2024
Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure
Kyanko K, Sahay K, Wang Y, Li S, Schreiber M, Hager M, Myers R, Johnson W, Zhang J, Krumholz H, Suter L, Triche E. Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure. JAMA Network Open 2024, 7: e2414431. PMID: 38829614, PMCID: PMC11148674, DOI: 10.1001/jamanetworkopen.2024.14431.Peer-Reviewed Original ResearchConceptsCenters for Medicare & Medicaid ServicesSpecialty subgroupsPerformance quintileMedicare AdvantageReadmission ratesRisk-standardized readmission ratesHospital-wide readmission measureHospital outcome measuresTest-retest reliabilityRisk-adjustment variablesMeasurement reliabilityAdministrative claims dataReadmission measuresImprove measurement reliabilityIntegrated data repositoryMA beneficiariesQuintile rankingsMedicare beneficiariesMedicaid ServicesAll-causePublic reportingStudy assessed differencesClaims dataOutcome measuresMA cohort
2021
Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models
Triche EW, Xin X, Stackland S, Purvis D, Harris A, Yu H, Grady JN, Li SX, Bernheim SM, Krumholz HM, Poyer J, Dorsey K. Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment Models. JAMA Network Open 2021, 4: e218512. PMID: 33978722, PMCID: PMC8116982, DOI: 10.1001/jamanetworkopen.2021.8512.Peer-Reviewed Original ResearchConceptsPOA indicatorRisk factorsOutcome measuresQuality outcome measuresRisk-adjustment modelsClaims dataAdmission indicatorsPatient risk factorsAcute myocardial infarctionPatient-level outcomesAdministrative claims dataQuality improvement studyClaims-based measuresComparative effectiveness studiesPatient claims dataInternational Statistical ClassificationMortality outcome measuresRelated Health ProblemsHospital quality measuresRisk model performanceHospital stayIndex admissionCare algorithmHeart failureMortality outcomes
2020
Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data
Li SX, Wang Y, Lama SD, Schwartz J, Herrin J, Mei H, Lin Z, Bernheim SM, Spivack S, Krumholz HM, Suter LG. Timely estimation of National Admission, readmission, and observation-stay rates in medicare patients with acute myocardial infarction, heart failure, or pneumonia using near real-time claims data. BMC Health Services Research 2020, 20: 733. PMID: 32778098, PMCID: PMC7416804, DOI: 10.1186/s12913-020-05611-w.Peer-Reviewed Original ResearchAttribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data
Dhruva SS, Parzynski CS, Gamble GM, Curtis JP, Desai NR, Yeh RW, Masoudi FA, Kuntz R, Shaw RE, Marinac‐Dabic D, Sedrakyan A, Normand S, Krumholz HM, Ross JS. Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data. Journal Of The American Heart Association 2020, 9: e013606. PMID: 32063087, PMCID: PMC7070203, DOI: 10.1161/jaha.119.013606.Peer-Reviewed Original ResearchMeSH KeywordsAdministrative Claims, HealthcareAgedAged, 80 and overCoronary RestenosisCoronary ThrombosisDatabases, FactualDrug-Eluting StentsFemaleHumansMaleMedicareMyocardial InfarctionPercutaneous Coronary InterventionProduct Surveillance, PostmarketingRegistriesRetreatmentRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesConceptsIndex percutaneous coronary interventionPercutaneous coronary interventionSame coronary arteryDrug-eluting stentsNCDR CathPCI RegistrySubsequent percutaneous coronary interventionAcute myocardial infarctionCoronary arteryClaims dataCathPCI RegistryAdverse eventsIndex procedureMyocardial infarctionRepeat percutaneous coronary interventionReal-world registry dataTarget vessel revascularizationCoronary stent placementAdministrative claims dataLong-term safetyLongitudinal claims dataPotential safety eventsVessel revascularizationCoronary interventionDES placementStent thrombosis
2019
Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data
Krumholz HM, Warner F, Coppi A, Triche EW, Li SX, Mahajan S, Li Y, Bernheim SM, Grady J, Dorsey K, Desai NR, Lin Z, Normand ST. Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data. JAMA Network Open 2019, 2: e198406. PMID: 31411709, PMCID: PMC6694388, DOI: 10.1001/jamanetworkopen.2019.8406.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionHeart failurePopulation-based programsPOA codesSingle diagnostic codeDiagnostic codesComparative effectiveness research studyPublic reportingIndex admission diagnosisDays of hospitalizationClinical Modification codesService claims dataAcute care hospitalsMultiple care settingsPatient-level modelsAdmission diagnosisTotal hospitalizationsCare hospitalPrevious diagnosisNinth RevisionMyocardial infarctionCandidate variablesCare settingsClaims dataMAIN OUTCOME
