2018
Genomics and electronic health record systems
Ohno-Machado L, Kim J, Gabriel R, Kuo G, Hogarth M. Genomics and electronic health record systems. Human Molecular Genetics 2018, 27: r48-r55. PMID: 29741693, PMCID: PMC5946823, DOI: 10.1093/hmg/ddy104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsElectronic health recordsHigh-level viewElectronic health record systemsHealth record systemsNext generation systemsEHR systemsSeamless fashionOpen issuesMyriad of approachesHealth recordsRecord systemSpecific solutionsEnd goalSources of informationAnalysis of genomesInformationCustomizationSystemGeneration systemVisionDegrees of successFunctionality
2016
iCONCUR: informed consent for clinical data and bio-sample use for research
Kim H, Bell E, Kim J, Sitapati A, Ramsdell J, Farcas C, Friedman D, Feupe S, Ohno-Machado L. iCONCUR: informed consent for clinical data and bio-sample use for research. Journal Of The American Medical Informatics Association 2016, 24: 380-387. PMID: 27589942, PMCID: PMC5391727, DOI: 10.1093/jamia/ocw115.Peer-Reviewed Original ResearchConceptsPatient preferencesClinical dataHuman immunodeficiency virus clinicInternal medicine clinicElectronic health record dataHealth record dataAcademic medical centerElectronic health recordsDe-identified dataOutpatient clinicMedicine clinicFamily historyMedical CenterInformed Consent ToolClinical settingClinicHealth recordsRecord dataInformed consent systemPatientsConsent toolRecipientsConsentE-consentParticipants
2013
Detecting inappropriate access to electronic health records using collaborative filtering
Menon A, Jiang X, Kim J, Vaidya J, Ohno-Machado L. Detecting inappropriate access to electronic health records using collaborative filtering. Machine Learning 2013, 95: 87-101. PMID: 24683293, PMCID: PMC3967851, DOI: 10.1007/s10994-013-5376-1.Peer-Reviewed Original ResearchElectronic health recordsCollaborative filteringInappropriate accessHealth recordsSuspicious accessPrivacy policiesAccess patternsMachine learningManual auditingSecurity expertsLatent featuresAccess dataRecord accessHistorical dataSecurityFilteringUnrestricted accessFuture violationsAccessAudit processSVMUsersDatasetLearningAuditing
2011
Using statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM