2021
Association between 30-day readmission rates and health information technology capabilities in US hospitals
Elysee G, Yu H, Herrin J, Horwitz LI. Association between 30-day readmission rates and health information technology capabilities in US hospitals. Medicine 2021, 100: e24755. PMID: 33663091, PMCID: PMC7909153, DOI: 10.1097/md.0000000000024755.Peer-Reviewed Original ResearchConceptsRisk-standardized readmission ratesHealth IT capabilitiesLower readmission riskReadmission riskReadmission ratesHealth information technologyElectronic health recordsHospital dischargeRetrospective cross-sectional studyU.S. acute care hospitalsHealth recordsAcute care hospitalsCross-sectional studyFragmentation of careHospital-level risk-standardized readmission ratesOne-point increaseHospital Compare websiteHealth information technology capabilitiesCare hospitalOutcome measuresOutpatient providersUS hospitalsCare deliveryPatient accessClinical stakeholders
2018
Hospital Leadership Diversity and Strategies to Advance Health Equity
Herrin J, Harris KG, Spatz E, Cobbs-Lomax D, Allen S, León T. Hospital Leadership Diversity and Strategies to Advance Health Equity. The Joint Commission Journal On Quality And Patient Safety 2018, 44: 545-551. PMID: 30166038, DOI: 10.1016/j.jcjq.2018.03.008.Peer-Reviewed Original ResearchIdentifying county characteristics associated with resident well-being: A population based study
Roy B, Riley C, Herrin J, Spatz ES, Arora A, Kell KP, Welsh J, Rula EY, Krumholz HM. Identifying county characteristics associated with resident well-being: A population based study. PLOS ONE 2018, 13: e0196720. PMID: 29791476, PMCID: PMC5965855, DOI: 10.1371/journal.pone.0196720.Peer-Reviewed Original ResearchConceptsCounty-level factorsClinical careCross-sectional studyQuality of lifeBetter health outcomesMulti-dimensional assessmentHealth outcomesBeing IndexGallup-Sharecare WellUS residentsCareCounty characteristicsSurvey participantsResident wellUS countiesScoresCounty equivalentsAssessmentFactorsCohortHospital Characteristics Associated With Postdischarge Hospital Readmission, Observation, and Emergency Department Utilization
Horwitz LI, Wang Y, Altaf FK, Wang C, Lin Z, Liu S, Grady J, Bernheim SM, Desai NR, Venkatesh AK, Herrin J. Hospital Characteristics Associated With Postdischarge Hospital Readmission, Observation, and Emergency Department Utilization. Medical Care 2018, 56: 281-289. PMID: 29462075, PMCID: PMC6170884, DOI: 10.1097/mlr.0000000000000882.Peer-Reviewed Original ResearchMeSH KeywordsCross-Sectional StudiesEmergency Service, HospitalFee-for-Service PlansHeart FailureHospital AdministrationHospitals, PublicHumansMedicareMyocardial InfarctionNursing Staff, HospitalOwnershipPatient ReadmissionPneumoniaResidence CharacteristicsRetrospective StudiesSafety-net ProvidersUnited StatesConceptsAcute care utilizationAcute myocardial infarctionHeart failureCare utilizationAcute careMyocardial infarctionHospital characteristicsNet hospitalExcess daysPublic hospitalsNonsafety net hospitalsHigher readmission ratesEmergency department utilizationProportion of hospitalsAcute care hospitalsSafety-net hospitalService Medicare beneficiariesLarge urban hospitalMajor teaching hospitalType of hospitalCross-sectional analysisPostdischarge utilizationHospital dischargeHospital factorsReadmission rates
2017
Regional Medicare Expenditures and Survival Among Older Women With Localized Breast Cancer
