Open AccessA Predictive Risk Model for Infection-Related Hospitalization Among Home Healthcare Patients
Author(s) -
Jingjing Shang,
David Russell,
Dawn Dowding,
Margaret McDonald,
Christopher M. Murtaugh,
Jianfang Liu,
Elaine Larson,
Sridevi Sridharan,
Carlin Brickner
Publication year - 2020
Publication title -
journal for healthcare quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.374
H-Index - 27
eISSN - 1945-1474
pISSN - 1062-2551
DOI - 10.1097/jhq.0000000000000214
Subject(s) - medicine , emergency department , logistic regression , infection control , emergency medicine , health care , risk of infection , medline , infection risk , risk assessment , intensive care medicine , medical emergency , computer security , psychiatry , biology , political science , computer science , law , economics , genetics , economic growth
Infection prevention is a high priority for home healthcare (HHC), but tools are lacking to identify patients at highest risk of developing infections. The purpose of this study was to develop and test a predictive risk model to identify HHC patients at risk of an infection-related hospitalization or emergency department visit. A nonexperimental study using secondary data was conducted. The Outcome and Assessment Information Set linked with relevant clinical data from 112,788 HHC admissions in 2014 was used for model development (70% of data) and testing (30%). A total of 1,908 patients (1.69%) were hospitalized or received emergency care associated with infection. Stepwise logistic regression models discriminated between individuals with and without infections. Our final model, when classified by highest risk of infection, identified a high portion of those who were hospitalized or received emergent care for an infection while also correctly categorizing 90.5% of patients without infection. The risk model can be used by clinicians to inform care planning. This is the first study to develop a tool for predicting infection risk that can be used to inform how to direct additional infection control intervention resources on high-risk patients, potentially reducing infection-related hospitalizations, emergency department visits, and costs.