Effect of intelligent early warning scoring system in condition monitoring of hospitalized non-critically ill patients
10.3760/cma.j.issn.1671-0282.2022.09.015
- VernacularTitle:智能早期预警评分系统对非重症患者预后评估的价值研究
- Author:
Bingying HUANG
1
;
Jiaying TANG
;
Jianping SONG
;
Yuping ZHANG
;
Fei ZHAO
;
Ying WANG
;
Lingling HONG
;
Meijuan LAN
Author Information
1. 浙江大学医学院附属第二医院护理部，杭州 310000
- Keywords:
Intelligent early warning scoring system;
Artificial intelligent;
Rapid response team;
Nursing
- From:
Chinese Journal of Emergency Medicine
2022;31(9):1243-1248
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish and apply the electronic further modified early warning score system (e-fMEWS), and explore its role in the condition evaluation and early warning of inpatients in non-critical units, so as to provide clinical nurses with an early and dynamic method to identify the potential deterioration risk of patients' condition.Methods:A retrospective analysis of 262 805 inpatients in multiple non-critical units of the Second Affiliated Hospital of Zhejiang University School of Medicine from January to December 2018 and January to December 2020 was performed. The patients who were hospitalized from January to December 2018 were used as the control group, and the responsible nurse used the traditional single evaluation index to start the emergency response system; the patients from January to December 2020 were used as the research group, and the emergency response system was started using e-fMEWS. The inclusion criteria were as follows: (1) hospitalization time ≥24 h; (2) patient ≥14 years old. Exclusion criteria were as follows: (1) patients had cardiopulmonary resuscitation before admission; (2) patients discontinued treatment or were transferred to another hospital during treatment; (3) patients received palliative care; (4) patients were admitted to non-critical wards in grade I of emergency pre-examination and triage. The activation of the rapid response team (RRT), the activation of the cardiorespiratory arrest team, the incidence of cardiac and respiratory arrest, the number of cases of invasive mechanical ventilation, the number of cases admitted to the intensive care unit, the length of hospital stay and the prognosis were compared. Statistical software SPSS 22.0 was used for data analysis.Results:Under the e-fMEWS assessment, compared with the control group, the rate of initiation of the research group decreased by 0.03%. For patients who initiated RRT, the average length of hospital stay was shortened, and the number of in-hospital respiratory cardiac arrest decreased (12.2% vs. 13.2%) and the number of cases transferred to the intensive care unit was less (42.8% vs. 50.6%), the rate of improvement and recovary increased (58.4% vs. 56.1%).Conclusions:The application of e-fMEWS can help clinical nurses to quickly and accurately identify the potential risk of deterioration of the patient's condition. Through early identification of potentially critically ill patients in non-critical units, early intervention and timely treatment can avoid adverse events and improve the patient prognosis.
