6533b855fe1ef96bd12affd2

RESEARCH PRODUCT

Hospital readmission rates: signal of failure or success?

Mauro LaudicellaPaolo Li DonniPeter C. Smith

subject

MaleMORTALITY-RATESEconomicsIMPACTSocial SciencesHospital performanceC50Business & EconomicsReadmission ratesmedia_commonAged 80 and overHip fractureOUTCOMESI18Mortality rateHealth PolicyHEALTH CARE SCIENCES & SERVICESHospitalsSurvival RateEngland1117 Public Health And Health ServicesMortality ratesFemaleMedical emergencyHEALTHLife Sciences & BiomedicineSample selectionmedicine.medical_specialtyACUTE MYOCARDIAL-INFARCTIONmedia_common.quotation_subjectBivariate analysisPatient ReadmissionReadmission ratemedicineQUALITYHumansSurvival rate1402 Applied EconomicsSelection (genetic algorithm)AgedQuality of Health CareSelection biasHospital readmissionSAMPLE SELECTIONScience & TechnologyModels Statisticalbusiness.industryHip FracturesPublic Health Environmental and Occupational HealthHIP FRACTUREHEALTH POLICY & SERVICESmedicine.diseaseMortality rateMODELEmergency medicinebusinessRACOSTS

description

AbstractHospital readmission rates are increasingly used as signals of hospital performance and a basis for hospital reimbursement. However, their interpretation may be complicated by differential patient survival rates. If patient characteristics are not perfectly observable and hospitals differ in their mortality rates, then hospitals with low mortality rates are likely to have a larger share of un-observably sicker patients at risk of a readmission. Their performance on readmissions will then be underestimated. We examine hospitals’ performance relaxing the assumption of independence between mortality and readmissions implicitly adopted in many empirical applications. We use data from the Hospital Episode Statistics on emergency admissions for fractured hip in 290,000 patients aged 65 and over from 2003 to 2008 in England. We find evidence of sample selection bias that affects inference from traditional models. We use a bivariate sample selection model to allow for the selection process and the dichotomous nature of the outcome variables.

10.1016/j.jhealeco.2013.06.004http://hdl.handle.net/10044/1/9224