Tannenbaum S, Soulos PR, Herrin J, Mougalian S, Long JB, Wang R, Ma X, Gross CP, Xu X. Regional Medicare Expenditures and Survival Among Older Women With Localized Breast Cancer. Medical Care 2017, 55: 1030-1038. PMID: 29068906, PMCID: PMC5863278, DOI: 10.1097/mlr.0000000000000822.Peer-Reviewed Original ResearchConceptsBreast cancer careHospital referral regionsNonmetastatic breast cancerBreast cancerCancer careMedicare beneficiariesMedicare expendituresCancer expendituresBetter survivalEnd Results-MedicareRetrospective cohort studyPatients 3 yearsClinical characteristicsCohort studyOverall survivalCancer stagePatient outcomesOutcome measuresReferral regionsOlder womenSignificant associationStage IIBivariate analysisCancerQuintile
2015
Assessing Community Quality of Health Care
Herrin J, Kenward K, Joshi MS, Audet AM, Hines SJ. Assessing Community Quality of Health Care. Health Services Research 2015, 51: 98-116. PMID: 26096649, PMCID: PMC4722214, DOI: 10.1111/1475-6773.12322.Peer-Reviewed Original ResearchConceptsHospital service areasNursing homesHospital measuresHospital qualityHigh hospital qualityHealth system factorsAgreement of measuresHigh-quality careHigh-quality hospitalsHome health agenciesDimensions of careComposite quality measureGeneral practitionersCare settingsNH careHealth agenciesQuality hospitalsHospitalCareHealth careSystem factorsAvailable quality measuresQuality measuresHigh differSettingChanging trends in type 2 diabetes mellitus treatment intensification, 2002-2010.
McCoy RG, Zhang Y, Herrin J, Denton BT, Mason JE, Montori VM, Smith SA, Shah ND. Changing trends in type 2 diabetes mellitus treatment intensification, 2002-2010. The American Journal Of Managed Care 2015, 21: e288-96. PMID: 26167776.Peer-Reviewed Original ResearchMeSH KeywordsAdministration, OralAdolescentAdultAge FactorsAgedBlood GlucoseComorbidityDiabetes Mellitus, Type 2Drug Therapy, CombinationFemaleGlycated HemoglobinHumansHypoglycemic AgentsIncretinsInsurance Claim ReviewMaleMetforminMiddle AgedResidence CharacteristicsRetrospective StudiesSex FactorsSocioeconomic FactorsSulfonylurea CompoundsUnited StatesYoung AdultConceptsTreatment intensificationCox proportional hazards regression analysisNational administrative data setProportional hazards regression analysisRetrospective secondary data analysisDiabetes treatment intensificationOptimal diabetes careHazards regression analysisDiabetes-related complicationsAdults 18 yearsTreatment-naïve adultsNon-Hispanic whitesComorbidity burdenMetformin monotherapySulfonylurea useMetformin prescriptionThiazolidinedione useGlycemic controlKaplan-MeierMean ageDiabetes careSignificant confoundersSecondary data analysisAdministrative data setsDiabetes therapy
2005
Enrolling Older Persons in Cancer Trials: The Effect of Sociodemographic, Protocol, and Recruitment Center Characteristics
Gross CP, Herrin J, Wong N, Krumholz HM. Enrolling Older Persons in Cancer Trials: The Effect of Sociodemographic, Protocol, and Recruitment Center Characteristics. Journal Of Clinical Oncology 2005, 23: 4755-4763. PMID: 16034051, DOI: 10.1200/jco.2005.14.365.Peer-Reviewed Original ResearchConceptsCancer trialsOlder personsRecruitment centerElderly enrollmentProportion of patientsEffect of patientProstate cancer trialsPatient-level variationFinal study sampleNational Cancer InstituteCross-sectional analysisEffects of sociodemographicsNonwhite patientsTrial participantsOutlier centersCancer InstitutePatientsEnrollment centerMultivariate analysisLikelihood of participantsCancer typesLogistic multilevel modelsTrialsCenter characteristicsStudy sample