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