2024
Impact of electronic health record updates and changes on the delivery and monitoring of interventions in embedded pragmatic clinical trials
Marsolo K, Cheville A, Melnick E, Jarvik J, Simon G, Sluka K, Crofford L, Staman K, Richesson R, Schlaeger J, Curtis L. Impact of electronic health record updates and changes on the delivery and monitoring of interventions in embedded pragmatic clinical trials. Contemporary Clinical Trials 2024, 148: 107744. PMID: 39561918, DOI: 10.1016/j.cct.2024.107744.Peer-Reviewed Original ResearchElectronic health record systemsElectronic health recordsPragmatic clinical trialsElectronic health record platformsDelivery of interventionsHealth recordsTrial interventionMonitoring of interventionsRecord updatesStudy teamMonitoring outcomesClinical trialsInterventionLocal implementationTechnological evolutionTeamDynamic natureTrialsDeliveryCase exampleSystemBenchmarking Emergency Physician EHR Time per Encounter Based on Patient and Clinical Factors
Iscoe M, Venkatesh A, Holland M, Krumholz H, Sheares K, Melnick E. Benchmarking Emergency Physician EHR Time per Encounter Based on Patient and Clinical Factors. JAMA Network Open 2024, 7: e2427389. PMID: 39136949, PMCID: PMC11322841, DOI: 10.1001/jamanetworkopen.2024.27389.Peer-Reviewed Original ResearchVirtual Scribes and Physician Time Spent on Electronic Health Records
Rotenstein L, Melnick E, Iannaccone C, Zhang J, Mugal A, Lipsitz S, Healey M, Holland C, Snyder R, Sinsky C, Ting D, Bates D. Virtual Scribes and Physician Time Spent on Electronic Health Records. JAMA Network Open 2024, 7: e2413140. PMID: 38787556, PMCID: PMC11127114, DOI: 10.1001/jamanetworkopen.2024.13140.Peer-Reviewed Original ResearchConceptsEHR timeElectronic health recordsHealth recordsPhysician timePre-post quality improvement studyPrimary care specialtiesQuality improvement studyFactors associated with changesAssociated with significant decreasesAssociated with burnoutMultivariate linear regression modelAcademic medical centerCare specialtiesImprovement studyLinear regression modelsMedical specialistsMedical specialtiesOutpatient settingStudy sampleSignificant decreasePhysiciansMedical CenterParticipation episodesAppointmentWomen's Hospital
2023
Quantifying EHR and Policy Factors Associated with the Gender Productivity Gap in Ambulatory, General Internal Medicine
Li H, Rotenstein L, Jeffery M, Paek H, Nath B, Williams B, McLean R, Goldstein R, Nuckols T, Hoq L, Melnick E. Quantifying EHR and Policy Factors Associated with the Gender Productivity Gap in Ambulatory, General Internal Medicine. Journal Of General Internal Medicine 2023, 39: 557-565. PMID: 37843702, PMCID: PMC10973284, DOI: 10.1007/s11606-023-08428-5.Peer-Reviewed Original ResearchElectronic health recordsWork relative value unitsPhysician genderPractice characteristicsWomen physiciansMen physiciansGeneral internal medicine physiciansEHR useInternal medicine physiciansPhysician productivityGeneral internal medicineMultivariable adjustmentPatient counselingCare discussionsPhysician ageClinical activityMedicine physiciansPredicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice
Lopez K, Li H, Paek H, Williams B, Nath B, Melnick E, Loza A. Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice. PLOS ONE 2023, 18: e0280251. PMID: 36724149, PMCID: PMC9891518, DOI: 10.1371/journal.pone.0280251.Peer-Reviewed Original ResearchConceptsElectronic health recordsEHR use patternsHealthcare industryPhysician departureSHAP valuesHealth recordsPhysician characteristicsLongitudinal cohortPhysician ageRisk physiciansAmbulatory practiceTargeted interventionsAppropriate interventionsPhysiciansTop variablesDocumentation timePhysician turnoverPredictive modelHeavy burdenInterventionInboxPhysician demandMachineValidatingPatients
2022
User centered clinical decision support to implement initiation of buprenorphine for opioid use disorder in the emergency department: EMBED pragmatic cluster randomized controlled trial
Melnick ER, Nath B, Dziura JD, Casey MF, Jeffery MM, Paek H, Soares WE, Hoppe JA, Rajeevan H, Li F, Skains RM, Walter LA, Patel MD, Chari SV, Platts-Mills TF, Hess EP, D'Onofrio G. User centered clinical decision support to implement initiation of buprenorphine for opioid use disorder in the emergency department: EMBED pragmatic cluster randomized controlled trial. The BMJ 2022, 377: e069271. PMID: 35760423, PMCID: PMC9231533, DOI: 10.1136/bmj-2021-069271.Peer-Reviewed Original ResearchConceptsOpioid use disorderUsual care armEmergency departmentUse disordersCare armPragmatic clusterClinical decision supportIntervention armRoutine emergency careSecondary implementation outcomesSeverity of withdrawalTertiary care centerClinical decision support toolInitiation of buprenorphineElectronic health record tasksElectronic health record workflowsRE-AIM frameworkElectronic health record platformsHealth record platformsClinical decision support systemElectronic health recordsVisit documentationTreatment of addictionUsual careAdult patientsPragmatic clinical trial design in emergency medicine: Study considerations and design types
