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• W4312076114 abstract "Aim We analyze 30-day hospital readmission trends of Congestive Heart Failure (CHF) patients by LOS for different sub-groups, address the potential endogeneity bias in LOS, and examine the influence of patient’s discharge location on readmission risk using nationwide multi-year Big Data.Methods Using the 2010–2017 National Readmissions Database (NRD), we estimate an ordinary least squares (OLS) regression and an instrumental variable (IV) model. We use a data visualization approach to show trends by various characteristics and address the potential endogeneity bias in LOS using the IV model.Results Readmitted CHF patients had longer LOS (5.37 days versus 4.80 days) and more medical comorbidities (4.0 versus 3.6), compared to non-readmitted patients. The readmission rates vary depending on patients’ primary payee with Medicaid/Medicare patients exhibiting the highest readmission rates. A patient’s hospital discharge type and hospital ownership also influence the probability of readmission.Conclusions LOS and CCI provide meaningful information in predicting a 30-day hospital readmission risk, but more importantly longer LOS reduces readmission risk. Studying these factors could provide better insights to all stakeholders and allow the healthcare industry to develop effective strategies to reduce readmission rates while improving patients’ quality of care during their hospitalization." @default.
• W4312076114 created "2023-01-04" @default.
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• W4312076114 date "2022-12-22" @default.
• W4312076114 modified "2023-10-16" @default.
• W4312076114 title "Examining readmission rates of congestive heart failure patients in the United States between 2010 and 2017: Does length of stay matter?" @default.
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• W4312076114 doi "https://doi.org/10.1080/20479700.2022.2157074" @default.
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