2025
Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals
Kuo T, Gabriel R, Koola J, Schooley R, Ohno-Machado L. Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals. Nature Communications 2025, 16: 1371. PMID: 39910076, PMCID: PMC11799213, DOI: 10.1038/s41467-025-56510-9.Peer-Reviewed Original ResearchConceptsHeart disease dataParts of informationLearning counterpartsCentralized solutionVertical scenariosPatient privacyPredictive analyticsFederated modelSynchronization timePrivacyUC San DiegoPatient-level recordsDisease dataPatient dataPrediction modelPatient careHealthcare centersUniversity of CaliforniaCalifornia hospitalsHealthcare systemQuality improvementPatient records
2019
Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice
Baxter S, Gali H, Huang A, Millen M, El-Kareh R, Nudleman E, Robbins S, Heichel C, Camp A, Korn B, Lee J, Kikkawa D, Longhurst C, Chiang M, Hribar M, Ohno-Machado L. Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice. American Journal Of Ophthalmology 2019, 206: 161-167. PMID: 30910517, PMCID: PMC6755078, DOI: 10.1016/j.ajo.2019.03.014.Peer-Reviewed Original ResearchConceptsPatient encountersNew patient evaluationsClinical workflowElectronic health record useOutpatient ophthalmology clinicHigh clinical volumeAcademic ophthalmology departmentsAcademic ophthalmology practicePaper-based documentationAge 43.9Postoperative visitRoutine followClinic hoursOphthalmology clinicOphthalmology departmentPatient evaluationOphthalmology practiceOutcome measurementsPatientsPatient careHours workRecord useOphthalmologistsClinical volumeTotal time
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