2024
Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overConnecticutDeep LearningDocumentationElectronic Health RecordsFemaleFunctional StatusHeart FailureHumansMaleMiddle AgedNatural Language ProcessingROC CurveConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsUse of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntihypertensive AgentsBlood PressureElectronic Health RecordsFemaleHumansHypertensionMaleMiddle AgedRetrospective StudiesTime FactorsTreatment OutcomeUnited StatesConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurementsBarriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure
Lu Y, Arowojolu O, Qiu X, Liu Y, Curry L, Krumholz H. Barriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure. JAMA Network Open 2024, 7: e2426135. PMID: 39106065, PMCID: PMC11304113, DOI: 10.1001/jamanetworkopen.2024.26135.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntihypertensive AgentsElectronic Health RecordsFemaleGuideline AdherenceHumansHypertensionMaleMiddle AgedPractice Guidelines as TopicQualitative ResearchConceptsBarriers to guideline adherenceElectronic health recordsGuideline adherenceClinician adherenceEHR dataElevated blood pressureHypertension managementAnalysis of EHR dataYale New Haven Health SystemSevere hypertensionClinical practice guidelinesAdherence scenariosQualitative content analysisPublic health challengeThematic saturationHealth recordsHealth systemBlood pressureThematic analysisTargeted interventionsManagement of severe hypertensionQualitative studyHealth challengesPractice guidelinesPatient outcomesLearning implementation of a guideline based decision support system to improve hypertension treatment in primary care in China: pragmatic cluster randomised controlled trial
Song J, Wang X, Wang B, Ge Y, Bi L, Jing F, Jin H, Li T, Gu B, Wang L, Hao J, Zhao Y, Liu J, Zhang H, Li X, Li J, Ma W, Wang J, Normand S, Herrin J, Armitage J, Krumholz H, Zheng X. Learning implementation of a guideline based decision support system to improve hypertension treatment in primary care in China: pragmatic cluster randomised controlled trial. The BMJ 2024, 386: e079143. PMID: 39043397, PMCID: PMC11265211, DOI: 10.1136/bmj-2023-079143.Peer-Reviewed Original ResearchConceptsClinical decision support systemsPrimary care practicesElectronic health recordsIntervention groupSystolic blood pressurePrimary careCare practicesBlood pressure <Health recordsPragmatic cluster randomised controlled trialCluster randomised controlled trialImproving hypertension treatmentPrimary care settingBlood pressure control ratesBlood pressureProportion of visitsProportion of participantsRandomised controlled trialsSystolic blood pressure <Control groupInjurious fallsRelated visitsCare settingsDiastolic blood pressure <Follow-upHypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records
Brush J, Lu Y, Liu Y, Asher J, Li S, Sawano M, Young P, Schulz W, Anderson M, Burrows J, Krumholz H. Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records. Journal Of The American Heart Association 2024, 13: e033253. PMID: 38686864, PMCID: PMC11179912, DOI: 10.1161/jaha.123.033253.Peer-Reviewed Original ResearchConceptsElectronic health recordsRegional health systemImprove hypertension careHealth systemHealth recordsHypertension careDiastolic blood pressureAge-adjusted prevalence ratesNon-Hispanic Black patientsPrevalence ratesLarger health systemCross-sectional analysisTransformation of medical dataLeveraging real-world dataHigh prevalence rateHypertension trendsHypertension prevalenceBlood pressureBlood pressure measurementsHypertension diagnosisPrimary outcomeNational trendsProportion of patientsAntihypertensive medicationsBlack patients
2023
Foundation models for generalist medical artificial intelligence
