A Study to Evaluate the Effectiveness of Artificial Intelligence-Assisted Early Warning Systems on Nurses' Recognition and Management of Clinical Deterioration Among Patients in Home Healthcare Settings in Doha, Qatar
Keywords:
Clinical deterioration, early warning systems, artificial intelligence, home healthcare, nursing, patient safetyAbstract
When a patient's condition begins to slide toward a crisis, the hours before the emergency areusually not silent; the body sends warning signals in the form of subtle changes in vital signs,and catching those signals early can be the difference between a manageable intervention anda hospital admission or worse. In a hospital this vigilance is shared among many staff, but in home healthcare a single nurse, often working alone in a patient's house, must notice and acton these signs unaided, which makes early recognition
References
1: A. Mahendra Vardhan and S. Sridhar, "Determining False Positive Analysis of Software Vulnerabilities with Predefined Scan Rules using Random Forest Classifier and Decision Tree Technique," 2022 4th International Conference on Advances in Computing, Communication Control
and Networking (ICAC3N), Greater Noida, India, 2022, pp. 622-625, DOI: https://doi.org/10.1109/ICAC3N56670.2022.10074458
