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Predicting Preoperative Consultation Clinician Time Using Machine Learning and Electronic Health Record Audit Log Data.

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Title: Predicting Preoperative Consultation Clinician Time Using Machine Learning and Electronic Health Record Audit Log Data.
Authors: Le ST; Kaiser Permanente Division of Research, Pleasanton, California., Lawson B; Kaiser Permanente Division of Research, Pleasanton, California., Lu Y; Kaiser Permanente Division of Research, Pleasanton, California., Gochi AM; University of California San Francisco, San Francisco, California., Kipnis P; Kaiser Permanente Division of Research, Pleasanton, California., Cohn BR; Kaiser Permanente Division of Research, Pleasanton, California., Liu VX; Kaiser Permanente Division of Research, Pleasanton, California.
Source: Anesthesiology [Anesthesiology] 2026 Mar 01; Vol. 144 (3), pp. 724-727. Date of Electronic Publication: 2026 Jan 07.
Publication Type: Journal Article
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 1300217 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1528-1175 (Electronic) Linking ISSN: 00033022 NLM ISO Abbreviation: Anesthesiology Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
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An: 41499726
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Predicting Preoperative Consultation Clinician Time Using Machine Learning and Electronic Health Record Audit Log Data.
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  Data: <searchLink fieldCode="AU" term="%22Le+ST%22">Le ST</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.<br /><searchLink fieldCode="AU" term="%22Lawson+B%22">Lawson B</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.<br /><searchLink fieldCode="AU" term="%22Lu+Y%22">Lu Y</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.<br /><searchLink fieldCode="AU" term="%22Gochi+AM%22">Gochi AM</searchLink>; University of California San Francisco, San Francisco, California.<br /><searchLink fieldCode="AU" term="%22Kipnis+P%22">Kipnis P</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.<br /><searchLink fieldCode="AU" term="%22Cohn+BR%22">Cohn BR</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.<br /><searchLink fieldCode="AU" term="%22Liu+VX%22">Liu VX</searchLink>; Kaiser Permanente Division of Research, Pleasanton, California.
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  Data: <searchLink fieldCode="JN" term="%221300217%22">Anesthesiology</searchLink> [Anesthesiology] 2026 Mar 01; Vol. 144 (3), pp. 724-727. <i>Date of Electronic Publication: </i>2026 Jan 07.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Lippincott+Williams+%26+Wilkins%22">Lippincott Williams & Wilkins </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>1300217 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1528-1175 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200033022%22">00033022 </searchLink><i>NLM ISO Abbreviation: </i>Anesthesiology <i>Subsets: </i>MEDLINE; In Process
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      – Type: doi
        Value: 10.1097/ALN.0000000000005862
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      – Code: eng
        Text: English
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      – TitleFull: Predicting Preoperative Consultation Clinician Time Using Machine Learning and Electronic Health Record Audit Log Data.
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            NameFull: Le ST
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            NameFull: Lawson B
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            – D: 01
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              Text: 2026 Mar 01
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              Y: 2026
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              Value: 1528-1175
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              Value: 144
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            – TitleFull: Anesthesiology
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