Moor M, Banerjee O, Abad Z, Krumholz H, Leskovec J, Topol E, Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature 2023, 616: 259-265. PMID: 37045921, DOI: 10.1038/s41586-023-05881-4.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceDatasets as TopicDiagnostic ImagingElectronic Health RecordsGenomicsHumansMedicineUnsupervised Machine LearningConceptsMedical AILarge medical datasetsMedical artificial intelligenceArtificial intelligence modelsImage annotationMedical datasetsArtificial intelligenceElectronic health recordsAI devicesIntelligence modelsTraining datasetDiverse datasetsExpressive outputHealth recordsRapid developmentDatasetFree-text explanationsMedical modalitiesNew paradigmMedical textsAITechnical capabilitiesDiverse setNewfound capabilitiesCapabilityDeveloping Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsElectronic Health RecordsHumansInternational Classification of DiseasesPredictive Value of TestsPulmonary EmbolismReproducibility of ResultsConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithmDeveloping an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records
Lu Y, Du C, Khidir H, Caraballo C, Mahajan S, Spatz E, Curry L, Krumholz H. Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009453. PMID: 36727515, DOI: 10.1161/circoutcomes.122.009453.Peer-Reviewed Original ResearchMeSH KeywordsAgedAntihypertensive AgentsBlood PressureElectronic Health RecordsFemaleHumansHypertensionMaleMiddle AgedConceptsPersistent hypertensionElectronic health recordsBlood pressureHealth recordsPharmacologic agentsPrescribed treatmentYale New Haven Health SystemTreatment planAdditional pharmacologic agentsAntihypertensive treatment intensificationConsecutive outpatient visitsElevated blood pressurePersistence of hypertensionElectronic health record dataHealth record dataEligible patientsTreatment intensificationChart reviewHispanic patientsOutpatient visitsMean agePharmacological treatmentConventional content analysisHypertensionClinician notes
2022
Sensible regulation and clinical implementation of clinical decision support software as a medical device
Mori M, Jarrin R, Lu Y, Kadakia K, Huang C, Ross JS, Krumholz HM. Sensible regulation and clinical implementation of clinical decision support software as a medical device. The BMJ 2022, 376: o525. PMID: 35228206, DOI: 10.1136/bmj.o525.Commentaries, Editorials and Letters
2021
Implementing Measurement Science for Electronic Health Record Use
Melnick ER, Sinsky CA, Krumholz HM. Implementing Measurement Science for Electronic Health Record Use. JAMA 2021, 325: 2149-2150. PMID: 33818587, DOI: 10.1001/jama.2021.5487.Peer-Reviewed Original ResearchMeSH KeywordsBenchmarkingElectronic Health RecordsFemaleHumansMalePhysiciansQuality ImprovementWorkloadTemporal relationship of computed and structured diagnoses in electronic health record data
Schulz WL, Young HP, Coppi A, Mortazavi BJ, Lin Z, Jean RA, Krumholz HM. Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics And Decision Making 2021, 21: 61. PMID: 33596898, PMCID: PMC7890604, DOI: 10.1186/s12911-021-01416-x.Peer-Reviewed Original ResearchMeSH KeywordsDiabetes MellitusElectronic Health RecordsHumansHypertensionInformation Storage and RetrievalOutpatientsConceptsElectronic health recordsStructured diagnosisOutpatient blood pressureElectronic health record dataAcademic health systemLow-density lipoproteinHealth record dataBlood pressureStructured data elementsAdministrative claimsHypertensionClinical informationHyperlipidemiaClinical phenotypeEquivalent diagnosisVital signsHealth systemDiagnosisProblem listAdditional studiesHealth recordsRecord dataTimely accessEHR dataPatients
2020
Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure
Lu Y, Huang C, Mahajan S, Schulz WL, Nasir K, Spatz ES, Krumholz HM. Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure. Journal Of The American Heart Association 2020, 9: e015033. PMID: 32200730, PMCID: PMC7428633, DOI: 10.1161/jaha.119.015033.Peer-Reviewed Original ResearchConceptsDiastolic blood pressureSystolic blood pressureElevated blood pressureBlood pressureElectronic health recordsPopulation health surveillanceHealth recordsYale New Haven Health SystemHealth surveillanceHealth systemPatterns of patientsLarge health systemUsual careOutpatient encountersControl ratePatientsCare patternsPopulation healthMonthsHgSurveillancePrevalenceRecordsVisitsCare
2018
The 21st Century Cures Act and electronic health records one year later: will patients see the benefits?
