Date of Award


Document Type

Open Access Dissertation


Health Services and Policy Management

First Advisor

Zaina Qureshi


Introduction: Inpatient hospital readmission rates represent an important clinical and economic problem. Clinical interventions have shown significant decreases in preventable readmissions, but are costly to implement. Another approach is to better equip patients with the knowledge and resources to manage their care after discharge. Patients receive instruction from both nurses and physicians, as well as information pertaining to post-discharge care and instructions for care while at home. This study examines the association between provider communication and inpatient hospital readmissions.

Methods: This study used survey data from the 2013 and 2014 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). The sample included all inpatient facilities (n=4,063) for demographic and patient experience data, and a subset (n=MIN 1,906 MAX 2,283) of facilities where hospital acquired infections data were available. Shapiro-Wilk test and ordinary least squares (OLS) regression analysis were performed to analyze the data. The key communication variables tested were Nurse Communication, Physician Communication, Information for Recover, and Understood Care for Recovery.

Results: Physician Communication, and Information for Recovery were found to have significant association with readmission rates, while Nurse Communication and Understood Care for Recovery were found not significantly associated with readmissions. Physician Communication was found to have a negative correlation with readmissions (β= -0.032, 95% CI -0.053 - -0.011, p < .003), as did Information for Recovery (β = -0.062, 95% CI -0.082 - -0.043, p < .000).

Conclusions: Physician Communication is directly tied to a decrease in readmissions, with each percentage point (scale of 0 to 100) where patients identify the physician communication well relating to a decrease of .032% in inpatient 30-day readmission rates. Patients who indicate they had proper information for recovery at home were found to have a significant decrease of .062% in admissions using the same scale.

One additional finding in the study that was not part of the study, yet warrants future research, is the significant positive correlation between methicillin-resistant staphylococcus aureus infections (MRSA) and readmissions. Each 1% increase in MRSA rates resulted in an increase in readmissions by 0.11%. Also of note is the positive correlation between bed size and readmissions with each bed increasing readmissions by .001% and the significant indicator of facilities in the Northeast having a .772% increase in readmissions

While the findings were all statistically significant, with p-values well below 0.05 for the discussed variables, one limitation of this study is the R2 value. With the infection rates and hospital demographic information added into the regression, the R2 maxed out at 0.2490 with an adjusted R2 of 0.2386. However, many studies for behavioral sciences, including Jacob Cohen’s widely-cited 1988 study, found an R2 of .13 to be the minimum required to explain a moderate effect and .26 to explain a large effect, giving this study’s outcomes considerable explanatory power.

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