Andrew Loza, MD, PhD
Instructor of Biomedical Informatics and Data ScienceCards
About
Research
Publications
2025
Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review
Kanaparthy N, Villuendas-Rey Y, Bakare T, Diao Z, Iscoe M, Loza A, Wright D, Safranek C, Faustino I, Brackett A, Melnick E, Taylor R. Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review. JMIR AI 2025, 4: e76743. PMID: 41071988, PMCID: PMC12513689, DOI: 10.2196/76743.Peer-Reviewed Original ResearchIdentifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study
Socrates V, Wright D, Huang T, Fereydooni S, Dien C, Chi L, Albano J, Patterson B, Kanaparthy N, Wright C, Loza A, Chartash D, Iscoe M, Taylor R. Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study. JMIR Aging 2025, 8: e69504. PMID: 40215480, PMCID: PMC12032504, DOI: 10.2196/69504.Peer-Reviewed Original Research
2024
Pre-to-post COVID-19 pandemic trends in time from emergency department arrival to inpatient floor arrival: Door to floor time
Loza A, Sangal R, Gielissen K, Melnick E, Sankey C, Ostfeld-Johns S. Pre-to-post COVID-19 pandemic trends in time from emergency department arrival to inpatient floor arrival: Door to floor time. The American Journal Of Emergency Medicine 2024, 89: 187-189. PMID: 39731897, DOI: 10.1016/j.ajem.2024.12.051.Peer-Reviewed Original ResearchCorrection: 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. Correction: Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice. PLOS ONE 2024, 19: e0315090. PMID: 39625911, PMCID: PMC11614266, DOI: 10.1371/journal.pone.0315090.Peer-Reviewed Original ResearchUsing 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, 28: 38-47. PMID: 39444281, PMCID: PMC11648331, 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
Crawling toward obsolescence: The extended lifespan of amylase for pancreatitis
Kanaparthy N, Loza A, Hauser R. Crawling toward obsolescence: The extended lifespan of amylase for pancreatitis. PLOS ONE 2023, 18: e0296180. PMID: 38127992, PMCID: PMC10734915, DOI: 10.1371/journal.pone.0296180.Peer-Reviewed Original ResearchAdoption of Emergency Department–Initiated Buprenorphine for Patients With Opioid Use Disorder
Gao E, Melnick E, Paek H, Nath B, Taylor R, Loza A. Adoption of Emergency Department–Initiated Buprenorphine for Patients With Opioid Use Disorder. JAMA Network Open 2023, 6: e2342786. PMID: 37948075, PMCID: PMC10638655, DOI: 10.1001/jamanetworkopen.2023.42786.Peer-Reviewed Original ResearchConceptsHealth care systemED initiationOpioid use disorderBuprenorphine initiationCare systemUse disordersEmergency department-initiated buprenorphineSecondary analysisClinician's roleEmergency department initiationClinical decision support interventionClinical decision support toolProportional hazard modelingCare of patientsNetwork of cliniciansDecision support interventionsAdvanced practice practitionersDose-dependent mannerUnique cliniciansTime-dependent covariatesTrial interventionNonintervention groupED clustersMore effective interventionsNumber of exposuresPROSER: 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
2022
Rates of Body Mass Index Increase in Children During the COVID-19 Pandemic
Loza AJ, Child I, Doolittle BR. Rates of Body Mass Index Increase in Children During the COVID-19 Pandemic. Childhood Obesity 2022, 19: 353-356. PMID: 35904946, DOI: 10.1089/chi.2022.0047.Peer-Reviewed Original ResearchConceptsClinic visitsWeight gainRetrospective longitudinal cohort studyBody mass index increaseRegular clinic visitsSignificant public health concernPrimary care clinicsHigh-risk subgroupsLongitudinal cohort studyYears of ageCOVID-19 pandemicPublic health concernPublic health officialsCohort studyCare clinicsPediatric obesityObesity statusRisk factorsPrepandemic periodHigher baselineAge groupsBMIHealth concernStudy periodHealth officials
News
News
- November 11, 2025
Andrew Loza Receives Poster Award at CTSA Meeting
- July 01, 2025
Graduate Spotlight: Huan Li's Journey Through Yale's CBB PhD Program
- May 29, 2025
Andrew Loza Receives 2024 Hartwell Foundation Award to Advance Pediatric Predictive Modeling
- February 26, 2025
New BIDS Faculty Spotlight: Andrew Loza
Get In Touch
Contacts
Email