Featured Publications
Hospital-Readmission Risk — Isolating Hospital Effects from Patient Effects
Krumholz HM, Wang K, Lin Z, Dharmarajan K, Horwitz LI, Ross JS, Drye EE, Bernheim SM, Normand ST. Hospital-Readmission Risk — Isolating Hospital Effects from Patient Effects. New England Journal Of Medicine 2017, 377: 1055-1064. PMID: 28902587, PMCID: PMC5671772, DOI: 10.1056/nejmsa1702321.Peer-Reviewed Original ResearchConceptsRisk-standardized readmission ratesReadmission ratesObserved readmission ratesSimilar diagnosesHospital effectsDifferent hospitalsHospital readmission performanceRate of readmissionHospital readmission ratesLower readmission ratesStudy sampleYears of ageSignificant differencesMultiple admissionsReadmission outcomesOnly significant differencePatient effectsSame patientMedicare recipientsPatientsReadmission performanceRisk-standardized hospital readmission ratesHospitalHospital qualityQuartileAccounting For Patients’ Socioeconomic Status Does Not Change Hospital Readmission Rates
Bernheim SM, Parzynski CS, Horwitz L, Lin Z, Araas MJ, Ross JS, Drye EE, Suter LG, Normand SL, Krumholz HM. Accounting For Patients’ Socioeconomic Status Does Not Change Hospital Readmission Rates. Health Affairs 2016, 35: 1461-1470. PMID: 27503972, PMCID: PMC7664840, DOI: 10.1377/hlthaff.2015.0394.Peer-Reviewed Original ResearchConceptsHospital Readmissions Reduction ProgramPatients' socioeconomic statusMedicare's Hospital Readmissions Reduction ProgramLow socioeconomic statusReadmission ratesSocioeconomic statusRisk-standardized readmission ratesHospital readmission ratesReadmissions Reduction ProgramMedicaid Services methodologyReadmission measuresHospital resultsPatientsHospitalSuch hospitalsPayment penaltiesReduction programsStatusCurrent CentersLower proportionLarge proportionPercentAdjustmentProportionRelationship Between Hospital Readmission and Mortality Rates for Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia
Krumholz HM, Lin Z, Keenan PS, Chen J, Ross JS, Drye EE, Bernheim SM, Wang Y, Bradley EH, Han LF, Normand SL. Relationship Between Hospital Readmission and Mortality Rates for Patients Hospitalized With Acute Myocardial Infarction, Heart Failure, or Pneumonia. JAMA 2013, 309: 587-593. PMID: 23403683, PMCID: PMC3621028, DOI: 10.1001/jama.2013.333.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesAcute myocardial infarctionRisk-standardized readmission ratesHospital risk-standardized mortality ratesHeart failureMyocardial infarctionHospital characteristicsMortality rateReadmission ratesProportion of hospitalsHospital readmissionMedicare feePneumoniaInfarctionService beneficiariesHospitalPatientsMedicaid ServicesHospital performanceSubgroupsFailureCauseReadmissionSignificant negative linear relationshipAn Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure
Keenan PS, Normand SL, Lin Z, Drye EE, Bhat KR, Ross JS, Schuur JD, Stauffer BD, Bernheim SM, Epstein AJ, Wang Y, Herrin J, Chen J, Federer JJ, Mattera JA, Wang Y, Krumholz HM. An Administrative Claims Measure Suitable for Profiling Hospital Performance on the Basis of 30-Day All-Cause Readmission Rates Among Patients With Heart Failure. Circulation Cardiovascular Quality And Outcomes 2008, 1: 29-37. PMID: 20031785, DOI: 10.1161/circoutcomes.108.802686.Peer-Reviewed Original ResearchConceptsRisk-standardized readmission ratesCause readmission rateReadmission ratesHeart failureHospital-level readmission ratesAdjusted readmission ratesAdministrative Claims MeasureUnadjusted readmission ratesHeart failure patientsHospital risk-standardized readmission ratesMedical record dataProfiling Hospital PerformanceHierarchical logistic regression modelsUse of MedicareMedical record modelNational Quality ForumLogistic regression modelsCause readmissionClaims-based modelsHospital dischargeFailure patientsC-statisticPreventable eventsPatientsQuality Forum
