2024
Using Panel Management to Identify Adult Patients With High-Risk Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis Fibrosis in a Primary Care Clinic: A Pilot Study.
Householder S, Loza A, Gupta V, Doolittle B. Using Panel Management to Identify Adult Patients With High-Risk Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis Fibrosis in a Primary Care Clinic: A Pilot Study. The Permanente Journal 2024, 1-10. PMID: 39444281, DOI: 10.7812/tpp/24.094.Peer-Reviewed Original ResearchPrimary care clinicsYears of ageCare clinicsPanel managementShear wave elastographyFIB-4 scoreElectronic health recordsDetection of patientsClinically relevant morbidityFollow-up appointmentsWave elastographyPrimary careRelevant morbidityFIB-4Advanced fibrosisFibrosis-4Adult patientsHealth recordsSubspecialty careMedical complexityExperience complicationsPatient acceptanceTargeted interventionsWork-upClinical care
2023
PROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data
Iscoe M, Loza A, Turbiville D, Campbell S, Peaper D, Balbuena-Merle R, Hauser R. PROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data. American Journal Of Clinical Pathology 2023, 160: 98-105. PMID: 37026746, DOI: 10.1093/ajcp/aqad024.Peer-Reviewed Original ResearchConceptsQuality improvement studyElectronic health recordsLaboratory valuesWeb-based clinical decision support toolClinical decision support toolElectronic health record dataHealth record dataImprovement studyResident trainingBlood smear interpretationClinical outcomesMorphologic findingsAcademic hospitalCorresponding reference rangesMedication informationReference rangeMicroscopy findingsCDS toolsIntervention effectsPathology practiceSmear interpretationHealth recordsRecord dataPathologistsPatientsPredicting 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