2024
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers
Dhingra L, Sangha V, Aminorroaya A, Bryde R, Gaballa A, Ali A, Mehra N, Krumholz H, Sen S, Kramer C, Martinez M, Desai M, Oikonomou E, Khera R. A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers. The American Journal Of Cardiology 2024 PMID: 39581517, DOI: 10.1016/j.amjcard.2024.11.028.Peer-Reviewed Original ResearchCleveland Clinic FoundationHypertrophic cardiomyopathyMedian follow-up periodHypertrophic cardiomyopathy therapyMonitoring treatment responseFollow-up periodImpact of therapyAtlantic Health SystemLack of improvementOral alternativePost-SRTMedical therapyTreatment responseMulticenter evaluationInterventricular septumPercutaneous reductionMavacamtenTherapyPatientsClinic FoundationPoint-of-care monitoringECGECG imagesScoresHealth systemValidating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism
Bikdeli B, Khairani C, Bejjani A, Lo Y, Mahajan S, Caraballo C, Jimenez J, Krishnathasan D, Zarghami M, Rashedi S, Jimenez D, Barco S, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Mojibian H, Aneja S, Khera R, Konstantinides S, Goldhaber S, Wang L, Zhou L, Monreal M, Piazza G, Krumholz H, Investigators P. Validating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism. Journal Of Thrombosis And Haemostasis 2024 PMID: 39505153, DOI: 10.1016/j.jtha.2024.10.013.Peer-Reviewed Original ResearchDischarge codesInternational ClassificationICD-10Yale New Haven Health SystemPositive predictive valueMass General Brigham hospitalsAccuracy of ICD-10ICD-10 codesPulmonary embolismHealth systemImage codingElectronic databasesF1 scorePre-specified protocolExcellent positive predictive valueIndependent physiciansHighest F1 scoreIdentification of pulmonary embolismAcute pulmonary embolismSecondary codePE codesScoresIdentified PERevised algorithmUse 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 ResearchConceptsElectronic 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 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 outcomesHypertension 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 patientsReal-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
Oikonomou E, Aminorroaya A, Dhingra L, Partridge C, Velazquez E, Desai N, Krumholz H, Miller E, Khera R. Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study. European Heart Journal - Digital Health 2024, 5: 303-313. PMID: 38774380, PMCID: PMC11104476, DOI: 10.1093/ehjdh/ztae023.Peer-Reviewed Original ResearchRisk of acute myocardial infarctionAssociated with lower oddsHospital health systemCoronary artery diseaseCardiac testingRisk of adverse outcomesUK BiobankHealth systemProvider-drivenLower oddsAssociated with better outcomesAcute myocardial infarctionBlack raceStable chest painFemale sexReal world evaluationDiabetes historyMulticohort studyFunction testsSuspected coronary artery diseaseYounger ageRisk profileAdverse outcomesMultinational cohortPost hoc analysis
2023
Developing 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 ResearchConceptsElectronic 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 reportsAlgorithm
2022
12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here
Krumholz HM. 12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here. Yonsei Medical Journal 2022, 63: 493-498. PMID: 35512753, PMCID: PMC9086693, DOI: 10.3349/ymj.2022.63.5.493.Peer-Reviewed Original Research
2021
Multicentre methodological study to create a publicly available score of hospital financial standing in the USA
Zinoviev R, Krumholz HM, Ciccarone R, Antle R, Forman HP. Multicentre methodological study to create a publicly available score of hospital financial standing in the USA. BMJ Open 2021, 11: e046500. PMID: 34301654, PMCID: PMC8311305, DOI: 10.1136/bmjopen-2020-046500.Peer-Reviewed Original ResearchConceptsCredit ratingsFinancial dataBond ratingsMoody’s credit ratingsHospital's financial standingHospital financial dataFinancial literatureFinancial stabilityFinancial indicatorsFinancial scoresFinancial trendsFinancial healthFinancial standingFinancial metricsGreater shareMedicare dischargesWeighted variablesShareHospital operationsUnique variablesSingle composite scoreVariablesMoodyHealth systemModelTemporal 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 ResearchConceptsElectronic 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
The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers.