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 admissionIdentification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications
Venkatesh AK, Mei H, Kocher KE, Granovsky M, Obermeyer Z, Spatz E, Rothenberg C, Krumholz H, Lin Z. Identification of Emergency Department Visits in Medicare Administrative Claims: Approaches and Implications. Academic Emergency Medicine 2017, 24: 422-431. PMID: 27864915, PMCID: PMC5905698, DOI: 10.1111/acem.13140.Peer-Reviewed Original ResearchConceptsED visitsEmergency department visitsClaims-based definitionED visitationAdministrative claimsDepartment visitsClaims dataAdministrative claims data setsHealthcare resource utilizationMore ED visitsAcute care practiceAdministrative claims dataQuality improvement interventionsEmergency care researchMedicare administrative claimsClaims data setsED useCritical careED servicesMedicare feeMedicare dataCare practicesService beneficiariesImprovement interventionsProvider definitions
2016
Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013
Lipska KJ, Yao X, Herrin J, McCoy RG, Ross JS, Steinman MA, Inzucchi SE, Gill TM, Krumholz HM, Shah ND. Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006–2013. Diabetes Care 2016, 40: 468-475. PMID: 27659408, PMCID: PMC5360291, DOI: 10.2337/dc16-0985.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedBlood GlucoseComorbidityDiabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsDrug UtilizationFemaleGlycated HemoglobinHumansHypoglycemiaHypoglycemic AgentsInsulinLogistic ModelsMaleMetforminMiddle AgedRetrospective StudiesSulfonylurea CompoundsThiazolidinedionesYoung AdultConceptsGlycemic controlSevere hypoglycemiaOlder patientsDipeptidyl peptidase-4 inhibitorsGlucose-lowering drugsGlucose-lowering medicationsProportion of patientsOverall glycemic controlPeptidase-4 inhibitorsMedicare Advantage patientsSex-standardized ratesType 2 diabetesOverall rateClass of agentsMore comorbiditiesChronic comorbiditiesYounger patientsAdvantage patientsDrug utilizationClaims dataPatientsHypoglycemiaHemoglobin AT2DMComorbiditiesLong-Term Risk for Device-Related Complications and Reoperations After Implantable Cardioverter-Defibrillator Implantation: An Observational Cohort Study.
Ranasinghe I, Parzynski CS, Freeman JV, Dreyer RP, Ross JS, Akar JG, Krumholz HM, Curtis JP. Long-Term Risk for Device-Related Complications and Reoperations After Implantable Cardioverter-Defibrillator Implantation: An Observational Cohort Study. Annals Of Internal Medicine 2016, 165: 20-29. PMID: 27135392, DOI: 10.7326/m15-2732.Peer-Reviewed Original ResearchICD-related complicationsNational Cardiovascular Data RegistryObservational cohort studyDevice-related complicationsICD implantationLong-term riskCohort studyMedicare feeNational Cardiovascular Data Registry ICD RegistryImplantable cardioverter defibrillator implantationImplantable cardioverter-defibrillator placementCardioverter-defibrillator implantationService claims dataCRT-D devicesSingle-chamber devicesCumulative incidenceNonfatal outcomesICD RegistryService patientsBlack raceFemale sexReoperationAmerican CollegeClaims dataComplications
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
Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission.
Horwitz LI, Partovian C, Lin Z, Grady JN, Herrin J, Conover M, Montague J, Dillaway C, Bartczak K, Suter LG, Ross JS, Bernheim SM, Krumholz HM, Drye EE. Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission. Annals Of Internal Medicine 2014, 161: s66-75. PMID: 25402406, PMCID: PMC4235629, DOI: 10.7326/m13-3000.Peer-Reviewed Original ResearchConceptsUnplanned readmissionReadmission measuresReadmission ratesReadmission riskMedicare feeHospital-wide readmission measureRisk-standardized readmission ratesPayer dataAdministrative Claims MeasureRisk-standardized ratesAverage-risk patientsUnplanned readmission rateDays of dischargeHospital risk-standardized readmission ratesAdult hospitalizationsComorbid conditionsPrincipal diagnosisClaims dataService claimsService beneficiariesReadmissionMeasure development studiesMedicaid ServicesRisk adjustmentHospital
2013
Regional Density of Cardiologists and Rates of Mortality for Acute Myocardial Infarction and Heart Failure
