Classifying Reasons For Hospital Readmissions

Tuesday, 19 November 2013: 10:00 AM

Kay R. Jansen, DNP, MSN, PMHCNS-BC, RN
College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, WI
Tae Youn Kim, PhD, RN
School of Nursing, University of California Davis, Sacramento, CA

Learning Objective 1: The learner will be able to describe the value of obtaining information from multiple sources regarding the reason(s) for hospital readmission.

Learning Objective 2: The learner will be able to identify one advantage of using a standard classification scheme for reasons for hospital readmission.

Unplanned hospital readmissions consume healthcare resources that increase costs and may decrease healthcare access for others. In the United States, for example, unplanned readmissions account for an estimated twenty-five billion dollars annually1. Efforts to predict risk for readmission generally use administrative data such as patient demographic characteristics, diseases and co-morbidities, number of previous admissions, and length of stay. Although such data may be used to screen for individuals at risk for hospital readmission, they are not sufficient for developing interventions that would target the reduction of readmissions, resulting in inequity of healthcare delivery. A standard classification scheme for hospital readmission would facilitate data collection and analysis of reasons for readmission. There are no widely used classification systems for hospital readmission. The purpose of this project was to develop a hospital readmission classification based on an integrative literature review and on data collected from patients, family members, nurses, physicians and the health record. Findings of the review supported a model for classifying hospital readmission that included four categories (patient, environment, clinical encounter, and organization)2 and 12 sub-categories. The classification provides a resource for nurse researchers, practitioners, and administrators to further examine reasons for hospital readmission and to target nursing interventions to reduce readmissions. Additional research is needed to refine, implement, and test the use of this classification in the electronic health record as a means to reduce readmissions while promoting positive outcomes for patients.