2025
A Measurement Science Framework to Optimize CDS for Opioid Use Disorder Treatment in the ED
Iscoe M, Hooper C, Levy D, Lutz J, Paek H, Rose C, Kannampallil T, Meeker D, Dziura J, Melnick E. A Measurement Science Framework to Optimize CDS for Opioid Use Disorder Treatment in the ED. Applied Clinical Informatics 2025, 16: 1067-1076. PMID: 40834872, PMCID: PMC12431813, DOI: 10.1055/a-2595-0317.Peer-Reviewed Original ResearchConceptsClinical decision supportEmergency departmentED initiationOpioid use disorderScience FrameworkClinical decision support applicationsEmergency department-initiated buprenorphineUse disorderSingle health systemInitiation of buprenorphineOpioid use disorder treatmentWorking group sessionsHealth systemGroup sessionsEligible encountersCo-design processMultidisciplinary partnersCo-designPostmeeting surveysCDS performancePriority categoriesAHRQConsensus methodologyFinal measurementBuprenorphine initiationCase Report: A health system’s experience using clinical decision support to promote note sharing after the 21st Century Cures Act
Iscoe M, Venkatesh A, Powers E, Kashyap N, Hsiao A, Millard H, Sangal R. Case Report: A health system’s experience using clinical decision support to promote note sharing after the 21st Century Cures Act. JAMIA Open 2025, 8: ooaf051. PMID: 40510806, PMCID: PMC12161449, DOI: 10.1093/jamiaopen/ooaf051.Peer-Reviewed Original ResearchClinical decision supportHealth system's experienceRegional health systemDecision supportPatient engagementCentury Cures ActHealth systemPortal accessClinical notesConsistent with prior research showingCures ActPromote complianceSensitive informationStudy periodLinear regressionPediatricObservational analysisPsychiatryPatients/proxiesNotesPatientsSupportHealthCliniciansResearch show
2024
Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models
Iscoe M, Socrates V, Gilson A, Chi L, Li H, Huang T, Kearns T, Perkins R, Khandjian L, Taylor R. Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models. Academic Emergency Medicine 2024, 31: 599-610. PMID: 38567658, DOI: 10.1111/acem.14883.Peer-Reviewed Original ResearchElectronic health recordsNatural language processingNatural language processing modelsEmergency departmentTransformer-based modelsClinical notesF1-measureClinical decision supportLanguage modelSpaCy modelsU.S. health systemElements of natural language processingPublic health surveillanceConvolutional neural network-based modelProcessing long documentsIdentification of symptomsHealth recordsHealth systemClinician notesNeural network-based modelMedical careHealth surveillanceSymptom identificationEntity recognitionNetwork-based model
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