2024
Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance
Nash K, Weerahandi H, Yu H, Venkatesh A, Holaday L, Herrin J, Lin Z, Horwitz L, Ross J, Bernheim S. Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance. JAMA 2024, 331: 111-123. PMID: 38193960, PMCID: PMC10777266, DOI: 10.1001/jama.2023.24874.Peer-Reviewed Original ResearchConceptsBlack patientsPatient populationHospital characteristicsHospital-wide readmission measureDual-eligible patientsHospital patient populationCross-sectional studyMeasures of hospitalHealth care qualityPatient demographicsReadmission ratesClinical outcomesPatient raceEligible hospitalsReadmissionMAIN OUTCOMEReadmission measuresMedicare dataUS hospitalsHospitalCare qualityPatientsMedicaid ServicesOutcomesLower percentage
2022
Factors Associated With Disparities in Hospital Readmission Rates Among US Adults Dually Eligible for Medicare and Medicaid
Silvestri D, Goutos D, Lloren A, Zhou S, Zhou G, Farietta T, Charania S, Herrin J, Peltz A, Lin Z, Bernheim S. Factors Associated With Disparities in Hospital Readmission Rates Among US Adults Dually Eligible for Medicare and Medicaid. JAMA Health Forum 2022, 3: e214611. PMID: 35977231, PMCID: PMC8903116, DOI: 10.1001/jamahealthforum.2021.4611.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionNon-DE patientsCommunity-level factorsHospital disparitiesHeart failureDE patientsReadmission ratesCohort studyUS hospitalsRisk-adjusted readmission ratesRetrospective cohort studyHospital readmission ratesLow-income older adultsHospital quality improvementEligible patientsHospital readmissionMedicaid eligibility policyCare transitionsMyocardial infarctionState Medicaid policiesWorse outcomesMedicare patientsMAIN OUTCOMEUS adultsPneumonia
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
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 ResearchIlluminating Hospital Disparities in Readmissions for Patients with Social Risk Factors: Comparing Hospital Performance Using Two Different Approaches
Herrin J, Peltz A, Zhou S, Du C, Barbo A, Charania S, Schwartz M, Lin Z, Bernheim S. Illuminating Hospital Disparities in Readmissions for Patients with Social Risk Factors: Comparing Hospital Performance Using Two Different Approaches. Health Services Research 2020, 55: 93-94. PMCID: PMC7440563, DOI: 10.1111/1475-6773.13462.Peer-Reviewed Original ResearchSocial risk factorsAbsolute risk differenceDual eligibility statusHospital disparitiesRisk factorsDual eligibilityMedicare patientsReadmission ratesOdds ratioRisk differenceSocioeconomic disparitiesMedicare FFS patientsRetrospective cohort studyClinical risk factorsCommon clinical conditionHospitalized Medicare patientsSES indexStandard Analytic FilesVeterans Affairs hospitalEligibility statusMedicare Part AHospital outcomesCohort studyFFS patientsHospital readmission
2019
Home Health Care After Skilled Nursing Facility Discharge Following Heart Failure Hospitalization
Weerahandi H, Bao H, Herrin J, Dharmarajan K, Ross JS, Jones S, Horwitz LI. Home Health Care After Skilled Nursing Facility Discharge Following Heart Failure Hospitalization. Journal Of The American Geriatrics Society 2019, 68: 96-102. PMID: 31603248, PMCID: PMC6964248, DOI: 10.1111/jgs.16179.Peer-Reviewed Original ResearchConceptsSkilled nursing facilitiesHF hospitalizationReadmission ratesReadmission riskHeart failure readmission ratesDays of dischargeHeart failure hospitalizationRetrospective cohort studyHospital discharge practicesMore functional impairmentHome health careFailure hospitalizationHF patientsUnplanned readmissionCohort studyHospital dischargePrimary outcomeRestorative therapySNF stayFunctional impairmentHome healthcare servicesService Medicare dataAdjusted modelCox modelNursing facilitiesRacial and Ethnic Differences in 30-Day Hospital Readmissions Among US Adults With Diabetes
Rodriguez-Gutierrez R, Herrin J, Lipska KJ, Montori VM, Shah ND, McCoy RG. Racial and Ethnic Differences in 30-Day Hospital Readmissions Among US Adults With Diabetes. JAMA Network Open 2019, 2: e1913249. PMID: 31603490, PMCID: PMC6804020, DOI: 10.1001/jamanetworkopen.2019.13249.Peer-Reviewed Original ResearchMeSH KeywordsAdministrative Claims, HealthcareAgedAged, 80 and overAsianBlack or African AmericanComorbidityDiabetes ComplicationsEthnicityFemaleHispanic or LatinoHospital Bed Capacity, 300 to 499Hospital Bed Capacity, 500 and overHospitals, UniversityHospitals, VoluntaryHumansIncomeMaleMiddle AgedMinority GroupsPatient ReadmissionRacial GroupsRetrospective StudiesUnited StatesWhite PeopleConceptsCause readmissionIndex hospitalizationBlack patientsWhite patientsUS adultsHigh riskAdministrative claims data setsHospital-level risk factorsEthnic differencesLarge hospitalsPlace of hospitalizationDays of dischargeRetrospective cohort studyLow-income patientsMedicare Advantage beneficiariesSystem-level factorsClaims data setsHealth care qualityRace/ethnicityCohort studyReadmission ratesAdult patientsHospital readmissionHispanic patientsReadmission riskTrends in Hospital Readmission of Medicare-Covered Patients With Heart Failure
