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 initiationEmergency Department Implementation of a Multimodal Electronic Health Record–Integrated Clinical Intervention for High-Sensitivity Troponin Testing Increases Diagnostic Efficiency
Sangal R, Iscoe M, Rothenberg C, Possick S, Taylor R, Safdar B, Desai N, Rhodes D, Venkatesh A. Emergency Department Implementation of a Multimodal Electronic Health Record–Integrated Clinical Intervention for High-Sensitivity Troponin Testing Increases Diagnostic Efficiency. Journal Of The American College Of Emergency Physicians Open 2025, 6: 100202. PMID: 40606317, PMCID: PMC12214263, DOI: 10.1016/j.acepjo.2025.100202.Peer-Reviewed Original ResearchEmergency departmentAcute myocardial infarction diagnosisTroponin testingED dischargeMyocardial infarction diagnosisAssociated with lower oddsAccelerated diagnostic protocolPhysician practice variationAcute coronary syndromeInfarction diagnosisChest pain evaluationMultivariate logistic regressionSuspected acute coronary syndromePostintervention periodDownstream testingPhysician practicesPreintervention periodED dispositionLower oddsCoronary syndromeDecreased oddsIncreased oddsPractice variationED patientsHs-cTnTAdaptive decision support for addiction treatment to implement initiation of buprenorphine for opioid use disorder in the emergency department: protocol for the ADAPT Multiphase Optimization Strategy trial
Iscoe M, Hooper C, Levy D, Buchanan L, Dziura J, Meeker D, Taylor R, D’Onofrio G, Oladele C, Sarpong D, Paek H, Wilson F, Heagerty P, Delgado M, Hoppe J, Melnick E. Adaptive decision support for addiction treatment to implement initiation of buprenorphine for opioid use disorder in the emergency department: protocol for the ADAPT Multiphase Optimization Strategy trial. BMJ Open 2025, 15: e098072. PMID: 39979056, PMCID: PMC11842997, DOI: 10.1136/bmjopen-2024-098072.Peer-Reviewed Original ResearchConceptsClinical decision supportMultiphase optimization strategyOpioid use disorderEmergency departmentInitiation of buprenorphineClinical decision support usePlan-Do-Study-Act cyclesLearning health system approachPlan-Do-Study-ActClinical decision support toolHealth system approachAdaptive decision supportUse disorderDecision supportAddiction treatmentPeer-reviewed journalsBuprenorphine initiationOpioid use disorder treatment initiationOpioid-related mortalityIntervention componentsED settingClinician feedbackInstitute Institutional Review BoardTreatment of opioid use disorderParticipating sites
2024
Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions
Taylor R, Sangal R, Smith M, Haimovich A, Rodman A, Iscoe M, Pavuluri S, Rose C, Janke A, Wright D, Socrates V, Declan A. Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions. Academic Emergency Medicine 2024, 32: 327-339. PMID: 39676165, PMCID: PMC11921089, DOI: 10.1111/acem.15066.Peer-Reviewed Original ResearchClinical decision supportEmergency departmentArtificial intelligencePatient safetyDiagnostic errorsImplementing AIImprove patient safetyClinical decision support systemsEnhance patient outcomesReducing diagnostic errorsLeverage artificial intelligenceEmergency medicineHealth careTargeted educationReduce cognitive loadQuality improvementEmergency cliniciansData retrievalReal-time insightsDecision supportPatient outcomesCognitive overloadInformation-gathering processPatient detailsCliniciansIdentifying 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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