Donald Wright, MD, MHS
Instructor of Emergency MedicineCards
About
Research
Publications
2025
AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning
Jin Q, Wang Z, Yang Y, Zhu Q, Wright D, Huang T, Khandekar N, Wan N, Ai X, Wilbur W, He Z, Taylor R, Chen Q, Lu Z. AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning. Nature Communications 2025, 16: 9377. PMID: 41130954, PMCID: PMC12549800, DOI: 10.1038/s41467-025-64430-x.Peer-Reviewed Original ResearchConceptsLanguage agentsHealthcare analyticsUnit testsTool learningTool buildersEmergency department notesDissemination challengesClinical calculatorsDiverse setRisk predictionIndividual patient careQuality checksUsabilityUsersPatient careMedical riskLearningCheckingAccuracyRisk managementClinical contextReal-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review
Kanaparthy N, Villuendas-Rey Y, Bakare T, Diao Z, Iscoe M, Loza A, Wright D, Safranek C, Faustino I, Brackett A, Melnick E, Taylor R. Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review. JMIR AI 2025, 4: e76743. PMID: 41071988, PMCID: PMC12513689, DOI: 10.2196/76743.Peer-Reviewed Original ResearchYoung women’s experience of personal recovery following acute myocardial infarction: A qualitative study
Zhu C, Arakaki A, Pan A, Danvers K, Barbardo A, Wyatt J, Wright C, Wright D, Pilote L, Raparelli V, Monin J, Oettingen G, Dreyer R, Pavlo A. Young women’s experience of personal recovery following acute myocardial infarction: A qualitative study. PLOS ONE 2025, 20: e0298798. PMID: 40924702, PMCID: PMC12419669, DOI: 10.1371/journal.pone.0298798.Peer-Reviewed Original ResearchConceptsPersonal recovery frameworkAcute myocardial infarction survivorsYoung women's experiencesAcute myocardial infarctionPersonal recoveryWomen's experiencesAwareness of self-careExperience of acute myocardial infarctionRate of acute myocardial infarctionYoung womenYear post-AMIExperiences of young womenRecovery frameworkIn-depth interviewsSelf-CareMyocardial infarctionYoung women's perspectivesPsychosocial domainsSocial supportQualitative studyWomen's perspectivesSocial rolesPost-AMIPhenomenological approachStudy findingsGoal Setting, Feedback, and Performance in Emergency Medicine Training
Coughlin R, Devlin D, Bonner S, Srica N, Jameyfield E, Goldflam K, Tsyrulnik A, Osborne K, Phadke M, Dziura J, Gore K, Wright D, Della-Giustina D, Gottlieb M, Bod J. Goal Setting, Feedback, and Performance in Emergency Medicine Training. MedEdPublish 2025, 15: 77. DOI: 10.12688/mep.21065.1.Peer-Reviewed Original ResearchGoal settingEmergency medicineFast-paced environmentOdds ratioPostgraduate yearEmergency medicine trainingConstructive feedbackEM residentsMedicine trainingEM facultyPostgraduate year levelEM programsFeedback contentTotal residentsSurvey toolResident performancePrimary outcomeVerbal feedbackExclusion criteriaResidentsThe Effect of Ambient Artificial Intelligence Scribes on Trainee Documentation Burden
Wright D, Kanaparthy N, Melnick E, Levy D, Huot S, Hsiao A, Schwamm L, Ong S. The Effect of Ambient Artificial Intelligence Scribes on Trainee Documentation Burden. Applied Clinical Informatics 2025, 16: 872-878. PMID: 40602775, PMCID: PMC12367366, DOI: 10.1055/a-2647-1142.Peer-Reviewed Original ResearchConceptsDocumentation burdenResident physician traineesPhysician traineesSystem Usability ScaleNet Promoter ScoreAssociated with improvementsMeasures of acceptabilityPilot periodNASA Task Load IndexMeasures of usabilityProspective observational studyTask Load IndexTrainee experienceNASA-TLX scoresUsability ScaleTrainee learningObservational studyGenerative artificial intelligenceTraineesPromoter ScoreArtificial intelligenceUtilization metricsTrainee useBurdenNASA-TLXResponse to the Letter to the Editor Regarding: "Automated Computation of the HEART Score with the GPT-4 Large Language Model".
Taylor RA, Wright DS. Response to the Letter to the Editor Regarding: "Automated Computation of the HEART Score with the GPT-4 Large Language Model". Am J Emerg Med 2025 PMID: 40410037, DOI: 10.1016/j.ajem.2025.05.027.Peer-Reviewed Original ResearchIdentifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study
Socrates V, Wright D, Huang T, Fereydooni S, Dien C, Chi L, Albano J, Patterson B, Kanaparthy N, Wright C, Loza A, Chartash D, Iscoe M, Taylor R. Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study. JMIR Aging 2025, 8: e69504. PMID: 40215480, PMCID: PMC12032504, DOI: 10.2196/69504.Peer-Reviewed Original ResearchAutomated 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 differenceAppsQuantitative valve motion assessment in adolescents using point-of-care ultrasound: short communication
Riera A, Chen L, Wright D, Leviter J. Quantitative valve motion assessment in adolescents using point-of-care ultrasound: short communication. The Ultrasound Journal 2025, 17: 11. PMID: 39847270, PMCID: PMC11757829, DOI: 10.1186/s13089-025-00402-y.Peer-Reviewed Original ResearchTricuspid annular plane systolic excursionE-point septal separationPoint-of-care ultrasoundAnnular plane systolic excursionRight ventricular systolic functionPediatric emergency departmentAbnormal vital signsCross-sectional studyVentricular systolic functionBody mass indexUnpaired t-testM-mode measurementsEmergency departmentSystolic excursionAnthropometric dataSystolic functionCardiopulmonary complaintsNo significant correlationMass indexPsychiatric illnessMental statusPearson correlationExclusion criteriaSeptal separationMotion assessment
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 detailsClinicians