Nursing surveillance saves lives.
That is the key takeaway of a new academic article co-authored by Jenna Thate, Ph.D., RN, CNE, associate professor of nursing. Thate has been involved with a team of researchers in a trial study of an early warning system called Communicating Narrative Concerns Entered by RNs (CONCERN). Results were published in April in Nature Medicine, and confirm through data what most nurses already intuitively know.
“This warning system is unique in that it models nursing concerns reflected in nursing notes to predict patient deterioration,” said Thate, who contributed to “Real-time surveillance system for
patient deterioration: a pragmatic cluster-randomized controlled trial.” She explained that nurse surveillance is a core component of nursing practice aimed at preventing adverse events, and that increased surveillance has been shown to be an early indicator of patient deterioration. In others words – for this and a wide variety of key reasons – nursing presence matters.
“Nurses can recognize subtle, yet observable, clinical changes that may not be captured in
physiological data or well-displayed in EHRs,” said Thate.
She said that the early identification of patients’ risk of deterioration is essential in preventing avoidable yet serious adverse hospital outcomes, including sepsis or even death.
“Failure to detect deterioration and intervene accordingly is strongly linked to information and communication breakdowns among the care team,” said Thate. “We’ve always known that what nurses do is so valuable, and these study results demonstrate that.”
Early warning systems (EWS) are known to positively impact patient outcomes, but until this study, only a few randomized controlled trials demonstrated the impact. Even then, many predictions were focused on one particular event type rather than a broad set of outcomes, such as in-hospital mortality, length of stay and sepsis.
The CONCERN acronym is a nod to the literal concern that nurses show through their regular hands-on care given to patients. If something unusual is noticed during collection of vital signs and other interaction, a nurse will often be the first to notice, and can alert the rest of the care team to increase the frequency and level of surveillance.
The approach for the CONCERN model uses electronic health record (EHR) metadata (for example, date and time stamps, and data type) of nursing surveillance activities. As a result, it identifies all-cause deterioration up to 42 hours earlier than models reliant on physiological indicators.
“Therefore, CONCERN can be used as clinical decision support to make the care team aware of deterioration much earlier so that more timely interventions can be performed,” said Thate.
“Dr. Thate’s dedication to advancing nursing scholarship – and the findings of this particular study – contribute to the ongoing elevation of Siena’s nursing program,” said Anne McCarthy, Ph.D., dean of the School of Science. “It’s an inspiration to our nursing students and colleagues, reflecting the program’s philosophy of caring science rooted in the College’s Franciscan values.”