Predicting 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 demandMachineValidatingPatientsCharacterizing 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