Examining readmission rates of congestive heart failure patients in the United States between 2010 and 2017: Does length of stay matter?

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.

KEYWORDS:

• Length of stay
• readmission
• congestive heart failure (CHF)
• quality of care
• HCUP

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nasibeh Azadeh-Fard

Nasibeh Azadeh-Fard, PhD is an Assistant Professor of Industrial and Systems Engineering at Rochester Institute of Technology (RIT). She holds a PhD in Industrial and Systems Engineering from Virginia Tech, and her research interests include healthcare systems, data analytics, and risk analysis. Prior to joining RIT, she was a postdoctoral fellow at Clemson University's Risk Engineering and System Analytics Center, where she conducted research on risk analysis and risk mitigation action plans for American International Group (AIG) insurance company using their Big Data. She has published several papers in peer-reviewed journals and presented her research outcomes in well-known national conferences such as INFORMS and the Institute for Industrial and Systems Engineering (IISE).

Steve Muchiri

Steve Muchiri, MBA, PhD is a tenured Associate Professor of Economics and the Labor and Human Resources Major (LHR) coordinator. He holds a PhD in economics from the University of Kentucky. His research interest includes issues in health and labor.

Fatma Pakdil

Fatma Pakdil, MBA, PhD is a tenured Professor of Management at Eastern Connecticut State University (ECSU). She holds her BS in Econometrics and PhD in Management and Organization. She researches quality management, lean management, Six Sigma, business analytics, and healthcare management since 2000. Dr. Pakdil is an internationally recognized scholar on lean management, quality management, and healthcare management topics.

Hannah Beazoglou

Hannah Beazoglou is an associate in CVS Health's General Management Development Program. Her current placement is with Network Strategy and Provider Experience at Aetna, a CVS Health company. She received a BS degree in Business Administration from Eastern Connecticut State University (ECSU).