Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda

ObjectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.SettingA single, private, not-for profit hospital in rural, southwestern Uganda.ParticipantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the...

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Veröffentlicht in:BMJ open 2018-06, Vol.8 (6), p.e020188-e020188
Hauptverfasser: Rice, Brian Travis, Bisanzo, Mark, Maling, Samuel, Joseph, Ryan, Mowafi, Hani
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container_issue 6
container_start_page e020188
container_title BMJ open
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creator Rice, Brian Travis
Bisanzo, Mark
Maling, Samuel
Joseph, Ryan
Mowafi, Hani
description ObjectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.SettingA single, private, not-for profit hospital in rural, southwestern Uganda.ParticipantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the derivation dataset, and from 2015 to 2017, 10 068 visits produced 20 165 total chief complaints for the validation dataset.MethodsA retrospective review of an emergency centre quality assurance database was performed. Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen’s kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.ResultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.ConclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.
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Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen’s kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.ResultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.ConclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.</description><identifier>ISSN: 2044-6055</identifier><identifier>EISSN: 2044-6055</identifier><identifier>DOI: 10.1136/bmjopen-2017-020188</identifier><identifier>PMID: 29950461</identifier><language>eng</language><publisher>England: BMJ Publishing Group LTD</publisher><subject>Complaints ; Emergency medical care ; Emergency Medicine ; Emergency services ; Epidemiology ; Hospitals ; Injuries ; Medical research ; Mortality ; Natural language processing ; Patients ; Public health ; Quality control ; Trauma</subject><ispartof>BMJ open, 2018-06, Vol.8 (6), p.e020188-e020188</ispartof><rights>Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.</rights><rights>2018 Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b472t-97a58c60b0466b247c6bb6b815778373f334d2821e279dee28c2bdd0ebfe68803</citedby><cites>FETCH-LOGICAL-b472t-97a58c60b0466b247c6bb6b815778373f334d2821e279dee28c2bdd0ebfe68803</cites><orcidid>0000-0002-9093-1831</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://bmjopen.bmj.com/content/8/6/e020188.full.pdf$$EPDF$$P50$$Gbmj$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://bmjopen.bmj.com/content/8/6/e020188.full$$EHTML$$P50$$Gbmj$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27549,27550,27924,27925,53791,53793,77601,77632</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29950461$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rice, Brian Travis</creatorcontrib><creatorcontrib>Bisanzo, Mark</creatorcontrib><creatorcontrib>Maling, Samuel</creatorcontrib><creatorcontrib>Joseph, Ryan</creatorcontrib><creatorcontrib>Mowafi, Hani</creatorcontrib><creatorcontrib>Global Emergency Care Investigators Group (Study Group)</creatorcontrib><title>Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda</title><title>BMJ open</title><addtitle>BMJ Open</addtitle><description>ObjectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.SettingA single, private, not-for profit hospital in rural, southwestern Uganda.ParticipantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the derivation dataset, and from 2015 to 2017, 10 068 visits produced 20 165 total chief complaints for the validation dataset.MethodsA retrospective review of an emergency centre quality assurance database was performed. Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen’s kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.ResultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.ConclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.