2023
Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.
Boyd A, Gonzalez-Guarda R, Lawrence K, Patil C, Ezenwa M, O'Brien E, Paek H, Braciszewski J, Adeyemi O, Cuthel A, Darby J, Zigler C, Ho P, Faurot K, Staman K, Leigh J, Dailey D, Cheville A, Del Fiol G, Knisely M, Grudzen C, Marsolo K, Richesson R, Schlaeger J. Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. Journal Of The American Medical Informatics Association 2023, 30: 1561-1566. PMID: 37364017, PMCID: PMC10436149, DOI: 10.1093/jamia/ocad115.Peer-Reviewed Original ResearchMeSH KeywordsBiasDelivery of Health CareElectronic Health RecordsHealth EquityHumansNational Institutes of Health (U.S.)United StatesConceptsElectronic health record dataPragmatic clinical trialsHealth record dataPopulation health problemElectronic health record systemsClinical trialsEHR-derived dataHealth record systemsHealth problemsSocial determinantsHealth equityRecord dataVulnerable populationsEHR dataHealthcare systemNational InstituteRecord systemLack of generalizabilityHealthDifferent subsetsGeneralizable researchEPCTsPopulationPotential biasTrialsEquity and bias in electronic health records data
Boyd A, Gonzalez-Guarda R, Lawrence K, Patil C, Ezenwa M, O'Brien E, Paek H, Braciszewski J, Adeyemi O, Cuthel A, Darby J, Zigler C, Ho P, Faurot K, Staman K, Leigh J, Dailey D, Cheville A, Del Fiol G, Knisely M, Marsolo K, Richesson R, Schlaeger J. Equity and bias in electronic health records data. Contemporary Clinical Trials 2023, 130: 107238. PMID: 37225122, PMCID: PMC10330606, DOI: 10.1016/j.cct.2023.107238.Peer-Reviewed Original ResearchPredicting 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
Emergency physicians' EHR use across hospitals: A cross-sectional analysis
Iscoe MS, Holland ML, Paek H, Flood C, Melnick ER. Emergency physicians' EHR use across hospitals: A cross-sectional analysis. The American Journal Of Emergency Medicine 2022, 61: 205-207. PMID: 35842301, DOI: 10.1016/j.ajem.2022.07.014.Peer-Reviewed Original Research