Mori M, Khera R, Lin Z, Ross JS, Schulz W, Krumholz HM. The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers. Methodist DeBakey Cardiovascular Journal 2020, 16: 212-219. PMID: 33133357, PMCID: PMC7587314, DOI: 10.14797/mdcj-16-3-212.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsLearning health systemLearning systemCommon data modelDynamic learning systemAdvanced analyticsBig dataData assetsData modelDigital solutionsCustomer interactionContinuous learningKnowledge generationEffective useConceptual modelAnalyticsSystemGoogleHealth systemLearningComparable scaleModelDataCompaniesQuality of primary health care in China: challenges and recommendations
Li X, Krumholz HM, Yip W, Cheng KK, De Maeseneer J, Meng Q, Mossialos E, Li C, Lu J, Su M, Zhang Q, Xu DR, Li L, Normand ST, Peto R, Li J, Wang Z, Yan H, Gao R, Chunharas S, Gao X, Guerra R, Ji H, Ke Y, Pan Z, Wu X, Xiao S, Xie X, Zhang Y, Zhu J, Zhu S, Hu S. Quality of primary health care in China: challenges and recommendations. The Lancet 2020, 395: 1802-1812. PMID: 32505251, PMCID: PMC7272159, DOI: 10.1016/s0140-6736(20)30122-7.Peer-Reviewed Original ResearchConceptsPrimary health care systemHealth care systemPrimary health carePublic health servicesClinical careHealth servicesPrimary health care physiciansPrimary health care practitionersPrimary health care institutionsBasic public health servicesHealth care physiciansHealth careCoronavirus disease 2019Entire health care systemHealth care practitionersHigh-value careLearning health systemChronic diseasesDisease 2019Health care institutionsInfectious diseasesHealth systemCare systemCareSuboptimal educationAgile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform
Schulz WL, Durant T, Torre CJ, Hsiao AL, Krumholz HM. Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform. Journal Of Medical Internet Research 2020, 22: e18707. PMID: 32442130, PMCID: PMC7257473, DOI: 10.2196/18707.Peer-Reviewed Original ResearchConceptsReal-time dataHealth information technologyReal-world dataHealth platformInformation technologyCombination of technologiesReal timeSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2Timely informationRespiratory syndrome coronavirus 2PlatformRespiratory tract infectionsSyndrome coronavirus 2Health care systemTract infectionsCoronavirus disease (COVID-19) outbreakIncident casesCoronavirus 2Novel applicationTechnologyAnalyticsHealth systemCare systemSpecific pathogensLeveraging 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 healthMonthsHgSurveillancePrevalenceRecordsVisitsCareCardiovascular Considerations for Patients, Health Care Workers, and Health Systems During the COVID-19 Pandemic
Driggin E, Madhavan MV, Bikdeli B, Chuich T, Laracy J, Biondi-Zoccai G, Brown TS, Der Nigoghossian C, Zidar DA, Haythe J, Brodie D, Beckman JA, Kirtane AJ, Stone GW, Krumholz HM, Parikh SA. Cardiovascular Considerations for Patients, Health Care Workers, and Health Systems During the COVID-19 Pandemic. Journal Of The American College Of Cardiology 2020, 75: 2352-2371. PMID: 32201335, PMCID: PMC7198856, DOI: 10.1016/j.jacc.2020.03.031.Peer-Reviewed Original ResearchConceptsHealth care workersCare workersCardiovascular ConsiderationsCardiovascular careCOVID-19Severe acute respiratory syndrome coronavirus 2Pre-existing cardiovascular diseaseAcute respiratory syndrome coronavirus 2Health systemRespiratory syndrome coronavirus 2Indirect cardiovascular complicationsCardiovascular side effectsAcute myocardial injurySyndrome coronavirus 2Coronavirus disease 2019Cardiovascular complicationsVenous thromboembolismMyocardial injuryCoronavirus 2Cardiovascular diseaseCardiovascular conditionsDisease 2019Severe diseaseSide effectsRapid triage
2018
