2024
Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review
Lacson R, Yu Y, Kuo T, Ohno-Machado L. Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review. Journal Of The American Medical Informatics Association 2024, 31: 1423-1435. PMID: 38726710, PMCID: PMC11105130, DOI: 10.1093/jamia/ocae084.Peer-Reviewed Original ResearchConceptsElectronic health recordsSystematic reviewData sharingMedical data sharingSpeed metricsPreferred Reporting ItemsCertificate storageDecentralized InternetNetwork permissionsBlockchain platformBlockchain applicationsEvaluation metricsBiomedical domainBlockchainBiomedical data managementHealth recordsData managementData storageReporting ItemsHealth sectorQuantitative metricsMedical facilitiesMetricsTrial managementClinical trial management
2023
Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program.
Yeh H, Peltz-Rauchman C, Johnson C, Pawloski P, Chesla D, Waring S, Stevens A, Epstein M, Joseph C, Miller-Matero L, Gui H, Tang A, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Schully S, Ramirez A, Qian J, Ahmedani B. Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program. PLOS ONE 2023, 18: e0290416. PMID: 37594966, PMCID: PMC10437856, DOI: 10.1371/journal.pone.0290416.Peer-Reviewed Original ResearchConceptsOpioid use disorderOpioid usePrescription opioidsElectronic health recordsReduced oddsDiagnosis of OUDSociodemographic characteristicsPrevalence of OUDNonmedical useLifetime opioid useEHR dataNon-Hispanic white participantsImportant clinical informationNon-medical useLifetime prevalenceStreet opioidsHigher oddsOpioidsClinical informationUse disordersUs Research ProgramSociodemographic correlatesLogistic regressionPrevalenceHealth records
2022
Evaluation of available risk scores to predict multiple cardiovascular complications for patients with type 2 diabetes mellitus using electronic health records
Ho J, Staimez L, Narayan K, Ohno-Machado L, Simpson R, Hertzberg V. Evaluation of available risk scores to predict multiple cardiovascular complications for patients with type 2 diabetes mellitus using electronic health records. Computer Methods And Programs In Biomedicine Update 2022, 3: 100087. PMID: 37332899, PMCID: PMC10274317, DOI: 10.1016/j.cmpbup.2022.100087.Peer-Reviewed Original ResearchType 2 diabetes mellitusCardiovascular risk modelsElectronic health recordsDiabetes mellitusCardiovascular risk prediction modelsHealth recordsElectronic health record dataAvailable risk scoresMultiple cardiovascular complicationsType 2 diabetesHosmer-Lemeshow goodnessHealth record dataHosmer-Lemeshow statisticRisk prediction modelCardiovascular complicationsCardiovascular outcomesCardiovascular endpointsC-statisticRisk modelRisk scoreType 2PatientsSecondary analysisImpact of raceRecord dataInvestigation of hypertension and type 2 diabetes as risk factors for dementia in the All of Us cohort
Nagar S, Pemu P, Qian J, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Ramirez A, Schully S, Able A, Green A, Zuchner S, Jordan I, Meller R. Investigation of hypertension and type 2 diabetes as risk factors for dementia in the All of Us cohort. Scientific Reports 2022, 12: 19797. PMID: 36396674, PMCID: PMC9672061, DOI: 10.1038/s41598-022-23353-z.Peer-Reviewed Original ResearchConceptsAssociation of hypertensionPrevalence of dementiaType 2 diabetesRisk factorsUS populationRace/ethnicityHigh prevalenceLarge observational cohort studyMultivariable logistic regression modelRisk factor modificationObservational cohort studyT2D risk factorsInvestigation of hypertensionAssociation of T2DOdds of dementiaRace/ethnicity groupsAssociation of sexCross-sectional analysisLogistic regression modelsWorld Health OrganizationElectronic health recordsConcurrent hypertensionModifiable comorbiditiesCohort studyFinal cohortEffect of Obesity on Risk of Hospitalization, Surgery, and Serious Infection in Biologic-Treated Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study
Gu P, Luo J, Kim J, Paul P, Limketkai B, Sauk J, Park S, Parekh N, Zheng K, Rudrapatna V, Syal G, Ha C, McGovern D, Melmed G, Fleshner P, Eisenstein S, Ramamoorthy S, Dulai P, Boland B, Grunvald E, Mahadevan U, Ohno-Machado L, Sandborn W, Singh S. Effect of Obesity on Risk of Hospitalization, Surgery, and Serious Infection in Biologic-Treated Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study. The American Journal Of Gastroenterology 2022, 117: 1639-1647. PMID: 35973139, DOI: 10.14309/ajg.0000000000001855.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseBiologic-treated patientsRisk of hospitalizationBody mass indexNormal body mass indexSerious infectionsBiologic agentsBowel diseaseCox proportional hazards analysisWorld Health Organization classificationEffect of obesityProportional hazards analysisElectronic health recordsCause hospitalizationVisceral obesityAdult patientsBaseline demographicsBiologic initiationBiologic therapyCohort studyEndoscopic outcomesMass indexOrganization classificationTreatment characteristicsStratified analysis
