2018
Admission 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
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 predictionScoresPatient and family engagement: a survey of US hospital practices
Herrin J, Harris KG, Kenward K, Hines S, Joshi MS, Frosch DL. Patient and family engagement: a survey of US hospital practices. BMJ Quality & Safety 2015, 25: 182. PMID: 26082560, PMCID: PMC4789699, DOI: 10.1136/bmjqs-2015-004006.Peer-Reviewed Original Research
2013
Translating comparative effectiveness of depression medications into practice by comparing the depression medication choice decision aid to usual care: study protocol for a randomized controlled trial
LeBlanc A, Bodde AE, Branda ME, Yost KJ, Herrin J, Williams MD, Shah ND, Houten HV, Ruud KL, Pencille LJ, Montori VM. Translating comparative effectiveness of depression medications into practice by comparing the depression medication choice decision aid to usual care: study protocol for a randomized controlled trial. Trials 2013, 14: 127. PMID: 23782672, PMCID: PMC3663744, DOI: 10.1186/1745-6215-14-127.Peer-Reviewed Original ResearchMeSH KeywordsAntidepressive AgentsClinical ProtocolsComparative Effectiveness ResearchDecision Support TechniquesDepressionHumansMedication AdherenceMental HealthMidwestern United StatesPatient SelectionPatient-Centered CarePrimary Health CareResearch DesignSurveys and QuestionnairesTime FactorsTreatment OutcomeConceptsUsual depression carePrimary care practicesDepression careInner-city primary care practiceCare practicesPractice-based trialPrimary care encountersTerms of efficacyDecision aidPatient-centered approachDesignThe objectiveAntidepressant therapyUsual careMedication choiceDepression medicationsDepression treatmentMedication adherencePatient knowledgeStudy protocolSevere depressionCare encountersImproved adherencePatient representativesPatient involvementPatients