7384624c73%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2596182901&rft_id=info:pmid/&rfr_iscdi=true