Nurse Staffing Levels Linked to Safety Outcomes for Patients: Study

The number and types of nurses working on a hospital staff have a significant effect on the safety outcomes for patients, according to the findings of a new study. 

Researchers from the London South Bank University and Birmingham City University indicate that the nursing staff make-up can directly affect the risk of patient falls, as well as nausea and vomiting. The findings were published earlier this month in the medical journal BMJ Open.

The study looked at patient data from the National Health Service hospital trust in England, analyzing physiological data from 120 million patient entries over six years.

According to the findings, when some aspects of patient care are regulated to unregistered nurses or healthcare support workers, instead of registered nurses, the risk of falls increase, and the management of symptoms like nausea and vomiting decrease.

Patient falls not only affect patient safety due to the risk of injury, but also can cause loss of independence, additional financial burden, and a loss of confidence on behalf of the patient, the researchers warn. A press release on the study issued by Birmingham City University suggests that replacing six healthcare support workers with six registered nurses on wards with high rates of patient falls could reduce the number of falls by as much as 15%.

“We must look at the usefulness of the currently collected data and how it might be used to shape hospital safety,” Professor Alison Leary, of London South Bank University, and one of the study’s authors, said in the press release. “This was a very exciting project to work on as it’s a different way of thinking about the contribution nurses make to patient safety. We were very surprised that so many signals emerged from the data and it is useful that we were able to feed the new knowledge back to the Trust who then used it in many different ways to look at patient safety.”

Researchers determined that hospitals could use a data-driven approach to develop optimum staffing level models in the same way that such data and models are used in industry such as retail, banking, and aviation where safety is critical.

“The relationship between staffing and outcomes appears to exist. It appears to be non-linear but calculable and a data-driven model appears possible,” the researchers concluded. “These findings could be used to build an initial mathematical model for acute staffing which could be further tested. It could even lead to whole systems approaches to modelling staffing, including predictive modelling.”