2024
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing
Nargesi A, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni G, Lin Z, Ahmad F, Krumholz H, Khera R. Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing. JACC Heart Failure 2024 PMID: 39453355, DOI: 10.1016/j.jchf.2024.08.012.Peer-Reviewed Original ResearchReduced ejection fractionEjection fractionHeart failureLeft ventricular ejection fractionVentricular ejection fractionYale-New Haven HospitalIdentification of patientsCommunity hospitalIdentification of heart failureLanguage modelNorthwestern MedicineMeasure care qualityQuality of careNew Haven HospitalDeep learning-based natural language processingHFrEFGuideline-directed careDeep learning language modelsMIMIC-IIIDetect HFrEFNatural language processingReclassification improvementHospital dischargePatientsCare qualityMeasuring Equity in Readmission as a Distinct Assessment of Hospital Performance
Nash K, Weerahandi H, Yu H, Venkatesh A, Holaday L, Herrin J, Lin Z, Horwitz L, Ross J, Bernheim S. Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance. JAMA 2024, 331: 111-123. PMID: 38193960, PMCID: PMC10777266, DOI: 10.1001/jama.2023.24874.Peer-Reviewed Original ResearchConceptsBlack patientsPatient populationHospital characteristicsHospital-wide readmission measureDual-eligible patientsHospital patient populationCross-sectional studyMeasures of hospitalHealth care qualityPatient demographicsReadmission ratesClinical outcomesPatient raceEligible hospitalsReadmissionMAIN OUTCOMEReadmission measuresMedicare dataUS hospitalsHospitalCare qualityPatientsMedicaid ServicesOutcomesLower percentage
2021
Temporal 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
2019
Substantial Differences Between Cohorts of Patients Hospitalized With Heart Failure in Canada and the United States
Lin Z, Li SX. Substantial Differences Between Cohorts of Patients Hospitalized With Heart Failure in Canada and the United States. JAMA Cardiology 2019, 4: 1178-1179. PMID: 31532467, DOI: 10.1001/jamacardio.2019.3314.Peer-Reviewed Original Research
2018
Defining Multiple Chronic Conditions for Quality Measurement
Drye EE, Altaf FK, Lipska KJ, Spatz ES, Montague JA, Bao H, Parzynski CS, Ross JS, Bernheim SM, Krumholz HM, Lin Z. Defining Multiple Chronic Conditions for Quality Measurement. Medical Care 2018, 56: 193-201. PMID: 29271820, DOI: 10.1097/mlr.0000000000000853.Peer-Reviewed Original ResearchConceptsMultiple chronic conditionsChronic conditionsMedicare feeService beneficiariesMedicare Chronic Conditions WarehouseMCC cohortBroad cohortChronic Conditions WarehouseRisk-standardized ratesNational quality measuresUnplanned admissionsFinal cohortTotal admissionsAdmission riskAccountable care organizationsAdmission ratesOutcome measuresAdmissionCohortCohort conditionCare organizationsPatientsStakeholder inputNarrow cohortBeneficiaries
2017
ADMISSION TYPES AMONG PATIENTS WITH HEART FAILURE CARED FOR BY ACCOUNTABLE CARE ORGANIZATIONS: VARIATION BY PERFORMANCE ON A MEASURE OF RISK STANDARDIZED ACUTE ADMISSION RATES
Benchetrit L, Zimmerman C, Bao H, Dharmarajan K, Atlaf F, Herrin J, Lin Z, Krumholz H, Drye E, Lipska K, Spatz E. ADMISSION TYPES AMONG PATIENTS WITH HEART FAILURE CARED FOR BY ACCOUNTABLE CARE ORGANIZATIONS: VARIATION BY PERFORMANCE ON A MEASURE OF RISK STANDARDIZED ACUTE ADMISSION RATES. Journal Of The American College Of Cardiology 2017, 69: 762. DOI: 10.1016/s0735-1097(17)34151-7.Peer-Reviewed Original Research
