2020
Community factors and hospital wide readmission rates: Does context matter?
Spatz ES, Bernheim SM, Horwitz LI, Herrin J. Community factors and hospital wide readmission rates: Does context matter? PLOS ONE 2020, 15: e0240222. PMID: 33095775, PMCID: PMC7584172, DOI: 10.1371/journal.pone.0240222.Peer-Reviewed Original Research
2018
An empiric approach to identifying physician peer groups from claims data: An example from breast cancer care
Herrin J, Soulos PR, Xu X, Gross CP, Pollack CE. An empiric approach to identifying physician peer groups from claims data: An example from breast cancer care. Health Services Research 2018, 54: 44-51. PMID: 30488484, PMCID: PMC6338298, DOI: 10.1111/1475-6773.13095.Peer-Reviewed Original ResearchConceptsPhysician peer groupsClaims dataEnd Results-MedicareBreast cancer careAdministrative claims dataHospital-based groupEmpiric approachCancer careBreast cancerHospital groupPatientsPatient volumePhysiciansEmpirical groupGroup reliabilityPhysician inclusionPercentGroupMedian overlapT1CancerPeer groupEpidemiologyAdmission diagnoses among patients with heart failure: Variation by ACO performance on a measure of risk-standardized acute admission rates
Benchetrit L, Zimmerman C, Bao H, Dharmarajan K, Altaf F, Herrin J, Lin Z, Krumholz HM, Drye EE, Lipska KJ, Spatz ES. Admission diagnoses among patients with heart failure: Variation by ACO performance on a measure of risk-standardized acute admission rates. American Heart Journal 2018, 207: 19-26. PMID: 30404047, DOI: 10.1016/j.ahj.2018.09.006.Peer-Reviewed Original ResearchMeSH KeywordsAccountable Care OrganizationsAgedAlgorithmsAnalysis of VarianceCardiovascular DiseasesComorbidityFemaleHeart FailureHospitalizationHumansInternational Classification of DiseasesMaleMedicare Part AMedicare Part BPatient AdmissionPatient DischargePatient-Centered CareSex DistributionTime FactorsUnited StatesConceptsHeart failureAccountable care organizationsMean admission rateAdmission ratesAdmission typeAcute admission ratesNoncardiovascular conditionsAdmission diagnosisCause admission ratesMedicare Shared Savings Program Accountable Care OrganizationsRate of hospitalizationPrincipal discharge diagnosisProportion of admissionsType of admissionNoncardiovascular causesHF admissionsHF patientsPerson yearsDischarge diagnosisPatient populationPatientsAdmissionKey quality metricDiagnosisSubstantial proportion
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
2012
The Effectiveness of Implementing an Electronic Health Record on Diabetes Care and Outcomes
Herrin J, da Graca B, Nicewander D, Fullerton C, Aponte P, Stanek G, Cowling T, Collinsworth A, Fleming NS, Ballard DJ. The Effectiveness of Implementing an Electronic Health Record on Diabetes Care and Outcomes. Health Services Research 2012, 47: 1522-1540. PMID: 22250953, PMCID: PMC3401397, DOI: 10.1111/j.1475-6773.2011.01370.x.Peer-Reviewed Original ResearchConceptsPrimary care practicesBlood pressureDiabetes careOptimal careCare practicesDiastolic blood pressureSystolic blood pressureYears of ageElectronic health record implementationElectronic health recordsAspirin prescriptionUnexposed patientsAspirin useCare bundleInsulin usePatient ageLipid controlPrimary outcomeClinical outcomesLDL cholesterolSmoking cessationChart auditDiabetes measuresDiabetes patientsPatients
2010
Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children
DORSEY KB, HERRIN J, KRUMHOLZ HM. Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children. Pediatric Obesity 2010, 6: e547-e555. PMID: 20883127, PMCID: PMC3815589, DOI: 10.3109/17477166.2010.490586.Peer-Reviewed Original ResearchConceptsVigorous physical activityOW/OBNon-overweight childrenMVPA boutsPhysical activityGreater body mass index z-scoreVPA boutsOW/OB groupBody mass index z-scoreMean daily MVPANon-overweight groupLess physical activityIndex z-scoreMinutes of MVPANon-overweight peersObese childrenObese participantsOverweight childrenOB groupDaily MVPASustained MVPADistinct patternsOB participantsMVPAConsecutive bouts
2008
An algorithm for identifying physical activity patterns from motion data.
Dorsey KB, Herrin J, Krumholz HM. An algorithm for identifying physical activity patterns from motion data. Pediatric Exercise Science 2008, 20: 305-18. PMID: 18714120, DOI: 10.1123/pes.20.3.305.Peer-Reviewed Original Research