Impact of Cost Display on Ordering Patterns for Hospital Laboratory and Imaging Services
Silvestri MT, Xu X, Long T, Bongiovanni T, Bernstein SL, Chaudhry SI, Silvestri JI, Stolar M, Greene EJ, Dziura JD, Gross CP, Krumholz HM. Impact of Cost Display on Ordering Patterns for Hospital Laboratory and Imaging Services. Journal Of General Internal Medicine 2018, 33: 1268-1275. PMID: 29845468, PMCID: PMC6082197, DOI: 10.1007/s11606-018-4495-6.Peer-Reviewed Original ResearchConceptsDecreased oddsImaging ordersHealth care servicesBehalf of patientsKey ResultsDuringMain MeasuresOutcomesParticipantsAll patientsHospital encountersImaging testsObservation encountersCare servicesHealth servicesMedicare fee scheduleHealth systemImaging costsDecreased numberHospital laboratoriesLab ordersStudy periodHospital labPatients
2016
Data Acquisition, Curation, and Use for a Continuously Learning Health System: A Vital Direction for Health and Health Care
Krumholz H, Bourne P, Kuntz R, Paz H, Terry S, Waldstreicher J. Data Acquisition, Curation, and Use for a Continuously Learning Health System: A Vital Direction for Health and Health Care. NAM Perspectives 2016, 6 DOI: 10.31478/201609w.Peer-Reviewed Original Research
2015
Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data
Horwitz LI, Grady JN, Cohen DB, Lin Z, Volpe M, Ngo CK, Masica AL, Long T, Wang J, Keenan M, Montague J, Suter LG, Ross JS, Drye EE, Krumholz HM, Bernheim SM. Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data. Journal Of Hospital Medicine 2015, 10: 670-677. PMID: 26149225, PMCID: PMC5459369, DOI: 10.1002/jhm.2416.Peer-Reviewed Original ResearchConceptsSame-hospital readmissionsNegative predictive valuePositive predictive valuePredictive valueReadmission measuresHospital-wide readmission measureGold standard chart reviewAdministrative claims-based algorithmDiagnostic cardiac catheterizationClaims-based algorithmLarge teaching centersAcute care hospitalsSmall community hospitalUnplanned readmissionChart reviewCardiac catheterizationScheduled careSpecificity 96.5Community hospitalReadmissionClaims dataCardiac devicesHealth systemTeaching centerPublic reporting
2014
Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System
Krumholz HM. Big Data And New Knowledge In Medicine: The Thinking, Training, And Tools Needed For A Learning Health System. Health Affairs 2014, 33: 1163-1170. PMID: 25006142, PMCID: PMC5459394, DOI: 10.1377/hlthaff.2014.0053.Peer-Reviewed Original ResearchConceptsBig dataNext-generation analyticsHealth care dataNew data sourcesAdvanced analyticsData meaningLearning health systemInformation needsUnmet information needsMassive quantitiesData sourcesHealth policy makersComplexity of patientsAnalyticsLearning health care systemComparative effectiveness researchHealth care systemPopulation health researchKnowledge generationCare dataHealth systemPatientsCare systemEffectiveness researchHealth research
2010
Implementation of a Registry for Acute Coronary Syndrome in Resource-Limited Settings: Barriers and Opportunities
Safavi K, Linnander EL, Allam AA, Bradley EH, Krumholz HM. Implementation of a Registry for Acute Coronary Syndrome in Resource-Limited Settings: Barriers and Opportunities. Asia Pacific Journal Of Public Health 2010, 22: 90s-95s. PMID: 20566539, DOI: 10.1177/1010539510373017.Peer-Reviewed Original ResearchConceptsAcute coronary syndromeCardiovascular diseaseCoronary syndromeHealth systemClinical Outcomes RegistryEvidence-based careCause of deathResource limited settingsResource-limited settingsWorld health systemsQuality improvement activitiesMiddle-income countriesOutcomes RegistryHigh-quality treatmentBlame-free cultureClinical registryRegistrySyndromeImprovement activitiesHospitalDiseaseSetting