1. A Predictive Risk Model for Infection-Related Hospitalization Among Home Healthcare Patients
- Author
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Elaine Larson, Sridevi Sridharan, David Russell, Carlin Brickner, Margaret V. McDonald, Jingjing Shang, Christopher M. Murtaugh, Jianfang Liu, and Dawn Dowding
- Subjects
Adult ,Male ,Emergency Medical Services ,medicine.medical_specialty ,home health care ,MEDLINE ,Infections ,Risk Assessment ,Article ,03 medical and health sciences ,Risk model ,0302 clinical medicine ,Surveys and Questionnaires ,Intervention (counseling) ,Health care ,medicine ,Humans ,Infection control ,030212 general & internal medicine ,Aged ,Aged, 80 and over ,OASIS ,business.industry ,030503 health policy & services ,Health Policy ,Risk of infection ,Public Health, Environmental and Occupational Health ,risk modeling ,Emergency department ,Middle Aged ,Stepwise regression ,Home Care Services ,United States ,Hospitalization ,Logistic Models ,Emergency medicine ,Female ,Infection ,0305 other medical science ,business - Abstract
Infection prevention is a high priority for home healthcare (HHC) but tools are lacking to identify patients at highest risk for 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 non-experimental study using secondary data was conducted. The Outcome and Assessment Information Set linked with relevant clinical data from 112,788 HHC admissions in 2014 were 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.
- Published
- 2020