Gettel CJ, Yiadom MYAB, Bernstein SL, Grudzen CR, Nath B, Li F, Hwang U, Hess EP, Melnick ER. Pragmatic clinical trial design in emergency medicine: Study considerations and design types. Academic Emergency Medicine 2022, 29: 1247-1257. PMID: 35475533, PMCID: PMC9790188, DOI: 10.1111/acem.14513.Peer-Reviewed Original ResearchConceptsPragmatic clinical trialsClinical trial designTrial designReal-world clinical practicePragmatic clinical trial designElectronic health recordsEmergency departmentClinical trialsStudy design typeClinical practiceStudy typeTrial componentsHealth recordsEmergency medicineEmergency medicine investigatorsHuman subjects concernsInvestigatorsStudy findingsStudy considerationsTrialistsTrialsCluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study
Fong A, Iscoe M, Sinsky CA, Haimovich A, Williams B, O'Connell RT, Goldstein R, Melnick E. Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study. JMIR Medical Informatics 2022, 10: e34954. PMID: 35275070, PMCID: PMC9055474, DOI: 10.2196/34954.Peer-Reviewed Original ResearchElectronic health recordsCare physiciansEHR timeRetrospective cohort studyRetrospective cohort analysisElectronic health record usePrimary care physiciansOffice-based physician practicesClusters of physiciansAmbulatory care physiciansCohort studyCohort analysisPediatric specialtiesInternal medicineRecord useEHR usePhysiciansPhysician practicesHealth recordsFamily medicineHoursPhenotype clustersPhenotypeLarge proportionMedicineGender Differences in Time Spent on Documentation and the Electronic Health Record in a Large Ambulatory Network
Rotenstein LS, Fong AS, Jeffery MM, Sinsky CA, Goldstein R, Williams B, Melnick ER. Gender Differences in Time Spent on Documentation and the Electronic Health Record in a Large Ambulatory Network. JAMA Network Open 2022, 5: e223935. PMID: 35323954, PMCID: PMC8948526, DOI: 10.1001/jamanetworkopen.2022.3935.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsCross-sectional studyAmbulatory care networkGender differencesCare networkTime Spent
2021
Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover
Melnick ER, Fong A, Nath B, Williams B, Ratwani RM, Goldstein R, O’Connell R, Sinsky CA, Marchalik D, Mete M. Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover. JAMA Network Open 2021, 4: e2128790. PMID: 34636911, PMCID: PMC8511970, DOI: 10.1001/jamanetworkopen.2021.28790.Peer-Reviewed Original ResearchConceptsElectronic health recordsPhysician turnoverRetrospective cohort studyElectronic health record usePractice networkPhysician productivityWarrants further investigationCohort studyEHR timeAge 45Care teamPhysician departurePhysician ordersMAIN OUTCOMEHigh riskPatient timeAmbulatory physiciansPatient volumeUnique physiciansRecord useEHR useHealth care organizationsPhysiciansHealth recordsClinical timeCharacterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis
Melnick ER, Ong SY, Fong A, Socrates V, Ratwani RM, Nath B, Simonov M, Salgia A, Williams B, Marchalik D, Goldstein R, Sinsky CA. Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis. Journal Of The American Medical Informatics Association 2021, 28: 1383-1392. PMID: 33822970, PMCID: PMC8279798, DOI: 10.1093/jamia/ocab011.Peer-Reviewed Original ResearchConceptsElectronic health recordsEHR timeCross-sectional analysisAmbulatory physiciansPatient timeHealth systemClinical hoursHours of patientsMedStar Health systemYale-New HavenObstetrics/gynecologyNeurology/psychiatryMultivariable analysisPhysician genderCertain medical specialtiesPhysical medicineFemale physiciansEHR usePhysiciansHealth recordsHealthcare systemMedical specialtiesHoursSpecialtiesGender
2019
Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study
Chartash D, Paek H, Dziura JD, Ross BK, Nogee DP, Boccio E, Hines C, Schott AM, Jeffery MM, Patel MD, Platts-Mills TF, Ahmed O, Brandt C, Couturier K, Melnick E. Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study. JMIR Medical Informatics 2019, 7: e15794. PMID: 31674913, PMCID: PMC6913746, DOI: 10.2196/15794.Peer-Reviewed Original ResearchOpioid use disorderNegative predictive valuePositive predictive valueEmergency department patientsEmergency departmentUse disordersHealth care systemPredictive valueComputable phenotypeExternal validation phasesDepartment patientsCare systemPhysician chart reviewLarge health care systemExternal validation cohortEmergency medicine physiciansHigh predictive valueElectronic health recordsChart reviewChief complaintValidation cohortPragmatic trialClinical dataBilling codesMedicine physicians
2017
Should US doctors embrace electronic health records?
Gellert G, Webster L, Gillean J, Melnick E, Kanzaria H. Should US doctors embrace electronic health records? The BMJ 2017, 356: j242. PMID: 28119282, DOI: 10.1136/bmj.j242.Peer-Reviewed Original Research