2020
Burden and Outcomes of Fragmentation of Care in Hospitalized Patients With Inflammatory Bowel Diseases: A Nationally Representative Cohort
Nguyen N, Luo J, Ohno-Machado L, Sandborn W, Singh S. Burden and Outcomes of Fragmentation of Care in Hospitalized Patients With Inflammatory Bowel Diseases: A Nationally Representative Cohort. Inflammatory Bowel Diseases 2020, 27: 1026-1034. PMID: 32944753, PMCID: PMC8205632, DOI: 10.1093/ibd/izaa238.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseFragmentation of careHigh riskCohort studyBowel diseaseNationwide Readmissions Database 2013Nationally Representative CohortUS cohort studyRisk of surgeryNumber of patientsRepresentative cohort studyHealth care qualityHospital mortalityHospitalized patientsCohort 2Outpatient careChronic diseasesCohort 1Representative cohortPatientsSingle episodeReadmissionHospitalizationCare qualityCareEXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning
Kuo T, Gabriel R, Cidambi K, Ohno-Machado L. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. Journal Of The American Medical Informatics Association 2020, 27: 747-756. PMID: 32364235, PMCID: PMC7309256, DOI: 10.1093/jamia/ocaa023.Peer-Reviewed Original ResearchConceptsBlockchain technologyCentral serverServer-based methodBenefits of blockchainData protection policiesCentralized serverArtificial intelligenceModel learningDecentralized approachSmall datasetsBlockchainServerComputation strategySingle pointGeneralizable modelCost of efficiencyGenomic datasetsDatasetDistributed modelTechnologyGenomic dataMultiple institutionsDiscrimination powerIntelligencePotential advantages/disadvantages
2019
Comparison of blockchain platforms: a systematic review and healthcare examples
Kuo T, Rojas H, Ohno-Machado L. Comparison of blockchain platforms: a systematic review and healthcare examples. Journal Of The American Medical Informatics Association 2019, 26: 462-478. PMID: 30907419, PMCID: PMC7787359, DOI: 10.1093/jamia/ocy185.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBlockchain platformPopular blockchain platformsBlockchain applicationsSuitable blockchain platformDifferent blockchain platformsClinical informatics researchersTechnical featuresSoftware engineersHealth informatics applicationsHealthcare applicationsInformatics applicationsHealthcare exampleInformatics researchersPlatformBiomedical research applicationsHealthcareBlockchainImportant technical featuresKey featuresApplicationsResearch applicationsFeaturesBrief introductionTechnologyRequirements
2017
Advancing healthcare and biomedical research via new data-driven approaches
Ohno-Machado L. Advancing healthcare and biomedical research via new data-driven approaches. Journal Of The American Medical Informatics Association 2017, 24: 471-471. PMID: 28403381, PMCID: PMC7651977, DOI: 10.1093/jamia/ocx036.Commentaries, Editorials and Letters
2016
Building systems that change clinical practice and advance health sciences research
Ohno-Machado L, Editor-in-Chief. Building systems that change clinical practice and advance health sciences research. Journal Of The American Medical Informatics Association 2016, 23: 857-857. PMID: 27562746, DOI: 10.1093/jamia/ocw129.Commentaries, Editorials and Letters
2015
Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership
FitzHenry F, Resnic F, Robbins S, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny M. Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Applied Clinical Informatics 2015, 06: 536-547. PMID: 26448797, PMCID: PMC4586341, DOI: 10.4338/aci-2014-12-cr-0121.Peer-Reviewed Original ResearchMeSH KeywordsCommon Data ElementsComparative Effectiveness ResearchDatabases, FactualDelivery of Health CareFemaleHumansMaleOutcome Assessment, Health CareConceptsOMOP Common Data ModelPerson monthsHealth systemCount of medicationsMore outpatient visitsHealth system databaseMedication administration recordsComparative effectiveness analysisObservational Medical Outcomes Partnership (OMOP) CDMPharmacy fillsEligible cohortOutpatient visitsHigh prevalenceMedicationsComparative effectivenessAdministration recordsCommon data modelCohortMini-SentinelObservational Medical Outcomes PartnershipMonths
2014
Big 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 LettersMeSH KeywordsData MiningDatasets as TopicDelivery of Health CareDisease ManagementElectronic Health RecordsHumansRisk FactorsTriageUnited StatesConceptsBig 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
Game changer: how informatics moved from a supporting role to a central position in healthcare
Ohno-Machado L. Game changer: how informatics moved from a supporting role to a central position in healthcare. Journal Of The American Medical Informatics Association 2013, 20: e197-e197. PMID: 24302668, PMCID: PMC3861937, DOI: 10.1136/amiajnl-2013-002434.Commentaries, Editorials and Letters