Nurse staffing and patient mortality in intensive care units.
BACKGROUND
Research evidence suggests that nurse staffing influences patient outcomes.


OBJECTIVE
To examine the relationship between nurse staffing and patient mortality in Korean intensive care units (ICUs).


METHODS
Using survey and administrative databases, this study included 27,372 ICU patients discharged from 42 tertiary and 194 secondary hospitals. Ownership (public vs. private), location (metropolitan city vs. province), size, specialization of ICUs (specialized vs. mixed), physician staffing and nurse staffing, and years of nurse experience were included as hospital and ICU characteristics. Nurse staffing was measured as the ratio of average daily census to the total number of full-time equivalent registered nurses in ICUs. Age, gender, 26 categories of primary diagnoses, 16 groups of comorbid conditions, and source of payment were used for risk adjustment. Mortality was defined as deaths that occurred in the hospital or on the date of hospital discharge. Using SAS GLIMMIX procedures, multivariate logistic regression analysis was employed separately for tertiary and secondary hospitals.


RESULTS
In tertiary hospitals, a greater likelihood of dying was found among patients who were admitted to a mixed ICU (odds ratio [OR] = 1.61, 95% confidence interval [CI] = 1.14-2.26) and where there was no board-certified physician present for 4 or more hours per day (OR = 1.56, 95% CI = 1.20-2.01). In secondary hospitals, every additional patient per RN was associated with a 9% increase in the odds of dying (OR = 1.09, 95% CI = 1.04-1.14). Nurse experience had no significant relationship with mortality.


CONCLUSIONS
Nurse and physician staffing and specialization of ICUs impacted patient mortality. Associations differed in tertiary and secondary hospitals. Further investigation of the impact of organizational environments in ICUs is needed.
Cho
Sung-Hyun
SH
Department of Nursing, Hanyang University, Korea.
Hwang
Jeong Hae
JH
Kim
Jaiyong
J
D000328:Adult; D017677:Age Distribution; D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data; D002423:Cause of Death; D005260:Female; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D007723:Korea / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015401:Nursing Administration Research; D015403:Nursing Evaluation Research; D009741:Nursing Staff, Hospital / Q000600:supply & distribution*; D017063:Outcome Assessment, Health Care; D010067:Ownership / Q000706:statistics & numerical data; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration*; D020379:Risk Adjustment; D012307:Risk Factors; D017678:Sex Distribution; D000078329:Workforce; D016526:Workload / Q000706:statistics & numerical data