2020
Patient and provider-level factors associated with changes in utilization of treatments in response to evidence on ineffectiveness or harm
Smith LB, Desai NR, Dowd B, Everhart A, Herrin J, Higuera L, Jeffery MM, Jena AB, Ross JS, Shah ND, Karaca-Mandic P. Patient and provider-level factors associated with changes in utilization of treatments in response to evidence on ineffectiveness or harm. International Journal Of Health Economics And Management 2020, 20: 299-317. PMID: 32350680, PMCID: PMC7725279, DOI: 10.1007/s10754-020-09282-2.Peer-Reviewed Original ResearchConceptsPermanent atrial fibrillationType 2 diabetesAtrial fibrillationPermanent atrial fibrillation patientsProvider-level factorsAtrial fibrillation patientsEffective new therapiesPrimary care providersUse of medicationsProvider-level characteristicsUtilization of treatmentHigh-quality health careDronedarone useInterrupted time-series regression modelsFibrillation patientsMedication useDiabetes patientsProvider characteristicsCare providersMedicare feeNew therapiesService claimsFemale providersPatientsMedications
2016
Development and validation of a simple risk score to predict 30‐day readmission after percutaneous coronary intervention in a cohort of medicare patients
Minges KE, Herrin J, Fiorilli PN, Curtis JP. Development and validation of a simple risk score to predict 30‐day readmission after percutaneous coronary intervention in a cohort of medicare patients. Catheterization And Cardiovascular Interventions 2016, 89: 955-963. PMID: 27515069, PMCID: PMC5397364, DOI: 10.1002/ccd.26701.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAlgorithmsDecision Support TechniquesFemaleHumansLogistic ModelsMaleMedicareMultivariate AnalysisOdds RatioPatient ReadmissionPercutaneous Coronary InterventionPredictive Value of TestsRegistriesReproducibility of ResultsRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesConceptsRisk of readmissionPCI patientsRisk scoreMultivariable logistic regression modelRisk score developmentDays of dischargeSimple risk scoreTime of dischargeModel c-statisticLogistic regression modelsStepwise selection modelCathPCI RegistryHospital dischargeReadmission ratesClinical factorsRevascularization proceduresValidation cohortC-statisticReadmissionHigh riskMedicare feeLower riskService claimsPatientsCohort
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