Home Care Workers' Judgment of Acute Conditions in Home Care Patients: A Retrospective Cohort Study. Issue 1 (February 2020)

• Record Type: Journal Article
• Authors: Ohta, Ryuichi; Sakamoto, Naoto; Maeno, Tetsuhiro
• Is Part Of: Home health care management & practice. Volume 32:Issue 1(2020)
• Page Start: 3
• Page End: 9
• Publication Date: 2020-02
• Subjects: acute care -- home care worker -- long-term insurance care -- older adults -- acute care sensitive condition; Home care services -- Periodicals; 649
• DOI: 10.1177/1084822319861095
• Languages: English
• ISSNs: 1084-8223

Abstract

Home care workers are likely to detect acute conditions in home care patients. Obtaining data about home care workers' detection of acute conditions is essential for improving the present quality of home care. This study investigates how home care workers approach the acute conditions of their patients. A retrospective cohort study was conducted with all home care patients who received services from home care workers in Unnan City, Shimane, Japan. The primary outcome was the frequency of acute conditions occurring in home care patients. We used logistic regression to model the acute conditions of the patients based on their age, gender, dependent condition, home-care-worker visit frequency, isolation, and number of chronic diseases. Of 445 participants, 74 had acute conditions (16.6%). The most common among the acute conditions was fever. There were no statistically significant relationships found between admission and the independent variables. The results of the logistic regression model demonstrated that visit frequency and isolation were statistically significant (odds ratio [OR] = 1.24, 95% confidence interval [CI] = [1.08, 1.43], p < .001; OR = 1.95, 95% CI = [1.09, 3.09], p = .001). This research shows that home care workers may be able to identify the early stages of acute diseases in their patients. The improvement of their detection skills may contribute to better health care for home care patients. The application of support systems such as information and communication technologies can promote the identification of acute conditions.