Better staffing linked to fewer Caesarean sections

Recent research published in the journal Nursing Outlook has found that when labour and delivery units in the US were adequately staffed by specialist nurses (similar to the midwife role in Ireland), there were lower Caesarean birth rates. 

“Our findings highlight how crucial nurse staffing is for optimal maternal outcomes,” said Audrey Lyndon, professor in health equity and executive vice dean at New York University Rory Meyers College of Nursing.

In the US, Caesarean sections account for nearly one-third of births and are the most common surgery performed in hospitals. Ireland, which has one of the highest C-section rates in the EU, has an even higher rate of 39% of births.¹ While Caesareans are often necessary for the health of the mother and child and can be lifesaving, the surgery carries more risks and a longer recovery than vaginal births and can cause complications in future pregnancies.

“If we can safely lower the C-section rate, we are improving outcomes for childbearing people and their families,” said Prof Lyndon.

In the US labour and delivery nurses play a vital role during childbirth, providing emotional and physical support at the bedside, monitoring the health of the mother and baby, and administering medication when required. The research found that when hospitals were understaffed, these nurses were forced to prioritise tasks that required the most immediate attention at the expense of other care.

“While nurses intuitively know that having enough nurses to provide the attentive care that mother and babies need and deserve improves outcomes, research has been minimal in linking maternity nurse staffing and patient outcomes,” said Kathleen Rice Simpson, a perinatal clinical nurse specialist and study author.

To determine if nurse staffing influenced that rate of Caesareans, the researchers examined how well maternity units adhered to staffing standards established by the Association of Women’s Health, Obstetric and Neonatal Nurses. These evidence-based standards call for one nurse to one birthing patient during labour, two nurses at birth and one nurse each for the mother and newborn in the first few hours post birth.

They surveyed 2,786 nurses from 193 hospitals across 23 US states about staffing on their maternity units. Collected in 2018 and 2019, these findings were matched with hospital administrative data and the rates of birth types.

It was found that better staffing during labour and birth was linked to lower Caesarean rates and higher vaginal birth rates; notably, this included higher vaginal births among mothers who had previously had sections. Caesarean rates were 11% lower in hospitals where nurse staffing aligned with the national standards.

The authors of the study said that their findings point toward one solution to address high Caesarean rates: to align staffing with expert-developed guidelines. The researchers also noted that the cost of adequate staffing during labour and birth could be balanced by the savings of avoiding unnecessary C-sections, including shorter hospital stays and fewer complications.

DOI: 10.1016/j.outlook.2024.102346

AI can use nursing data to save lives

An artificial intelligence (AI) tool that can analyse nurses’ notes detected when patients in the hospital were deteriorating nearly two days ahead of traditional methods, reducing the risk of death by over 35%. This was according to a year-long clinical trial of more than 60,000 patients led by researchers at Columbia University in the US. 

The CONCERN Early Warning System AI tool uses machine learning to analyse nursing documentation patterns to predict when a patient is deteriorating before the change is reflected in vital signs, allowing for timely intervention.

In the study, published in the journal Nature Medicine, CONCERN reduced the average hospital stay by more than 12 hours and led to a 7.5% decrease in risk of sepsis. Patients monitored by CONCERN were roughly 25% more likely to be transferred to an intensive care unit compared to those who had usual care.

Nurses often recognise subtle signs that a patient is deteriorating, such as pallor change or small changes in mental status. But these concerns, when noted in a patient’s electronic health record, might not cause immediate intervention.

CONCERN analyses when nurses identify and respond to these small, but meaningful changes, by looking at their increased surveillance of patients, including frequency and time of assessments, in a model that generates hourly, easy-to-read risk scores to support clinical decision-making.

“Nurses are particularly skilled and experienced in detecting when something is wrong with patients under their care. When we can combine that expertise with AI, we can produce real-time, actionable insights that save lives. The CONCERN Early Warning System would not work without the decisions and expert opinions of nurses’ data inputs. By making nurses’ expert instincts visible to the entire care team, this technology ensures faster interventions, better outcomes and, ultimately, more lives saved,” said Sarah Rossetti, lead author of the study, registered nurse and associate professor of biomedical informatics and nursing at Columbia University.

DOI: 10.1038/s41591-025-03609-7

Reference

1. HSE 2024. Irish Maternity Indicator System National Report 2023