Blecker S, Herrin J, Li L, Yu H, Grady JN, Horwitz LI. Trends in Hospital Readmission of Medicare-Covered Patients With Heart Failure. Journal Of The American College Of Cardiology 2019, 73: 1004-1012. PMID: 30846093, PMCID: PMC7011858, DOI: 10.1016/j.jacc.2018.12.040.Peer-Reviewed Original ResearchConceptsHospital Readmissions Reduction ProgramSecondary heart failureReadmission ratesHeart failureReadmissions Reduction ProgramHF hospitalizationAffordable Care ActMedicare's Hospital Readmissions Reduction ProgramRisk-adjusted readmission ratesCause readmission rateHigher readmission ratesAcute myocardial infarctionCare ActReduction programsLinear spline regression modelsPneumonia hospitalizationsHospital readmissionMedicare hospitalizationsRetrospective studySecondary diagnosisMyocardial infarctionPrincipal diagnosisHospitalizationSpline regression modelsPatientsMeasuring hospital‐specific disparities by dual eligibility and race to reduce health inequities
Lloren A, Liu S, Herrin J, Lin Z, Zhou G, Wang Y, Kuang M, Zhou S, Farietta T, McCole K, Charania S, Sheares K, Bernheim S. Measuring hospital‐specific disparities by dual eligibility and race to reduce health inequities. Health Services Research 2019, 54: 243-254. PMID: 30666634, PMCID: PMC6341208, DOI: 10.1111/1475-6773.13108.Peer-Reviewed Original ResearchConceptsAfrican American racePatient case mixDual eligibilityReadmission ratesAmerican raceRisk-standardized outcomesHigher readmission ratesDual eligibility statusAcute myocardial infarctionAfrican American patientsRisk-standardized readmission ratesAcute care hospitalsQuality of careMedicaid Services methodologyHealth care qualityHospital disparitiesCare hospitalHeart failureInpatient admissionsMyocardial infarctionAmerican patientsMedicare patientsCase mixHealth outcomesHospital
2018
Hospital 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 ratesEffect of Hospital Readmission Reduction on Patients at Low, Medium, and High Risk of Readmission in the Medicare Population
Blecker S, Herrin J, Kwon JY, Grady JN, Jones S, Horwitz LI. Effect of Hospital Readmission Reduction on Patients at Low, Medium, and High Risk of Readmission in the Medicare Population. Journal Of Hospital Medicine 2018, 13: 537-543. PMID: 29455229, PMCID: PMC6063766, DOI: 10.12788/jhm.2936.Peer-Reviewed Original Research
2017
Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015
Salerno AM, Horwitz LI, Kwon JY, Herrin J, Grady JN, Lin Z, Ross JS, Bernheim SM. Trends in readmission rates for safety net hospitals and non-safety net hospitals in the era of the US Hospital Readmission Reduction Program: a retrospective time series analysis using Medicare administrative claims data from 2008 to 2015. BMJ Open 2017, 7: e016149. PMID: 28710221, PMCID: PMC5541519, DOI: 10.1136/bmjopen-2017-016149.Peer-Reviewed Original ResearchConceptsHospital Readmissions Reduction ProgramNon-safety net hospitalsSafety-net hospitalMedicare administrative claims dataReadmission ratesAdministrative claims dataNet hospitalReadmissions Reduction ProgramRetrospective time series analysisSafety netClaims dataTime series analysisSocioeconomic statusUnplanned readmission ratePrincipal discharge diagnosisLow socioeconomic statusInterrupted time seriesReduction programsFive-digit zip codeSeries analysisHRRP penaltiesIndex admissionHospital proportionDischarge diagnosisService patientsHospital Readmissions among Commercially Insured and Medicare Advantage Beneficiaries with Diabetes and the Impact of Severe Hypoglycemic and Hyperglycemic Events
McCoy RG, Lipska KJ, Herrin J, Jeffery MM, Krumholz HM, Shah ND. Hospital Readmissions among Commercially Insured and Medicare Advantage Beneficiaries with Diabetes and the Impact of Severe Hypoglycemic and Hyperglycemic Events. Journal Of General Internal Medicine 2017, 32: 1097-1105. PMID: 28685482, PMCID: PMC5602759, DOI: 10.1007/s11606-017-4095-x.Peer-Reviewed Original ResearchConceptsDiabetes Complications Severity IndexSevere dysglycemiaIndex hospitalizationMedicare Advantage beneficiariesRisk factorsBetter care transitionsComplications Severity IndexPost-discharge managementIndependent risk factorYounger patient ageOptumLabs Data WarehouseStrong risk factorYears of ageBackgroundHospital readmissionsDesignRetrospective analysisCause readmissionUnplanned readmissionPatient agePrior hospitalizationReadmission ratesYounger patientsHeart failureHospital readmissionSevere hypoglycemiaDiabetes complicationsHospital Characteristics Associated With Risk-standardized Readmission Rates
