2017
Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring
Steventon A, Chaudhry SI, Lin Z, Mattera JA, Krumholz HM. Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring. BMC Medical Informatics And Decision Making 2017, 17: 43. PMID: 28420352, PMCID: PMC5395848, DOI: 10.1186/s12911-017-0426-4.Peer-Reviewed Original ResearchMeSH KeywordsBody WeightDiagnostic ErrorsFemaleFraudHeart FailureHumansMaleMiddle AgedMonitoring, AmbulatoryReproducibility of ResultsSelf ReportTelemedicineConceptsEnd-digit preferenceHeart failureHeart Failure Outcomes trialEffective preventive careCharacteristics of patientsSelf-reported weightHealth care professionalsSix-month trial periodIntervention patientsMore medicationsAccuracy of reportingOutcome trialsTrial enrollmentPreventive careClinical managementUnnecessary treatmentDesign of initiativesCare professionalsPatientsRegistration numberAlert fatigueElectronic medical dataTrial periodTrialsNumber of daysReal-World Data on Heart Failure Readmission Reduction Real or Real Uncertain?∗
Krumholz HM, Dhruva SS. Real-World Data on Heart Failure Readmission Reduction Real or Real Uncertain?∗. Journal Of The American College Of Cardiology 2017, 69: 2366-2368. PMID: 28330750, DOI: 10.1016/j.jacc.2017.03.019.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 predictionScores
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