</description><subject>Complaints</subject><subject>Emergency medical care</subject><subject>Emergency Medicine</subject><subject>Emergency services</subject><subject>Epidemiology</subject><subject>Hospitals</subject><subject>Injuries</subject><subject>Medical research</subject><subject>Mortality</subject><subject>Natural language processing</subject><subject>Patients</subject><subject>Public health</subject><subject>Quality control</subject><subject>Trauma</subject><issn>2044-6055</issn><issn>2044-6055</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>9YT</sourceid><sourceid>ACMMV</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkUtr3DAUhUVpaEKaX1Aogm66cSrLem4KJU0fEOgmWQtJvp7R1Jamkj2Qf18lnoa0q2qh53cO9-og9KYll23biQ9u2qU9xIaSVjakzkq9QGeUMNYIwvnLZ_tTdFHKjtTBuOacvkKnVGtOmGjP0M_PkMPBziFFbGOPD3YM_XpMA7bYbwMM2KdpP9oQZ1y2Kc9jKDMeUsZLLH4L_TJCj61fZnj0gAnyBqK_x95mwCHiu029t6_RyWDHAhfH9Rzdfbm-vfrW3Pz4-v3q003jmKRzo6XlygviaoXCUSa9cE441XIpVSe7oetYTxVtgUrdA1Dlqet7Am4AoRTpztHH1Xe_uAl6D3HOdjT7HCab702ywfz9EsPWbNLBiPqRmulq8P5okNOvBcpsplA8jKONkJZiKBEtI1ywrqLv_kF3acmxtlcprhmVRKtKdSvlcyolw_BUTEvMQ57mmKd5yNOseVbV2-d9PGn-pFeByxWo6v9y_A2Ioa0t</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Rice, Brian Travis</creator><creator>Bisanzo, Mark</creator><creator>Maling, Samuel</creator><creator>Joseph, Ryan</creator><creator>Mowafi, Hani</creator><general>BMJ Publishing Group LTD</general><general>BMJ Publishing Group</general><scope>9YT</scope><scope>ACMMV</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BTHHO</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9093-1831</orcidid></search><sort><creationdate>20180601</creationdate><title>Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda</title><author>Rice, Brian Travis ; 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Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Publicly Available Content Database</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 China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMJ open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rice, Brian Travis</au><au>Bisanzo, Mark</au><au>Maling, Samuel</au><au>Joseph, Ryan</au><au>Mowafi, Hani</au><aucorp>Global Emergency Care Investigators Group (Study Group)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda</atitle><jtitle>BMJ open</jtitle><addtitle>BMJ Open</addtitle><date>2018-06-01</date><risdate>2018</risdate><volume>8</volume><issue>6</issue><spage>e020188</spage><epage>e020188</epage><pages>e020188-e020188</pages><issn>2044-6055</issn><eissn>2044-6055</eissn><abstract>ObjectivesDerive and validate a shortlist of chief complaints to describe unscheduled acute and emergency care in Uganda.SettingA single, private, not-for profit hospital in rural, southwestern Uganda.ParticipantsFrom 2009 to 2015, 26 996 patient visits produced 42 566 total chief complaints for the derivation dataset, and from 2015 to 2017, 10 068 visits produced 20 165 total chief complaints for the validation dataset.MethodsA retrospective review of an emergency centre quality assurance database was performed. Data were abstracted, cleaned and refined using language processing in Stata to produce a longlist of chief complaints, which was collapsed via a consensus process to produce a shortlist and turned into a web-based tool. This tool was used by two local Ugandan emergency care practitioners to categorise complaints from a second longlist produced from a separate validation dataset from the same study site. Their agreement on grouping was analysed using Cohen’s kappa to determine inter-rater reliability. The chief complaints describing 80% of patient visits from automated and consensus shortlists were combined to form a candidate chief complaint shortlist.ResultsAutomated data cleaning and refining recognised 95.8% of all complaints and produced a longlist of 555 chief complaints. The consensus process yielded a shortlist of 83 grouped chief complaints. The second validation dataset was reduced in Stata to a longlist of 451 complaints. Using the shortlist tool to categorise complaints produced 71.5% agreement, yielding a kappa of 0.70 showing substantial inter-rater reliability. Only one complaint did not fit into the shortlist and required a free-text amendment. The two shortlists were identical for the most common 14 complaints and combined to form a candidate list of 24 complaints that could characterise over 80% of all emergency centre chief complaints.ConclusionsShortlists of chief complaints can be generated to improve standardisation of data entry, facilitate research efforts and be employed for paper chart usage.</abstract><cop>England</cop><pub>BMJ Publishing Group LTD</pub><pmid>29950461</pmid><doi>10.1136/bmjopen-2017-020188</doi><orcidid>https://orcid.org/0000-0002-9093-1831</orcidid><oa>free_for_read</oa></addata></record>
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subjects Complaints
Emergency medical care
Emergency Medicine
Emergency services
Epidemiology
Hospitals
Injuries
Medical research
Mortality
Natural language processing
Patients
Public health
Quality control
Trauma
title Derivation and validation of a chief complaint shortlist for unscheduled acute and emergency care in Uganda
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