2021
Geographic Variation in Obesity at the State Level in the All of Us Research Program
Clark C, Chandler P, Zhou G, Noel N, Achilike C, Mendez L, O’Connor G, Smoller J, Weiss S, Murphy S, Ommerborn M, Karnes J, Klimentidis Y, Jordan C, Hiatt R, Ramirez A, Loperena R, Mayo K, Cohn E, Ohno-Machado L, Boerwinkle E, Cicek M, Schully S, Mockrin S, Gebo K, Karlson E. Geographic Variation in Obesity at the State Level in the All of Us Research Program. Preventing Chronic Disease 2021, 18: e104. PMID: 34941480, PMCID: PMC8718125, DOI: 10.5888/pcd18.210094.Peer-Reviewed Original ResearchConceptsBody mass indexObesity prevalenceElectronic health recordsSevere obesityPrevalence estimatesParticipants' electronic health recordsObesity prevention strategiesNational population-based estimatesEHR dataPopulation-based estimatesOverall obesity prevalenceState-level geographic variationsPopulation health studiesUS participantsEnrollment visitMass indexObesity preventionHigh prevalenceHealth StudyPrecision health approachObesityPrevention strategiesUs Research ProgramLarge geographic variationsCohort dataRacial, ethnic, and gender differences in obesity and body fat distribution: An All of Us Research Program demonstration project
Karnes J, Arora A, Feng J, Steiner H, Sulieman L, Boerwinkle E, Clark C, Cicek M, Cohn E, Gebo K, Loperena-Cortes R, Ohno-Machado L, Mayo K, Mockrin S, Ramirez A, Schully S, Klimentidis Y. Racial, ethnic, and gender differences in obesity and body fat distribution: An All of Us Research Program demonstration project. PLOS ONE 2021, 16: e0255583. PMID: 34358277, PMCID: PMC8345840, DOI: 10.1371/journal.pone.0255583.Peer-Reviewed Original ResearchConceptsBody mass indexBody fat distributionNon-Hispanic blacksNon-Hispanic whitesAlanine aminotransferaseNHB participantsFat distributionAge-adjusted obesity prevalenceNutrition Examination Survey dataBaseline physical examinationRace/ethnicityElectronic health recordsNHB womenWaist circumferenceMass indexHip ratioNHB menPhysical examinationNational HealthObesity prevalenceObesity ratesGender differencesObesityUs Research ProgramNHW participants
2020
Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers
Madhavan S, Bastarache L, Brown J, Butte A, Dorr D, Embi P, Friedman C, Johnson K, Moore J, Kohane I, Payne P, Tenenbaum J, Weiner M, Wilcox A, Ohno-Machado L. Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers. Journal Of The American Medical Informatics Association 2020, 28: 393-401. PMID: 33260207, PMCID: PMC7665546, DOI: 10.1093/jamia/ocaa287.Commentaries, Editorials and LettersCOVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes
Dong X, Li J, Soysal E, Bian J, DuVall S, Hanchrow E, Liu H, Lynch K, Matheny M, Natarajan K, Ohno-Machado L, Pakhomov S, Reeves R, Sitapati A, Abhyankar S, Cullen T, Deckard J, Jiang X, Murphy R, Xu H. COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes. Journal Of The American Medical Informatics Association 2020, 27: 1437-1442. PMID: 32569358, PMCID: PMC7337837, DOI: 10.1093/jamia/ocaa145.Peer-Reviewed Original ResearchConceptsElectronic health recordsLOINC codesSecondary useRule-based toolOnline web applicationOpen-source packageCritical data elementsWeb applicationData networksEnd usersData elementsIndependent test setHealth recordsTest setKey challengesData normalizationCritical resourcesTest namesRoutine clinical practice dataCodeClinical practice dataCoronavirus disease 2019COVID-19 diagnostic testsToolDevelopersPromoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era
Baxter S, Gali H, Chiang M, Hribar M, Ohno-Machado L, El-Kareh R, Huang A, Chen H, Camp A, Kikkawa D, Korn B, Lee J, Longhurst C, Millen M. Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era. Applied Clinical Informatics 2020, 11: 130-141. PMID: 32074650, PMCID: PMC7030957, DOI: 10.1055/s-0040-1701255.Peer-Reviewed Original ResearchConceptsElectronic Health Record EraDocumentation efficiencyEHR implementationPhysician-patient interactionAcademic ophthalmology departmentsElectronic health recordsOutpatient ophthalmologyQuality improvement strategiesUse of scribesOutpatient encountersProspective studyOphthalmology departmentQI interventionsQI strategiesPatientsTeam-based workflowsEHR efficiencyNote templateArray of interventionsOphthalmologistsEHR useHealth recordsDocumentation timePhysician burnoutTablet-based application
