Featured Publications
Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment
Herrin J, Abraham NS, Yao X, Noseworthy PA, Inselman J, Shah ND, Ngufor C. Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment. JAMA Network Open 2021, 4: e2110703. PMID: 34019087, PMCID: PMC8140376, DOI: 10.1001/jamanetworkopen.2021.10703.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAnticoagulantsAntifibrinolytic AgentsAtrial FibrillationClinical Decision-MakingCohort StudiesCross-Sectional StudiesFemaleFibrinolytic AgentsGastrointestinal HemorrhageHumansMachine LearningMaleMiddle AgedMyocardial IschemiaPredictive Value of TestsRetrospective StudiesRisk AssessmentThienopyridinesUnited StatesVenous ThromboembolismYoung AdultConceptsGastrointestinal bleedingIschemic heart diseaseCross-sectional studyThienopyridine antiplatelet agentAntithrombotic treatmentVenous thromboembolismAntiplatelet agentsRandom survival forestStudy cohortAtrial fibrillationValidation cohortHeart diseaseHAS-BLED risk scoreRetrospective cross-sectional studyCox proportional hazards regressionHAS-BLED scorePrior GI bleedPatients 18 yearsCohort of patientsEntire study cohortProportional hazards regressionOptumLabs Data WarehouseMedicare Advantage enrolleesPositive predictive valueRisk prediction model
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
2015
Association 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