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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.
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@#Objective To explore the efficacy of artificial intelligence (AI) detection on pulmonary nodule compared with multidisciplinary team (MDT) in regional medical center. Methods We retrospectively analyzed the clinical data of 102 patients with lung nodules in the Xiamen Fifth Hospital from April to December 2020. There were 57 males and 45 females at age of 36-90 (48.8±11.6) years. The preoperative chest CT was imported into AI system to record the detected lung nodules. The detection rate of pulmonary nodules by AI system was calculated, and the sensitivity, specificity of AI in the different diagnosis of benign and malignant pulmonary was calculated and compared with manual film reading by MDT. Results A total of 322 nodules were detected by AI software system, and 305 nodules were manually detected by physicians (P<0.05). Among them, 113 pulmonary nodules were diagnosed by pathologist. Thirty-eight of 40 lung cancer nodules were AI high-risk nodules, the sensitivity was 95.0%, and 25 of 73 benign nodules were AI high-risk nodules, the specificity was 65.8%. Lung cancer nodules were correctly diagnosed by MDT, but benign nodules were still considered as lung cancer at the first diagnosis in 10 patients. Conclusion AI assisted diagnosis system has strong performance in the detection of pulmonary nodules, but it can not content itself with clinical needs in the differentiation of benign and malignant pulmonary nodules. The artificial intelligence system can be used as an auxiliary tool for MDT to detect pulmonary nodules in regional medical center.