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 PMID: 39676165, 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