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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Implementing Delirium Screening in an Intermediate Care Unit
Christyn A Gaa1, Bimbola F Akintade2
1University of Maryland School of Nursing, Baltimore, MD christyn.gaa@umaryland.edu.
Background:
Delirium occurs in as many as 82% of hospitalized patients. Use of a valid and reliable tool allows for early detection and management to mitigate adverse effects, including a decrease in patient falls.
Objective:
To conduct a quality improvement project to implement the confusion assessment method (CAM) tool in an intermediate care unit and measure delirium screening compliance, feasibility of the tool, and the effect on reported patient falls.
Methods:
Web-based training using a 14-item pre-post assessment for knowledge comprehension. The CAM tool was added to the electronic health record (EHR), and documentation compliance was measured for eight weeks. Afterwards, a nurse perception survey was distributed, and 60-day pre- and post-intervention patient falls were compared.
Results:
Forty-seven nurses completed the training. Post-test averages were higher than the pre-test (p = .16); five answers showed significant improvement (p < .02). Screening and documentation compliance were 79.1%. Twenty-one nurses completed the perception survey, demonstrating agreement that delirium CAM screening is a feasible intervention. Patient falls were reduced by 57%.
Conclusion:
Addition of the CAM tool into the EHR-enhanced screening compliance.
Implications For Nursing:
Early delirium detection may reduce patient falls. The CAM is a feasible instrument and delirium screening is a worthwhile intervention.
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