2019
Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records
Baxter S, Marks C, Kuo T, Ohno-Machado L, Weinreb R. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. American Journal Of Ophthalmology 2019, 208: 30-40. PMID: 31323204, PMCID: PMC6888922, DOI: 10.1016/j.ajo.2019.07.005.Peer-Reviewed Original ResearchConceptsPrimary open-angle glaucomaElectronic health recordsMultivariable logistic regressionSurgical interventionGlaucoma surgeryPOAG patientsSystemic dataHigher mean systolic blood pressureMean systolic blood pressureNon-opioid analgesic medicationsLogistic regressionCertain medication classesEye-specific dataHealth recordsRisk of progressionSystolic blood pressureOpen-angle glaucomaSingle academic institutionAnti-hyperlipidemic medicationsAnalgesic medicationMedication classesProgressive diseaseBlood pressureCalcium blockersOphthalmic medicationsImpact of Electronic Health Record Implementation on Ophthalmology Trainee Time Expenditures
Gali H, Baxter S, Lander L, Huang A, Millen M, El-Kareh R, Nudleman E, Chao D, Robbins S, Heichel C, Camp A, Korn B, Lee J, Kikkawa D, Longhurst C, Chiang M, Hribar M, Ohno-Machado L. Impact of Electronic Health Record Implementation on Ophthalmology Trainee Time Expenditures. Journal Of Academic Ophthalmology 2019, 11: e65-e72. PMID: 33954272, PMCID: PMC8095731, DOI: 10.1055/s-0039-3401986.Peer-Reviewed Original ResearchTime-motion observationsElectronic health recordsOphthalmology traineesPatient encountersOphthalmology residentsEHR implementationEHR useMean total timeAcademic ophthalmology departmentsTime-motion studyElectronic health record implementationSubspecialty clinicsOphthalmology departmentClinical activityPatientsLinear mixed effects modelsEHR impactTotal timeTrainee burnoutPaper chartsHealth recordsDocumentation timeMixed effects modelsEffects modelWeeks
2018
Sharing data from electronic health records within, across, and beyond healthcare institutions: Current trends and perspectives
Ohno-Machado L. Sharing data from electronic health records within, across, and beyond healthcare institutions: Current trends and perspectives. Journal Of The American Medical Informatics Association 2018, 25: 1113-1113. PMID: 30184157, PMCID: PMC7646901, DOI: 10.1093/jamia/ocy116.Commentaries, Editorials and LettersGenomics 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
2017
Integrated precision medicine: the role of electronic health records in delivering personalized treatment
Sitapati A, Kim H, Berkovich B, Marmor R, Singh S, El‐Kareh R, Clay B, Ohno‐Machado L. Integrated precision medicine: the role of electronic health records in delivering personalized treatment. WIREs Mechanisms Of Disease 2017, 9 PMID: 28207198, PMCID: PMC5400726, DOI: 10.1002/wsbm.1378.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEnsembles of NLP Tools for Data Element Extraction from Clinical Notes.
Kuo T, Rao P, Maehara C, Doan S, Chaparro J, Day M, Farcas C, Ohno-Machado L, Hsu C. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes. AMIA Annual Symposium Proceedings 2017, 2016: 1880-1889. PMID: 28269947, PMCID: PMC5333200.Peer-Reviewed Original ResearchConceptsNatural language processingNLP toolsElectronic health recordsData elementsConcept extractionLanguage processingEnsemble methodDiverse conceptsEvaluation resultsHealth recordsElement extractionClinical notesPlausible solutionToolPipelineExtractionPerformanceEnsembleExtraction performanceConceptNarrative textProcessingText
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
2014
“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationDataBig Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014, 33: 1123-1131. PMID: 25006137, DOI: 10.1377/hlthaff.2014.0041.Commentaries, Editorials and LettersConceptsBig dataClinical analyticsPrivacy concernsUse casesElectronic health recordsAnalyticsTypes of dataHealth recordsTypes of insightsNecessary analysisSupport of researchHigh-cost patientsUnprecedented opportunityMonitoring devicesCostHealth careAlgorithmMultiple organ systemsRapid progressInfrastructureUS health care systemHealth care systemSystemAdverse eventsClinical data
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