Clin Surg | Volume 7, Issue 1 | Research Article | Open Access

Using Automated National Early Warning Score (NEWS) 2 in Early Detection of in-Hospital Patient Deterioration

Anhar Alhussaini1,2*, Abdulaziz Altoub1,3, Dane Henry1,4, Carolyne Mukuha1,5, Doris Nessim1,6,7, Morkel Jacques Otto1,8 and Mary-Lynn Watson1,9

1Safety, Quality, Informatics and Leadership (SQIL) Certificate Program (2020-2021), Harvard Medical School Postgraduate Medical Education, USA
2Master of Education in the Health Professions, School of Education, Johns Hopkins University, USA
3Department of Emergency Medicine, King Saud University, Saudi Arabia
4Lake Regional Health System, Osage Beach, Missouri, USA
5CRISSP program Quality Improvement and Learning, University of Nairobi, Kenya
6Healthcare and Pharmacy Leader, USA
7Department of Family Medicine, McMaster University, Canada
8IntraVita International, Colchester, UK
9Department of Emergency Medicine, Dalhousie University, Canada

*Correspondance to: Anhar Alhussaini 

Fulltext PDF

Abstract

National Early Warning Score (NEWS) 2 was developed by the Royal College of Physicians to be used as early detection of in-hospital patient deterioration. The score has been shown to improve prognosis and triage efficiency. While the benefit of using a triage system has been established, in the real world, inefficient manual data entry and human error contribute to possible delays in identifying patients requiring intensive care transfers. Our team wanted to create an automated predictive model using informatics to improve the quality of patient care and healthcare provider workflow efficiency to identify high-risk patients who would benefit from early interventions by rapid-response teams.

Keywords:

National early warning score; NEWS 2; RCP; Deterioration; EHR

Citation:

Alhussaini A, Altoub A, Henry D, Mukuha C, Nessim D, Otto MJ, et al. Using Automated National Early Warning Score (NEWS) 2 in Early Detection of in-Hospital Patient Deterioration. Clin Surg. 2022; 7: 3408.

Subscribe to Our Newsletter