Horwitz LI, Bernheim SM, Ross JS, Herrin J, Grady JN, Krumholz HM, Drye EE, Lin Z. Hospital Characteristics Associated With Risk-standardized Readmission Rates. Medical Care 2017, 55: 528-534. PMID: 28319580, PMCID: PMC5426655, DOI: 10.1097/mlr.0000000000000713.Peer-Reviewed Original Research
2016
Association Between Hospital Penalty Status Under the Hospital Readmission Reduction Program and Readmission Rates for Target and Nontarget Conditions
Desai NR, Ross JS, Kwon JY, Herrin J, Dharmarajan K, Bernheim SM, Krumholz HM, Horwitz LI. Association Between Hospital Penalty Status Under the Hospital Readmission Reduction Program and Readmission Rates for Target and Nontarget Conditions. JAMA 2016, 316: 2647-2656. PMID: 28027367, PMCID: PMC5599851, DOI: 10.1001/jama.2016.18533.Peer-Reviewed Original ResearchConceptsHospital Readmissions Reduction ProgramAcute myocardial infarctionReadmission ratesReadmissions Reduction ProgramHeart failurePenalty statusNontarget conditionsMedicare feeMean readmission rateThirty-day riskRetrospective cohort studyUnplanned readmission rateReduction programsHRRP announcementHRRP implementationPenalized hospitalsCohort studyService patientsMyocardial infarctionMAIN OUTCOMEExcess readmissionsMedicare beneficiariesService beneficiariesHospitalPatientsDevelopment 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 claimsPatientsCohortQuasi-Experimental Evaluation of the Effectiveness of a Large-Scale Readmission Reduction Program
Jenq GY, Doyle MM, Belton BM, Herrin J, Horwitz LI. Quasi-Experimental Evaluation of the Effectiveness of a Large-Scale Readmission Reduction Program. JAMA Internal Medicine 2016, 176: 681. PMID: 27065180, DOI: 10.1001/jamainternmed.2016.0833.Peer-Reviewed Original ResearchConceptsDischarge patientsReadmissions Reduction ProgramControl populationReadmission ratesIntervention periodSame-hospital readmission ratesUrban academic medical centerTarget populationAdjusted readmission ratesOdds of readmissionHigh-risk patientsZip codesAdjusted admission ratesInterrupted time series analysisAcademic medical centerQuasi-experimental evaluationLogistic regression modelsReduction programsDischarge dispositionReadmission reduction effortsComparative interrupted time series analysisMedication reconciliationService patientsMean ageTransitional careASSOCIATIONS OF HOSPITAL STRATEGIES AND 30-DAY RISK-STANDARDIZED READMISSION RATES IN PERCUTANEOUS CORONARY INTERVENTION
Minges K, Herrin J, Desai N, Messenger J, Nallamothu B, Rumsfeld J, Elma M, Chen P, Ting H, Curtis J. ASSOCIATIONS OF HOSPITAL STRATEGIES AND 30-DAY RISK-STANDARDIZED READMISSION RATES IN PERCUTANEOUS CORONARY INTERVENTION. Journal Of The American College Of Cardiology 2016, 67: 2105. DOI: 10.1016/s0735-1097(16)32106-4.Peer-Reviewed Original Research
2015
Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study
Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study. JACC Heart Failure 2015, 4: 12-20. PMID: 26656140, PMCID: PMC5459404, DOI: 10.1016/j.jchf.2015.07.017.Peer-Reviewed Original ResearchConceptsReadmission ratesPatient-reported informationHeart failureHealth statusReadmission riskC-statisticRisk scorePsychosocial variablesMedical record abstractionWeeks of dischargeReadmission risk modelNon-clinical factorsCandidate risk factorsReadmission risk predictionRecord abstractionClinical variablesPatient interviewsMedical recordsRisk factorsPatientsPsychosocial informationPsychosocial characteristicsTelephone interviewsRisk predictionScoresAssociation of hospital volume with readmission rates: a retrospective cross-sectional study
Horwitz LI, Lin Z, Herrin J, Bernheim S, Drye EE, Krumholz HM, Hines HJ, Ross JS. Association of hospital volume with readmission rates: a retrospective cross-sectional study. The BMJ 2015, 350: h447. PMID: 25665806, PMCID: PMC4353286, DOI: 10.1136/bmj.h447.Peer-Reviewed Original ResearchConceptsReadmission ratesHospital volumeRetrospective cross-sectional studyUS acute care hospitalsHospital readmission ratesAcute care hospitalsCross-sectional studyMedical cancer treatmentCare hospitalAdult dischargesHospital characteristicsMedicare feeCancer treatmentHospitalAssociationDaysService dataPatientsCardiovascularGynecologyQuintileNeurology