Changes in proportion of bachelor's nurses associated with improvements in patient outcomes.
Karen B LasaterDouglas M SloaneMatthew D McHughJoshua Porat-DahlerbruchLinda H AikenPublished in: Research in nursing & health (2021)
This study uses data from two cross-sections in time (2006, 2016) to determine whether changes over time in hospital employment of bachelor's of science in nursing (BSN) nurses is associated with changes in patient outcomes. Data sources include nurse survey data, American Hospital Association Annual Survey data, and patient administrative claims data from state agencies in California, Florida, New Jersey, and Pennsylvania. The study sample included general surgical patients aged 18-99 years admitted to one of the 519 study hospitals. Multilevel logistic regression and truncated negative binomial models were used to estimate the cross-sectional and longitudinal effects of the proportion of hospital BSN nurses on patient outcomes (i.e., in-hospital mortality, 7- and 30-day readmissions, length of stay). Between 2006 and 2016, the average proportion of BSN nurses in hospitals increased from 41% to 56%. Patients in hospitals that increased their proportion of BSN nurses over time had significantly reduced odds of risk-adjusted mortality (odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.92-0.98), 7-day readmission (OR: 0.96, 95% CI: 0.94-0.99) and 30-day readmission (OR: 0.98, 95% CI: 0.95-1.00), and shorter lengths of stay (incident rate ratio [IRR]: 0.98, 95% CI: 0.97-0.99). Longitudinal findings of an association between increased proportions of BSN nurses and improvements in patient outcomes corroborate previous cross-sectional research, suggesting that a better educated nurse workforce may add value to hospitals and patients.
Keyphrases
- healthcare
- cross sectional
- mental health
- electronic health record
- end stage renal disease
- big data
- public health
- newly diagnosed
- ejection fraction
- cardiovascular disease
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- emergency department
- type diabetes
- machine learning
- health insurance
- coronary artery disease
- adverse drug
- case report
- nursing students
- artificial intelligence
- patient reported outcomes
- acute care
- data analysis