Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity

Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity

Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Emergency radiology 2024-12
Hauptverfasser: McMenamy, John, Kochkine, Sergey, Bernstein, Mark, Lucero, Anthony, Miles, Randy, Schwertner, Adam, Thaker, Ashesh, Naeger, David M
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title Emergency radiology
container_volume
creator McMenamy, John
Kochkine, Sergey
Bernstein, Mark
Lucero, Anthony
Miles, Randy
Schwertner, Adam
Thaker, Ashesh
Naeger, David M
description Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level. Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients. 6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P 
doi_str_mv 10.1007/s10140-024-02297-z
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmed_primary_39661269</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3146629460</sourcerecordid><originalsourceid>FETCH-LOGICAL-p141t-efe20695af3c47085fa8cd3bc32ab63bc9f58e1fd3b5b393eddf2f47b39d69583</originalsourceid><addsrcrecordid>eNpNUE1PwzAMjRCIjcEf4IBy5BJImjRtjmjiS5q0yzhXaeJAoF9rUqbx6wliSBzs92Q_P9lG6JLRG0ZpcRsYZYISmokUmSrI1xGaM8FLklJ-_I_P0FkI75RSqWR5imZcSckyqeZou3aOmL4LfQNYT7FvdQSL9Ri988brBvsuQtP4V-gMYOjedEKLd_344Zp-lyq6biBg3w5j_5k60ML4I95jC0PyaaGLeLnBRg_a-Lg_RydONwEuDrhALw_3m-UTWa0fn5d3KzIwwSIBB1laN9eOG1HQMne6NJbXhme6lgmVy0tgLpXymisO1rrMiSJxm8ZKvkDXv75pr-0EIVatDyadojvop1BxJqTMlJA0Sa8O0qluwVbD6Fs97qu_N_Fvik1t2Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3146629460</pqid></control><display><type>article</type><title>Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity</title><source>SpringerLink Journals - AutoHoldings</source><creator>McMenamy, John ; Kochkine, Sergey ; Bernstein, Mark ; Lucero, Anthony ; Miles, Randy ; Schwertner, Adam ; Thaker, Ashesh ; Naeger, David M</creator><creatorcontrib>McMenamy, John ; Kochkine, Sergey ; Bernstein, Mark ; Lucero, Anthony ; Miles, Randy ; Schwertner, Adam ; Thaker, Ashesh ; Naeger, David M</creatorcontrib><description>Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level. Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients. 6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P &lt; 0.001). 4 subgroups demonstrated 56-75% decreases in median CT occupied time (P &lt; 0.001) and decreases in variability in ¾ subgroups. This off-console workflow enables increased CT capacity to meet rising ED demand. Similar improvements could be expected across most exam sets and imaging settings if broadly implemented.</description><identifier>ISSN: 1438-1435</identifier><identifier>EISSN: 1438-1435</identifier><identifier>DOI: 10.1007/s10140-024-02297-z</identifier><identifier>PMID: 39661269</identifier><language>eng</language><publisher>United States</publisher><ispartof>Emergency radiology, 2024-12</ispartof><rights>2024. The Author(s), under exclusive licence to American Society of Emergency Radiology (ASER).</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39661269$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McMenamy, John</creatorcontrib><creatorcontrib>Kochkine, Sergey</creatorcontrib><creatorcontrib>Bernstein, Mark</creatorcontrib><creatorcontrib>Lucero, Anthony</creatorcontrib><creatorcontrib>Miles, Randy</creatorcontrib><creatorcontrib>Schwertner, Adam</creatorcontrib><creatorcontrib>Thaker, Ashesh</creatorcontrib><creatorcontrib>Naeger, David M</creatorcontrib><title>Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity</title><title>Emergency radiology</title><addtitle>Emerg Radiol</addtitle><description>Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level. Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients. 6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P &lt; 0.001). 