Document Type

Article

Publication Date

8-2018

Publication Title

Nursing Research

Volume

67

Issue

4

Pages

305-313

Abstract

Background Statistical models for predicting readmissions have been published for high-risk patient populations but typically focus on patient characteristics; nurse judgment is rarely considered in a formalized way to supplement prediction models.

Objectives The purpose of this study was to determine psychometric properties of long and short forms of the Registered Nurse Readiness for Hospital Discharge Scale (RN-RHDS), including reliability, factor structure, and predictive validity.

Methods Data were aggregated from two studies conducted at four hospitals in the Midwestern United States. The RN-RHDS was completed within 4 hours before hospital discharge by the discharging nurse. Data on readmissions and emergency department visits within 30 days were extracted from electronic medical records.

Results The RN-RHDS, both long and short forms, demonstrate acceptable reliability (Cronbach’s alphas of .90 and .73, respectively). Confirmatory factor analysis demonstrated less than adequate fit with the same four-factor structure observed in the patient version. Exploratory factor analysis identified three factors, explaining 60.2% of the variance. When nurses rate patients as less ready to go home (<7 out of 10), patients are 6.4–9.3 times more likely to return to the hospital within 30 days, in adjusted models.

Discussion The RN-RHDS, long and short forms, can be used to identify medical-surgical patients at risk for potential unplanned return to hospital within 30 days, allowing nurses to use their clinical judgment to implement interventions prior to discharge. Use of the RN-RHDS could enhance current readmission risk prediction models.

Comments

Author Posting. © Wolters Kluwer Health, Inc. 2018. This article is posted here by permission of Wolters Kluwer Health, Inc. for personal use, not for redistribution. The article was published in Nursing Research, 2018, https://doi.org/10.1097/NNR.0000000000000293

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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