Lye CT, Forman HP, Daniel JG, Krumholz HM. The 21st Century Cures Act and electronic health records one year later: will patients see the benefits? Journal Of The American Medical Informatics Association 2018, 25: 1218-1220. PMID: 30184156, PMCID: PMC7646899, DOI: 10.1093/jamia/ocy065.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth information technologyInformation exchange networkHealth information exchangeData sharingHealth information exchange networkInformation technologyCentury Cures ActInformation exchangeInteroperabilityHealth dataCures ActImplementationAccessResearch initiativesRequirementsExchange networksSharingCertification requirementsNetworkTechnologyPotential benefitsRules
2017
Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization
Schwartz J, Wang Y, Qin L, Schwamm LH, Fonarow GC, Cormier N, Dorsey K, McNamara RL, Suter LG, Krumholz HM, Bernheim SM. Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization. Stroke 2017, 48: 3101-3107. PMID: 28954922, DOI: 10.1161/strokeaha.117.017960.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesElectronic health record dataHealth record dataStroke severityClaims dataMortality rateAmerican Heart Association/American Stroke AssociationHealth Stroke Scale scoreRisk variablesMedicaid ServicesRisk adjustmentMedian risk-standardized mortality rateGuidelines-Stroke registryLow-mortality hospitalsStroke Scale scoreAcute ischemic strokeAmerican Stroke AssociationOdds of mortalityMortality measuresRecord dataIschemic stroke hospitalizationsHigh-mortality hospitalsService claims dataRisk-adjustment variablesHospital admissionBlockchain Technology
Angraal S, Krumholz HM, Schulz WL. Blockchain Technology. Circulation Cardiovascular Quality And Outcomes 2017, 10: e003800. PMID: 28912202, DOI: 10.1161/circoutcomes.117.003800.Peer-Reviewed Original ResearchCopy Fees and Limitation of Patients’ Access to Their Own Medical Records
Jaspers AW, Cox JL, Krumholz HM. Copy Fees and Limitation of Patients’ Access to Their Own Medical Records. JAMA Internal Medicine 2017, 177: 457-458. PMID: 28135350, DOI: 10.1001/jamainternmed.2016.8560.Peer-Reviewed Original ResearchPrediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures
Mortazavi B, Desai N, Zhang J, Coppi A, Warner F, Krumholz H, Negahban S. Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE Journal Of Biomedical And Health Informatics 2017, 21: 1719-1729. PMID: 28287993, DOI: 10.1109/jbhi.2017.2675340.Peer-Reviewed Original ResearchMeSH KeywordsCardiac Surgical ProceduresElectronic Health RecordsHumansMachine LearningModels, StatisticalPostoperative ComplicationsConceptsMajor cardiovascular proceduresElectronic health recordsRespiratory failureAdverse eventsCardiovascular proceduresYale-New Haven HospitalPostoperative respiratory failurePatient cohortHospital costsPatient outcomesSpecific patientPatientsHealth recordsCohort-specific modelsCharacteristic curveInfectionFailureHospitalCohortClinicians
2015
Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records
McNamara RL, Wang Y, Partovian C, Montague J, Mody P, Eddy E, Krumholz HM, Bernheim SM. Development of a Hospital Outcome Measure Intended for Use With Electronic Health Records. Medical Care 2015, 53: 818-826. PMID: 26225445, DOI: 10.1097/mlr.0000000000000402.Peer-Reviewed Original ResearchConceptsElectronic health recordsOutcome measuresClinical dataMortality rateClinical practiceFuture quality improvement measuresRisk-standardized mortality ratesHospital risk-standardized mortality ratesLow-mortality hospitalsHealth recordsSystolic blood pressureOdds of mortalityClinical registry dataAcute myocardial infarctionHigh-mortality hospitalsHospital outcome measuresEHR dataFinal risk modelCurrent clinical practiceStandard clinical practiceFirst outcome measureNational Quality ForumCurrent electronic health recordsQuality improvement measuresChart abstraction