2025
Automated computation of the HEART score with the GPT-4 large language model
Wright D, Socrates V, Huang T, Safranek C, Sangal R, Dilip M, Boivin Z, Srica N, Wright C, Feher A, Miller E, Chartash D, Taylor R. Automated computation of the HEART score with the GPT-4 large language model. The American Journal Of Emergency Medicine 2025, 93: 120-125. PMID: 40184662, PMCID: PMC12202168, DOI: 10.1016/j.ajem.2025.03.065.Peer-Reviewed Original ResearchConceptsClinical decision supportHEART scoreChest pain observation unitAverage heart scoreCohen's weighted kappaDecision supportNurse practitionersPhysician assistantsRetrospective cohort studyRoutine careCases of disagreementProspective InvestigationAPP scoresCohort studySafety interventionsPhysician judgmentObservation unitAdverse cardiac eventsInstitutional registryScoresPhysiciansInterventionParticipantsNo significant differenceAppsAdaptive 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 modelFormative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol
Wong A, Nath B, Shah D, Kumar A, Brinker M, Faustino I, Boyce M, Dziura J, Heckmann R, Yonkers K, Bernstein S, Adapa K, Taylor R, Ovchinnikova P, McCall T, Melnick E. Formative evaluation of an emergency department clinical decision support system for agitation symptoms: a study protocol. BMJ Open 2024, 14: e082834. PMID: 38373857, PMCID: PMC10882402, DOI: 10.1136/bmjopen-2023-082834.Peer-Reviewed Original ResearchConceptsComputerised clinical decision supportED treatRestraint useExperiences of restraint useMental health-related visitsEmergency departmentPrevent agitationSystems-related factorsImprove patient experienceClinical decision support systemsRegional health systemClinical decision supportDe-escalation techniquesRandomised controlled trialsFormative evaluationPeer-reviewed journalsBest-practice guidanceAt-risk populationsCDS toolsThematic saturationED cliniciansPatient experienceED sitesHealth systemED physicians
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