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 demandMachineValidatingPatientsAnalysis 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 time