2023
Predicting relations between SOAP note sections: The value of incorporating a clinical information model
Socrates V, Gilson A, Lopez K, Chi L, Taylor R, Chartash D. Predicting relations between SOAP note sections: The value of incorporating a clinical information model. Journal Of Biomedical Informatics 2023, 141: 104360. PMID: 37061014, PMCID: PMC10197152, DOI: 10.1016/j.jbi.2023.104360.Peer-Reviewed Original ResearchAutomatable end‐of‐life screening for older adults in the emergency department using electronic health records
Haimovich A, Xu W, Wei A, Schonberg M, Hwang U, Taylor R. Automatable end‐of‐life screening for older adults in the emergency department using electronic health records. Journal Of The American Geriatrics Society 2023, 71: 1829-1839. PMID: 36744550, PMCID: PMC10258151, DOI: 10.1111/jgs.18262.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesDeathElectronic Health RecordsEmergency Service, HospitalHumansReproducibility of ResultsRetrospective StudiesConceptsAdvance care planningDecision curve analysisLife screeningComorbidity indexCode statusPrognostic modelHealth systemOlder adultsCurve analysisOlder ED patientsPalliative care interventionsObservational cohort studyEmergency department visitsPalliative care servicesElixhauser Comorbidity IndexReceiver-operating characteristic curveIdentification of patientsMultivariable logistic regressionLarge regional health systemLife-limiting illnessRisk older adultsCode status ordersLife Screening ToolMortality predictive modelsElectronic health records
2016
The Association Between Physician Empathy and Variation in Imaging Use
Melnick ER, O'Brien EG, Kovalerchik O, Fleischman W, Venkatesh AK, Taylor RA. The Association Between Physician Empathy and Variation in Imaging Use. Academic Emergency Medicine 2016, 23: 895-904. PMID: 27343485, PMCID: PMC5884096, DOI: 10.1111/acem.13017.Peer-Reviewed Original ResearchConceptsCT utilizationEmergency physician performanceEmergency physiciansPhysician performanceCT utilization ratesEmergency Department CTPhysician survey respondentsPatient-level variablesCross-sectional studyCohort of physiciansPhysician empathyLarge health systemPsychometric testsMixed effects regression modelsPhysician-based factorsPsychometric scalesSurvey response rateAcademic EDSubset analysisPhysician demographicsHead CTInterphysician variationResponse rateImaging useRTS scorePrediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK. Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine 2016, 23: 269-278. PMID: 26679719, PMCID: PMC5884101, DOI: 10.1111/acem.12876.Peer-Reviewed Original ResearchConceptsMachine learning approachesElectronic health recordsLearning approachPredictive analyticsMachine learning techniquesRandom forest modelClinical decision support systemBig Data DrivenDecision support systemForest modelLearning techniquesUse casesData-DrivenFacilitate automationTraditional analytic techniquesAnalyticsSupport systemSimple heuristicsNew analyticsHealth recordsSmall setTree modelQuestion of generalizabilityPrediction modelDecision rules