4 subgroups demonstrated 56-75% decreases in median CT occupied time (P &lt; 0.001) and decreases in variability in ¾ subgroups. This off-console workflow enables increased CT capacity to meet rising ED demand. Similar improvements could be expected across most exam sets and imaging settings if broadly implemented.</description><issn>1438-1435</issn><issn>1438-1435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNUE1PwzAMjRCIjcEf4IBy5BJImjRtjmjiS5q0yzhXaeJAoF9rUqbx6wliSBzs92Q_P9lG6JLRG0ZpcRsYZYISmokUmSrI1xGaM8FLklJ-_I_P0FkI75RSqWR5imZcSckyqeZou3aOmL4LfQNYT7FvdQSL9Ri988brBvsuQtP4V-gMYOjedEKLd_344Zp-lyq6biBg3w5j_5k60ML4I95jC0PyaaGLeLnBRg_a-Lg_RydONwEuDrhALw_3m-UTWa0fn5d3KzIwwSIBB1laN9eOG1HQMne6NJbXhme6lgmVy0tgLpXymisO1rrMiSJxm8ZKvkDXv75pr-0EIVatDyadojvop1BxJqTMlJA0Sa8O0qluwVbD6Fs97qu_N_Fvik1t2Q</recordid><startdate>20241211</startdate><enddate>20241211</enddate><creator>McMenamy, John</creator><creator>Kochkine, Sergey</creator><creator>Bernstein, Mark</creator><creator>Lucero, Anthony</creator><creator>Miles, Randy</creator><creator>Schwertner, Adam</creator><creator>Thaker, Ashesh</creator><creator>Naeger, David M</creator><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>20241211</creationdate><title>Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity</title><author>McMenamy, John ; Kochkine, Sergey ; Bernstein, Mark ; Lucero, Anthony ; Miles, Randy ; Schwertner, Adam ; Thaker, Ashesh ; Naeger, David M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p141t-efe20695af3c47085fa8cd3bc32ab63bc9f58e1fd3b5b393eddf2f47b39d69583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McMenamy, John</creatorcontrib><creatorcontrib>Kochkine, Sergey</creatorcontrib><creatorcontrib>Bernstein, Mark</creatorcontrib><creatorcontrib>Lucero, Anthony</creatorcontrib><creatorcontrib>Miles, Randy</creatorcontrib><creatorcontrib>Schwertner, Adam</creatorcontrib><creatorcontrib>Thaker, Ashesh</creatorcontrib><creatorcontrib>Naeger, David M</creatorcontrib><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Emergency radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McMenamy, John</au><au>Kochkine, Sergey</au><au>Bernstein, Mark</au><au>Lucero, Anthony</au><au>Miles, Randy</au><au>Schwertner, Adam</au><au>Thaker, Ashesh</au><au>Naeger, David M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity</atitle><jtitle>Emergency radiology</jtitle><addtitle>Emerg Radiol</addtitle><date>2024-12-11</date><risdate>2024</risdate><issn>1438-1435</issn><eissn>1438-1435</eissn><abstract>Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level. Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients. 6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P &lt; 0.001). 4 subgroups demonstrated 56-75% decreases in median CT occupied time (P &lt; 0.001) and decreases in variability in ¾ subgroups. This off-console workflow enables increased CT capacity to meet rising ED demand. Similar improvements could be expected across most exam sets and imaging settings if broadly implemented.</abstract><cop>United States</cop><pmid>39661269</pmid><doi>10.1007/s10140-024-02297-z</doi></addata></record>
fulltext fulltext
identifier ISSN: 1438-1435
ispartof Emergency radiology, 2024-12
issn 1438-1435
1438-1435
language eng
recordid cdi_pubmed_primary_39661269
source SpringerLink Journals - AutoHoldings
title Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T00%3A41%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Off-console%20automated%20artificial%20intelligence%20enhanced%20workflow%20enables%20improved%20emergency%20department%20CT%20capacity&rft.jtitle=Emergency%20radiology&rft.au=McMenamy,%20John&rft.date=2024-12-11&rft.issn=1438-1435&rft.eissn=1438-1435&rft_id=info:doi/10.1007/s10140-024-02297-z&rft_dat=%3Cproquest_pubme%3E3146629460%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3146629460&rft_id=info:pmid/39661269&rfr_iscdi=true