Electronic Health Records can tell a clinician many things, including whether a patient is likely to develop a pressure injury. That phenomenon has now been reported in the literature.
A study published in the American Journal of Critical Care that examined data in electronic health records (EHRs) related to nursing skin assessments found that irritated skin is a strong predictor of pressure injury development in surgical critical care patients.
“This study demonstrates the power of using informatics to analyze the vast amounts of data in electronic health records.
“Critical care nurses routinely conduct head-to-toe skin assessments every 12 hours and document changes in condition in the EHR. Modern informatics approaches enable us to more easily examine large-scale, real-world data available in the health record to gain insights and improve outcomes.”
— study co-author Jenny Alderden, PhD, APRN, CCRN, CCNS, an assistant professor at the University of Utah College of Nursing, Salt Lake City; a critical care nurse specialist with expertise in pressure injury risk assessment and prevention; and a member of the University of Utah pressure injury prevention committee
The study’s analysis of more than 5,000 patient records underscores the importance of treating and monitoring irritated skin early and eliminating the cause as an important step to prevent pressure injuries, according to a media release from the American Association of Critical-Care Nurses.
5 Years of Data Scrutinized
The retrospective cohort study included data from surgical critical care patients admitted to the surgical intensive care unit (ICU) or cardiovascular surgical ICU at the University of Utah Hospital, Salt Lake City, from 2014 through 2018. The research team analyzed 5 years of data from 5,101 patients using a modern statistical test called Least Absolute Shrinkage and Selection Operator (LASSO).
Of the 399 patients in the sample who had a HAPI develop, 110 patients had a stage 1 pressure injury develop, with 40% (44) worsening to a more severe stage during their ICU stay.
The most common pressure injury location was the coccyx, followed by the buttocks, sacrum and an extremity excluding the heel.
The research builds on the team’s ongoing work to develop and test its machine learning model to predict pressure injury development in critical care patients. The long-term goal is to develop a predictive model that will be deployed in the EHR to provide real-time decision support, enabling nurses to identify patients at highest risk for pressure injury and to apply targeted interventions, the release explains.
[Source(s): American Association of Critical-Care Nurses, Newswise]
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