Kulkarni VT, Ross JS, Wang Y, Nallamothu BK, Spertus JA, Normand SL, Masoudi FA, Krumholz HM. Regional Density of Cardiologists and Rates of Mortality for Acute Myocardial Infarction and Heart Failure. Circulation Cardiovascular Quality And Outcomes 2013, 6: 352-359. PMID: 23680965, PMCID: PMC5323047, DOI: 10.1161/circoutcomes.113.000214.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCardiologyCohort StudiesFemaleHealth Services AccessibilityHealth Services Needs and DemandHealthcare DisparitiesHeart FailureHospitalizationHumansLinear ModelsLogistic ModelsMaleMedicareMyocardial InfarctionOdds RatioPhysiciansPneumoniaPrognosisResidence CharacteristicsRisk AssessmentRisk FactorsTime FactorsUnited StatesWorkforceConceptsAcute myocardial infarctionHeart failureHospital referral regionsMortality riskLowest quintileMyocardial infarctionReferral regionsMedicare administrative claims dataCharacteristics of patientsRisk of deathAdministrative claims dataHierarchical logistic regression modelsLogistic regression modelsRate of mortalityRegional densityHighest quintileNumber of cardiologistsWorse outcomesClaims dataPatientsPneumoniaCardiologistsHospitalizationAdmissionQuintile
2012
Development of 2 Registry-Based Risk Models Suitable for Characterizing Hospital Performance on 30-Day All-Cause Mortality Rates Among Patients Undergoing Percutaneous Coronary Intervention
Curtis JP, Geary LL, Wang Y, Chen J, Drye EE, Grosso LM, Spertus JA, Rumsfeld JS, Weintraub WS, Masoudi FA, Brindis RG, Krumholz HM. Development of 2 Registry-Based Risk Models Suitable for Characterizing Hospital Performance on 30-Day All-Cause Mortality Rates Among Patients Undergoing Percutaneous Coronary Intervention. Circulation Cardiovascular Quality And Outcomes 2012, 5: 628-637. PMID: 22949491, DOI: 10.1161/circoutcomes.111.964569.Peer-Reviewed Original ResearchMeSH KeywordsAcute Coronary SyndromeAgedAged, 80 and overAngina PectorisChi-Square DistributionComorbidityFemaleHeart DiseasesHospital MortalityHospitalsHumansLogistic ModelsMaleMyocardial InfarctionOdds RatioOutcome and Process Assessment, Health CarePercutaneous Coronary InterventionQuality Indicators, Health CareRegistriesRisk AssessmentRisk FactorsShock, CardiogenicTime FactorsTreatment OutcomeUnited StatesConceptsST-segment elevation myocardial infarctionPercutaneous coronary interventionRisk-standardized mortality ratesElevation myocardial infarctionPatient mortality ratesMyocardial infarctionMortality rateCardiogenic shockCoronary interventionDerivation cohortHospital risk-standardized mortality ratesCause mortality ratesAdministrative claims dataQuality of careHierarchical logistic regression modelsNational Quality ForumLogistic regression modelsObserved mortality rateCathPCI RegistryNational HospitalClaims dataInfarctionPatientsQuality ForumFinal modelSkilled Nursing Facility Referral and Hospital Readmission Rates after Heart Failure or Myocardial Infarction
Chen J, Ross JS, Carlson MD, Lin Z, Normand SL, Bernheim SM, Drye EE, Ling SM, Han LF, Rapp MT, Krumholz HM. Skilled Nursing Facility Referral and Hospital Readmission Rates after Heart Failure or Myocardial Infarction. The American Journal Of Medicine 2012, 125: 100.e1-100.e9. PMID: 22195535, PMCID: PMC3246370, DOI: 10.1016/j.amjmed.2011.06.011.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionRisk-standardized readmission ratesSkilled nursing facilitiesHeart failureHospital-level variationReadmission ratesMyocardial infarctionRate of dischargeHospital-level readmission ratesSubstantial hospital-level variationService Medicare patientsCause readmission rateRisk of readmissionHospital readmission ratesHF admissionsRegression modelsAMI patientsFacility referralPrincipal diagnosisMedicare patientsMedicare claimsClaims dataAMI admissionsAMI hospitalizationNursing facilities
2007
Assessing surrogacy of data sources for institutional comparisons
Normand S, Wang Y, Krumholz H. Assessing surrogacy of data sources for institutional comparisons. Health Services And Outcomes Research Methodology 2007, 7: 79-96. DOI: 10.1007/s10742-006-0018-8.Peer-Reviewed Original ResearchThe Impact of Venous Thromboembolism on Risk of Death or Hemorrhage in Older Cancer Patients
Gross CP, Galusha DH, Krumholz HM. The Impact of Venous Thromboembolism on Risk of Death or Hemorrhage in Older Cancer Patients. Journal Of General Internal Medicine 2007, 22: 321-326. PMID: 17356962, PMCID: PMC1824718, DOI: 10.1007/s11606-006-0019-x.Peer-Reviewed Original ResearchConceptsRisk of deathOlder cancer patientsConcomitant venous thromboembolismVenous thromboembolismMajor hemorrhageCancer patientsCancer typesCancer diagnosisMedicare administrative claims dataPrevalence of VTEEnd Results cancer registryRetrospective cohort studyAdministrative claims dataCohort studyCancer RegistryInvasive cancerExcess riskMost cancer typesCancer stageClaims dataHemorrhagePatientsSociodemographic factorsPotential mediatorsDeath