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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 2 of 2 Research Studies DisplayedCimiotti JP, Becker ER, Li Y
Association of registered nurse staffing with mortality risk of Medicare beneficiaries hospitalized with sepsis.
The purpose of this cross-sectional study was to determine if registered nurse workload was related with mortality in Medicare beneficiaries admitted to an acute care hospital with sepsis. The researchers evaluated the records of Medicare beneficiaries ages 65 to 99 years with a primary diagnosis of sepsis that was present on admission to 1 of 1958 nonfederal, general acute care hospitals that had data on CMS SEP-1 scores and registered nurse workload. Researchers utilized 2018 data from the American Hospital Association Annual Survey, CMS Hospital Compare, and Medicare claims. The patient outcome of interest was mortality within 60 days of admission. The study found that 702,140 Medicare beneficiaries with a mean age of 78.2 years, 51% of whom were women, had a diagnosis of sepsis. In a multivariable regression model, each additional registered nurse hour per patient day (HPPD) was associated with a 3% decrease in the odds of 60-day mortality. The researchers concluded that hospitals which provide more registered nurse hours of care could possibly decrease the likelihood of mortality in Medicare beneficiaries with sepsis.
AHRQ-funded; HS026232.
Citation: Cimiotti JP, Becker ER, Li Y .
Association of registered nurse staffing with mortality risk of Medicare beneficiaries hospitalized with sepsis.
JAMA Health Forum 2022 May;3(5):e221173. doi: 10.1001/jamahealthforum.2022.1173..
Keywords: Sepsis, Mortality, Provider: Nurse, Nursing, Workforce
Cohen B, Sanabria E, Liu J
Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index.
The purpose of this study was to develop, validate, and utilize a simulation model to predict healthcare-associated infections (HAIs), length of stay (LOS), and patient mortality, as well as evaluate whether the variation in incidence of HAIs was dependent upon the adequacy of unit staffing levels. The researchers analyzed data from all patients discharged from four different types of New York City hospitals within a single healthcare network between 2012-2016 (N=562,435). The researchers developed a simulation model to estimate the daily probability rates of 5 different HAIs, length of stay, and mortality, and modeled staffing adequacy based on nursing care supply (as indicated by total nurse staffing) and nursing care demand (indicated using the Nursing Intensity of Care Index.) The study results indicated that the model predictions were within 95% confidence intervals of the actual outcomes. The authors reported that the incidence of HAI was the highest when total nurse staffing (supply) was lowest and nursing care intensity (demand) was highest.
AHRQ-funded; HS024915.
Citation: Cohen B, Sanabria E, Liu J .
Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index.
Infect Control Hosp Epidemiol 2022 Mar;43(3):298-305. doi: 10.1017/ice.2021.114..
Keywords: Healthcare-Associated Infections (HAIs), Provider: Nurse, Inpatient Care, Mortality