간호사 확보수준이 입원 환자의 병원사망과 입원 30일 이내 사망에 미치는 영향 (Effects of Nurse Staffing Level on In-hospital Mortality and 30-day Mortality after Admission using Korean National Health Insurance Data)
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- 임상간호연구
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- 제28권1호
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- pp.1-12
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- 2022
Purpose: The purpose of this study is to investigate the association between the nurse staffing level and the patient mortality using Korean National Health Insurance data. Methods: The data of 1,068,059 patients from 913 hospitals between 2015 and 2016 were analyzed. The nurse staffing level was categorized based on the bed-to-nurse ratio in general wards, intensive care units (ICUs), and hospitals overall. The x2 test and generalized estimating equations (GEE) multilevel multivariate logistic regression analyses were used to explore in-hospital mortality and 30-day mortality after admission. Results: The in-hospital mortality rate was 2.9% and 30-day mortality after admission rate was 3.0%. Odd Ratios (ORs) for in-hospital mortality were statistically lower in general wards with a bed-to-nurse ratio of less than 3.5 compared to that with 6.0 or more (OR=0.72, 95% CI=0.63~0.84) and in ICUs with a bed-to-nurse ratio of less than 0.88 compared to that with 1.25 or more (OR=0.78, 95% CI=0.66~0.92). ORs for 30-day mortality after admission were statistically lower in general wards with a bed-to-nurse ratio of less than 3.5 compared to that with 6.0 or more (OR=0.83, 95% CI=0.73~0.94) and in ICUs with a bed-to-nurse ratio of less than 0.63 compared to that with 1.25 or more (OR=0.85, 95% CI=0.72~1.00). Conclusion: To reduce the patient mortality, it is necessary to ensure a sufficient number of nurses by improving the nursing fee system according to the nurse staffing level.
Objectives: This study analyzed the Korea Health Panel Annual Data 2019 to investigate factors related to the use of non-insured Korean medicine (KM) treatment in individuals with chronic diseases. The non-insured KM treatments of interest were herbal decoction (HD) and pharmacopuncture (PA). Methods: Among adults aged 19 or older, 6,159 individuals with chronic diseases who received outpatient KM treatment at least once in 2019 were included. They were divided into three groups according to the KM treatment used: (1) basic insured KM non-pharmacological treatment (BT) group (n = 629); (2) HD group (n = 256); (3) PA group (n = 184). Logistic regression analysis was used to explore factors associated with favoring HD or PA use over BT. Potentially relevant candidate factors were classified using the Andersen Behavior Model. Results: Compared to BT, the 1st to 3rd quartiles of income compared to the 4th quartile (odds ratio: 1.50 to 2.06 for HD; 2.03 to 2.83 for PA), health insurance subscribers compared to medical aid (odds ratio: 2.51; 13.43), and presence of musculoskeletal diseases (odds ratio: 1.66; 1.91) were significantly positively associated with HD and PA use. Moreover, the presence of cardiovascular disease (odds ratio: 1.46) and neuropsychiatric disease (odds ratio: 1.97) were also significantly positively associated with HD use. Conclusion: The presence of some chronic diseases, especially musculoskeletal diseases, was significantly positively associated with HD and PA use, while low economic status was significantly negatively associated with HD and PA use, indicating the potential existence of unmet medical needs in this population. Since chronic diseases impose a considerable health burden, the results of this study can be used for reference for future health insurance coverage policies in South Korea.
Background: Financial efficiency in monetary units and operational efficiency in non-monetary units are separately classified and evaluated. This is done to prevent the duplication of monetary units and non-monetary units in inputs and outputs. In addition, analyses are conducted to determine the factors that affect each aspect of efficiency. To prevent duplication of monetary and non-monetary units in inputs and outputs, financial efficiency, consisting of monetary units, and operational efficiency, comprising non-monetary units, are separately classified and evaluated. Furthermore, an analysis is conducted to identify the factors that affect each aspect of efficiency. Methods: This study conducted a panel analysis of 34 regional public hospitals and influencing factors on efficiency for 5 years from 2015 to 2019. Financial efficiency and operational efficiency were calculated through data envelopment analysis. Moreover, multiple regression analysis was conducted to identify the factors that influence both financial efficiency and operational efficiency. Results: The factors that affect financial efficiency include the number of medical institutions within the treatment area and the ratio of patients receiving medical care. Additionally, operational efficiency is influenced by the type of medical institution, the number of medical institutions within the treatment area, and the number of nursing positions per 100 beds. Conclusion: In order for regional public hospitals to faithfully fulfill their functions and roles as regional base public hospitals, several measures are necessary. Firstly, continuous monitoring and reasonable support are required to ensure efficient operation and performance. Secondly, a financial support plan tailored to the characteristics of local medical centers is needed. Additionally, local medical centers should strive to enhance their own efficiency.
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