PURPOSE: The purpose of this study was to propose optimal hospitalization fees for nurse staffing levels and to improve the current nursing fee policy. METHODS: A break-even analysis was used to evaluate the impact of a nursing fee policy on hospital's financial performance. Variables considered included the number of beds, bed occupancy rate, annual total patient days, hospitalization fees for nurse staffing levels, the initial annual nurses' salary, and the ratio of overhead costs to nursing labor costs. Data were collected as secondary data from annual reports of the Hospital Nursing Association and national health insurance. RESULTS: The hospitalization fees according to nurse staffing levels in general hospitals are required to sustain or decrease in grades 1, 2, 3, 4, and 7, and increase in grades 5 and 6. It is suggested that the range between grade 2 and 3 be sustained at the current level, the range between grade 4 and 5 be widen or merged into one, and the range between grade 6 and 7 be divided into several grades. CONCLUSION: Readjusting hospitalization fees for nurse staffing level will improve nurse-patient ratio and enhance the quality of nursing care in hospitals. Follow-up studies including tertiary hospitals and small hospitals are recommended.
Bed Occupancy/economics ; Costs and Cost Analysis ; Hospitals, General/*economics ; Humans ; National Health Programs/*economics ; Nurse-Patient Relations ; Nursing Care ; Nursing Staff, Hospital/economics

PURPOSE: The purpose of this study was to investigate the casual relationship between nurses' social capital and turnover intention and to verify the goodness of fit between a hypothetical model and actual data in order to suggest the best model. METHODS: This survey was conducted with 315 nurses working in general hospitals in Seoul. Data were collected from December 1 to December 30, 2011, and analyzed using SPSS Windows 18.0 and AMOS 16.0. RESULTS: Nurses' social capital was found to have a direct effect on reducting organization cynicism and increasing organizational commitment. Nurses' organizational cynicism and organizational commitment were found to have a direct effect on turnover intention, but social capital did not have a direct effect on turnover intention. However, social capital had a partial and indirect effect on turnover intention through mediating organizational cynicism and organizational commitment. CONCLUSION: Results of this study indicate that nurse managers should put increased effort in reducing nurses' organizational cynicism and improving their organizational commitment, two contrary parameters. At the same time managers need to develop plans to establish social capital more efficiently so that nurses have lower turnover intention.
Adult ; Female ; Hospitals, General ; Humans ; Intention ; Nursing Staff, Hospital/economics/*psychology ; *Organizational Culture ; *Personnel Turnover ; Questionnaires

PURPOSE: To analyze the structure of Korean nurse labor market and examine its effect on hospital nurse staffing. METHODS: Secondary data were obtained from Statistics Korea, Education Statistics, and Health Insurance Review & Assessment Service and Patient Survey. Intensity of monopsony in the nurse labor market was measured by Herfindahl Hirshman Index (HHI). Hospital nurse staffing level was divided into high and low. While controlling for confounding factors such as inpatient days and severity mix of patients, effects of characteristics of nurse labor markets on nurse staffing levels were examined using multi-level logistic regressions. RESULTS: For characteristics of nurse labor markets, metropolitan areas had high intensity of monopsony, while the capital area had competitive labor market and the unemployed nurse rate was higher than other areas. Among hospital characteristics, bed occupancy rate was significantly associated with nurse staffing levels. Among characteristics of nurse labor markets, the effect of HHI was indeterminable. CONCLUSION: The Korean nurse labor market has different structure between the capital and other metropolitan areas. But the effect of the structure of nurse labor market on nurse staffing levels is indeterminable. Characteristics such as occupancy rate and number of beds are significantly associated with nurse staffing levels. Further study in support of the effect of nurse labor market is needed.
Hospitals ; Humans ; Logistic Models ; Nursing Staff, Hospital/economics/*supply & distribution ; Workplace

BACKGROUND: Adequate staffing is necessary to meet patient care needs and provide safe, quality nursing care. In November 1999, the Korean government implemented a new staffing policy that differentiates nursing fees for inpatients based on nurse-to-bed ratios. The purpose was to prevent hospitals from delegating nursing care to family members of patients or paid caregivers, and ultimately deteriorating the quality of nursing care services. PURPOSE: To examine nurse staffing levels and related factors including hospital, nursing and medical staff, and financial characteristics. METHODS: A cross-sectional design was employed using two administrative databases, Medical Care Institution Database and Medical Claims Data for May 1-31, 2002. Nurse staffing was graded from 1 to 6, based on grading criteria of nurse-to-bed ratios provided by the policy. The study sample consisted of 42 tertiary and 186 general acute care hospitals. RESULTS: None of tertiary or general hospitals gained the highest nurse staffing of Grade 1 (i.e., less than 2 beds per nurse in tertiary hospitals; less than 2.5 beds per nurse in general hospitals). Two thirds of the general hospitals had the lowest staffing of Grade 6 (i.e., 4 or more beds per nurse in tertiary hospitals; 4.5 or more beds per nurse in general hospitals). Tertiary hospitals were better staffed than general hospitals, and private hospitals had higher staffing levels compared to public hospitals. Large-sized general hospitals located in metropolitan areas had higher staffing than other general hospitals. Occupancy rate was positively related to nurse staffing. A negative relationship between nursing assistant and nurse staffing was found in general hospitals. A greater number of physician specialists were associated with better nurse staffing. CONCLUSIONS: The staffing policy needs to be evaluated and modified to make it more effective in leading hospitals to increase nurse staffing.
Workload/economics ; Program Evaluation ; Personnel Staffing and Scheduling/*economics ; Nursing Staff, Hospital/economics/*supply & distribution ; Logistic Models ; Korea ; Humans ; *Hospital Charges ; *Health Policy ; Cross-Sectional Studies ; Bed Occupancy/economics ; Analysis of Variance

PURPOSE: This study was done to analyze variations in unit staffing and recommend policies to improve nursing staffing levels in intensive care units (ICUs). METHOD: A cross-sectional study design was used, employing survey data from the Health Insurance Review Agency conducted from June-July, 2003. Unitstaffing was measured using two indicators; bed-to-nurse (B/N) ratio (number of beds per nurse), and patient-to-nurse (P/N)ratio (number of average daily patients per nurse). Staffing levels were compared according to hospital and ICU characteristics. RESULT: A total of 414 institutions were operating 569 adult and 86 neonatal ICUs. Tertiary hospitals (n=42) had the lowest mean B/N (0.82) and P/N (0.76) ratios in adult ICUs, followed by general hospitals (B/N: 1.34, P/N: 0.97). Those ratios indicated that a nurse took care of 3 to 5 patients per shift. Neonatal ICUs had worse staffing and had greater variations in staffing ratios than adult ICUs. About 17% of adult and 26% of neonatal ICUs were staffed only by adjunct nurses who had responsibility for a general ward as well as the ICU. CONCLUSION: Stratification of nurse staffing levels and differentiation of ICU utilization fees based on staffing grades are recommended as a policy tool to improve nurse staffing in ICUs.
Analysis of Variance ; Female ; Humans ; Intensive Care Units/economics/*manpower/statistics & numerical data ; Intensive Care Units, Neonatal/economics/*manpower/statistics & numerical data ; Nursing Staff, Hospital/economics/*supply & distribution ; Personnel Staffing and Scheduling/*economics ; Workload