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
2014
Physician clinical management strategies and reasoning: a cross-sectional survey using clinical vignettes of eight common medical admissions
Smith KL, Ashburn S, Aminawung JA, Mann M, Ross JS. Physician clinical management strategies and reasoning: a cross-sectional survey using clinical vignettes of eight common medical admissions. BMC Health Services Research 2014, 14: 176. PMID: 24742131, PMCID: PMC4021187, DOI: 10.1186/1472-6963-14-176.Peer-Reviewed Original ResearchConceptsClinical management strategiesMedical admissionsGuideline supportCross-sectional surveyClinical vignettesEvidence-based careAvoidance of careYears of ageLocal practice patternsInternal medicine programsSupportive evidenceResultsOur samplePractice patternsPhysicians' likelihoodClinical scenariosAdmissionBrief clinical scenariosDiagnostic testsPhysiciansClinical responsibilitiesMedicine programsStrong evidenceManagement strategiesEvidence disseminationCareHospital variation in risk-standardized hospital admission rates from US EDs among adults
Capp R, Ross JS, Fox JP, Wang Y, Desai MM, Venkatesh AK, Krumholz HM. Hospital variation in risk-standardized hospital admission rates from US EDs among adults. The American Journal Of Emergency Medicine 2014, 32: 837-843. PMID: 24881514, DOI: 10.1016/j.ajem.2014.03.033.Peer-Reviewed Original ResearchConceptsHospital admission ratesEmergency departmentAdmission ratesClinical characteristicsED visitsHospital factorsClinical factorsAdult ED visitsUS emergency departmentsHospital teaching statusCross-sectional analysisPatient characteristicsHospital admissionHospital variationPatientsTeaching statusHospitalED dataVisitsRepresentative sampleAdultsRural locationsAdmissionFactorsNational variations
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
Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling.
Drye EE, Normand SL, Wang Y, Ross JS, Schreiner GC, Han L, Rapp M, Krumholz HM. Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling. Annals Of Internal Medicine 2012, 156: 19-26. PMID: 22213491, PMCID: PMC3319769, DOI: 10.7326/0003-4819-156-1-201201030-00004.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesAcute myocardial infarctionHospital risk-standardized mortality ratesHospital mortality measuresHeart failureMortality rateObservational studyNonfederal acute care hospitalsMortality measuresAcute care hospitalsMean LOSPrimary outcomeStandardized followCare hospitalBlood InstituteService patientsMyocardial infarctionNational HeartPatient LOSMedicare feePneumoniaHospitalAdmissionHospital qualityHospital profiling
2010
The relationship between systolic blood pressure on admission and mortality in older patients with heart failure
Vidán MT, Bueno H, Wang Y, Schreiner G, Ross JS, Chen J, Krumholz HM. The relationship between systolic blood pressure on admission and mortality in older patients with heart failure. European Journal Of Heart Failure 2010, 12: 148-155. PMID: 20083624, PMCID: PMC2807767, DOI: 10.1093/eurjhf/hfp195.Peer-Reviewed Original ResearchConceptsAdmission systolic blood pressureSystolic blood pressureHeart failureBlood pressureOlder patientsNational Heart Failure ProjectHigher systolic blood pressureInitial systolic blood pressureHeart Failure ProjectMultivariable logistic regressionPrevious hypertensionSBP 90Ventricular dysfunctionClinical factorsIndependent associationOdds ratioMedicare patientsMortality ratePatientsMmHgLogistic regressionMortalityAdmissionSubgroupsInverse relationship