Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield
The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED). A retrospective...
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Veröffentlicht in: | The American journal of emergency medicine 2021-08, Vol.46, p.160-164 |
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description | The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED).
A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.
The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4–51.9) vs. 45.1% (95% CI 41.8–48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4–53.8) vs. 50.5% (95% CI 45.6–55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94–1.38).
For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization. |
doi_str_mv | 10.1016/j.ajem.2020.05.119 |
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A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.
The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4–51.9) vs. 45.1% (95% CI 41.8–48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4–53.8) vs. 50.5% (95% CI 45.6–55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94–1.38).
For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization.</description><identifier>ISSN: 0735-6757</identifier><identifier>EISSN: 1532-8171</identifier><identifier>DOI: 10.1016/j.ajem.2020.05.119</identifier><identifier>PMID: 33071089</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Abdomen ; Abdominal CT ; Abdominal pain ; Acuity ; Computed tomography ; Emergency medical care ; Gender ; Length of stay ; Pain ; Patients ; Physician ; Split-flow ; Triage ; Utilization</subject><ispartof>The American journal of emergency medicine, 2021-08, Vol.46, p.160-164</ispartof><rights>2020 Elsevier Inc.</rights><rights>Copyright © 2020 Elsevier Inc. All rights reserved.</rights><rights>2020. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c434t-b56e0936308af138606100f9b4a1a3eb521811b88c0f58d8a29d2fc3b90919e93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2555958423?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,3549,27923,27924,45994,64384,64386,64388,72340</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33071089$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Patterson, Brian W.</creatorcontrib><creatorcontrib>Johnson, Jordan</creatorcontrib><creatorcontrib>Ward, Michael A.</creatorcontrib><creatorcontrib>Hamedani, Azita G.</creatorcontrib><creatorcontrib>Sharp, Brian</creatorcontrib><title>Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield</title><title>The American journal of emergency medicine</title><addtitle>Am J Emerg Med</addtitle><description>The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED).
A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.
The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4–51.9) vs. 45.1% (95% CI 41.8–48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4–53.8) vs. 50.5% (95% CI 45.6–55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94–1.38).
For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization.</description><subject>Abdomen</subject><subject>Abdominal CT</subject><subject>Abdominal pain</subject><subject>Acuity</subject><subject>Computed tomography</subject><subject>Emergency medical care</subject><subject>Gender</subject><subject>Length of stay</subject><subject>Pain</subject><subject>Patients</subject><subject>Physician</subject><subject>Split-flow</subject><subject>Triage</subject><subject>Utilization</subject><issn>0735-6757</issn><issn>1532-8171</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc-L1DAUgIMo7rj6D3iQgBcvrS9J0yYgggzrD1jwsoK3kCavsyltMyadlfnvzTDroh485ZDvfSTvI-Qlg5oBa9-OtR1xrjlwqEHWjOlHZMOk4JViHXtMNtAJWbWd7C7Is5xHAMYa2TwlF0JAx0DpDfl-NQzoVhoHamneT2Gthin-pPvbYw4u2IWGha4p2B3SOXqcaFyo7X2cw2Inur2hMXlMYdnRZFekdvH0GHDyz8mTwU4ZX9yfl-Tbx6ub7efq-uunL9sP15VrRLNWvWwRtGgFKDswoVpoGcCg-8YyK7CXnCnGeqUcDFJ5Zbn2fHCi16CZRi0uyfuzd3_oZ_QOlzXZyexTmG06mmiD-ftmCbdmF-9M10kt264I3twLUvxxwLyaOWSH02QXjIdseCM5aKkkFPT1P-gYD6nsoVBSFp1quCgUP1MuxZwTDg-PYWBO4cxoTuHMKZwBaUq4MvTqz288jPwuVYB3ZwDLMu8CJpNdwMWhD6kEND6G__l_AY4EqMs</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Patterson, Brian W.</creator><creator>Johnson, Jordan</creator><creator>Ward, Michael A.</creator><creator>Hamedani, Azita G.</creator><creator>Sharp, Brian</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7T5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20210801</creationdate><title>Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield</title><author>Patterson, Brian W. ; Johnson, Jordan ; Ward, Michael A. ; Hamedani, Azita G. ; Sharp, Brian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-b56e0936308af138606100f9b4a1a3eb521811b88c0f58d8a29d2fc3b90919e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abdomen</topic><topic>Abdominal CT</topic><topic>Abdominal pain</topic><topic>Acuity</topic><topic>Computed tomography</topic><topic>Emergency medical care</topic><topic>Gender</topic><topic>Length of stay</topic><topic>Pain</topic><topic>Patients</topic><topic>Physician</topic><topic>Split-flow</topic><topic>Triage</topic><topic>Utilization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Patterson, Brian W.</creatorcontrib><creatorcontrib>Johnson, Jordan</creatorcontrib><creatorcontrib>Ward, Michael A.</creatorcontrib><creatorcontrib>Hamedani, Azita G.</creatorcontrib><creatorcontrib>Sharp, Brian</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Immunology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</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><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The American journal of emergency medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Patterson, Brian W.</au><au>Johnson, Jordan</au><au>Ward, Michael A.</au><au>Hamedani, Azita G.</au><au>Sharp, Brian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield</atitle><jtitle>The American journal of emergency medicine</jtitle><addtitle>Am J Emerg Med</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>46</volume><spage>160</spage><epage>164</epage><pages>160-164</pages><issn>0735-6757</issn><eissn>1532-8171</eissn><abstract>The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED).
A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.
The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4–51.9) vs. 45.1% (95% CI 41.8–48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4–53.8) vs. 50.5% (95% CI 45.6–55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94–1.38).
For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>33071089</pmid><doi>10.1016/j.ajem.2020.05.119</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Abdomen Abdominal CT Abdominal pain Acuity Computed tomography Emergency medical care Gender Length of stay Pain Patients Physician Split-flow Triage Utilization |
title | Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield |
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