Kathleen L Bobay1, Marianne E Weiss, Debra Oswald, Olga Yakusheva. 1. Kathleen L. Bobay, PhD, RN, NEA-BC, is Professor, Marcella Niehoff School of Nursing, Loyola University Chicago, Maywood, Illinois. Marianne E. Weiss, DNSc, RN, is Wheaton Franciscan Healthcare St. Joseph/Sister Rosalie Klein Professor of Women's Health Professor, Marquette University School of Nursing, Milwaukee, Wisconsin Debra Oswald, PhD, is Professor, Department of Psychology, Marquette University, Milwaukee, Wisconsin. Olga Yakusheva, PhD, is Associate Professor, University of Michigan School of Nursing, Ann Arbor.
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.
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.
Authors: Kirstin A Manges; Andrea S Wallace; Patricia S Groves; Marilyn M Schapira; Robert E Burke Journal: J Hosp Med Date: 2021-06 Impact factor: 2.899
Authors: Marianne E Weiss; Olga Yakusheva; Kathleen L Bobay; Linda Costa; Ronda G Hughes; Susan Nuccio; Morris Hamilton; Sarah Bahr; Danielle Siclovan; James Bang Journal: JAMA Netw Open Date: 2019-01-04