2015
Association of hospital volume with readmission rates: a retrospective cross-sectional study
Horwitz LI, Lin Z, Herrin J, Bernheim S, Drye EE, Krumholz HM, Hines HJ, Ross JS. Association of hospital volume with readmission rates: a retrospective cross-sectional study. The BMJ 2015, 350: h447. PMID: 25665806, PMCID: PMC4353286, DOI: 10.1136/bmj.h447.Peer-Reviewed Original ResearchConceptsReadmission ratesHospital volumeRetrospective cross-sectional studyUS acute care hospitalsHospital readmission ratesAcute care hospitalsCross-sectional studyMedical cancer treatmentCare hospitalAdult dischargesHospital characteristicsMedicare feeCancer treatmentHospitalAssociationDaysService dataPatientsCardiovascularGynecologyQuintileNeurology
2010
Telemonitoring in Patients with Heart Failure
Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, Phillips CO, Hodshon BV, Cooper LS, Krumholz HM. Telemonitoring in Patients with Heart Failure. New England Journal Of Medicine 2010, 363: 2301-2309. PMID: 21080835, PMCID: PMC3237394, DOI: 10.1056/nejmoa1010029.Peer-Reviewed Original ResearchConceptsPrimary end pointUsual care groupSecondary end pointsHeart failureEnd pointHeart failure outcomesNumber of hospitalizationsTelephone-based interactive voice response systemUsual careAdverse eventsPatient's clinicianMedian ageCare groupLarge trialsInteractive voice response systemPatientsSmall studyVoice response systemNumber of daysHospitalizationReadmissionTelemonitoringSignificant differencesCliniciansDeath
2007
Changes in outcomes for internal medicine inpatients after work-hour regulations.
Horwitz LI, Kosiborod M, Lin Z, Krumholz HM. Changes in outcomes for internal medicine inpatients after work-hour regulations. Annals Of Internal Medicine 2007, 147: 97-103. PMID: 17548401, DOI: 10.7326/0003-4819-147-2-200707170-00163.Peer-Reviewed Original ResearchConceptsIntensive care unit utilizationLength of stayDrug-drug interactionsWork-hour regulationsNonteaching servicesHospital deathPharmacist interventionsReadmission ratesConsecutive patientsRetrospective cohort studyInternal medicine patientsInternal medicine inpatientsUnit utilizationAdverse drug-drug interactionsTeaching serviceAcademic medical centerCohort studyDischarge dispositionMedicine inpatientsMedicine patientsFatigue-related errorsMedical CenterRehabilitation facilityRate of dischargePatients
2005
A Randomized Outpatient Trial of a Decision-Support Information Technology Tool
Apkon M, Mattera JA, Lin Z, Herrin J, Bradley EH, Carbone M, Holmboe ES, Gross CP, Selter JG, Rich AS, Krumholz HM. A Randomized Outpatient Trial of a Decision-Support Information Technology Tool. JAMA Internal Medicine 2005, 165: 2388-2394. PMID: 16287768, DOI: 10.1001/archinte.165.20.2388.Peer-Reviewed Original ResearchMeSH KeywordsAdultAmbulatory CareAttitude of Health PersonnelCost-Benefit AnalysisDecision Support Systems, ClinicalFemaleFloridaHealth ResourcesHospitals, MilitaryHumansKentuckyMaleMass ScreeningMultivariate AnalysisOutcome and Process Assessment, Health CarePatient SatisfactionPreventive MedicineQuality of Health CareConceptsProvider satisfactionAmbulatory clinic visitsUsual care patientsDays of enrollmentQuality process measuresQuality of careProportion of opportunitiesUsual careClinic visitsOutpatient trialSecondary outcomesPrimary outcomeAcute carePatient satisfactionIntervention groupHealth care opportunitiesPatientsClinical decisionCare opportunitiesPharmacy resourcesPreventive measuresProcess measuresCareMedical resourcesModest improvement