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
2019
Social Determinants of Health in the Digital Age
Abnousi F, Rumsfeld JS, Krumholz HM. Social Determinants of Health in the Digital Age. JAMA 2019, 321: 247-248. PMID: 30570663, DOI: 10.1001/jama.2018.19763.Peer-Reviewed Original Research
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort
2017
With Great Power Comes Great Responsibility
Khera R, Krumholz HM. With Great Power Comes Great Responsibility. Circulation Cardiovascular Quality And Outcomes 2017, 10: e003846. PMID: 28705865, PMCID: PMC5728376, DOI: 10.1161/circoutcomes.117.003846.Peer-Reviewed Original Research
2016
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circulation Cardiovascular Quality And Outcomes 2016, 9: 629-640. PMID: 28263938, PMCID: PMC5459389, DOI: 10.1161/circoutcomes.116.003039.Peer-Reviewed Original Research
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