摘要
目的对大数据平台的访问控制机制进行研究,并搭载人工智能ChatGPT协同平台进行护理管理,旨在试行川东北地区瓣膜病患者自我监测随访大数据平台,初步测验其运用效果,为护理工作者提供更实用的随访工具。方法以回顾性研究,采用便利抽样法选取2022年1—10月在川北医学院附属医院行机械瓣膜置换术或术后到门诊复查,服用口服华法林抗凝治疗并愿意使用本平台的患者32例,依据其2022年11—12月使用平台的数据,将32例患者按国际标准化比值(INR)达标率的高低分为高达标率组和低达标率组,各16例,根据其使用平台的次数与INR值达标率,评估平台的运行效果和平台对患者抗凝效果的影响。结果高达标率组患者登录平台次数及书写INR值次数分别为(11.31±3.38)、(7.00±1.63)次,低达标率组分别为(9.44±3.39)、(6.06±1.88)次,2组比较差异均无统计学意义(均P>0.05);高达标率组患者书写INR值在正常值范围内及出现预警次数分别为(6.38±1.50)次及1.00(0,2.00)次,低达标率组分别为(4.05±1.57)及2.00(2.00,3.50)次,2组比较差异均有统计学意义(t=4.26,Z=-2.22,均P<0.05)。结论搭载ChatGPT的人工机械瓣膜置换术后患者自我监测随访大数据平台可以优化并规范护理随访工作流程,提高护理工作效率,同时为人工机械瓣膜置换术后患者提供更好的自我管理途径,协助患者监测INR值和预测可能的病情变化,提供相应预警和建议,帮助其更好地参与自我抗凝管理,提高抗凝疗效。
Objective This paper examines the access control mechanisms of a big data platform and explores its integration with the ChatGPT artificial intelligence platform for nursing management.The aim was to pilot a self-monitoring and follow-up big data platform for valve disease patients in the Northeastern region of China and assess its effectiveness,providing healthcare professionals with a more practical follow-up tool.Methods Convenience sampling was used to select 32 patients who underwent mechanical valve replacement surgery or postoperative follow-up at the affiliated hospital of North Sichuan Medical College between January and October 2022 by a retrospective study,were taking oral warfarin anticoagulant therapy,and were willing to use the platform.Based on their platform usage data from November to December 2022,the 32 patients were divided into two groups according to their INR compliance rates:a high compliance group(16 patients)and a low compliance group(16 patients).Evaluate the operational effectiveness of the platform and its impact on patient anticoagulation efficacy based on its usage frequency and INR value compliance rate.Results The number of login times and INR values written by patients in the high-standard-rate group were(11.31±3.38)and(7.00±1.63)times respectively,which were higher than those in the low-standard-rate group(9.44±3.39)and(6.06±1.88)times,the difference were not statistically significant(all P>0.05).The number of INR values written within the normal range and the number of occurrences of warning values by patients in the high-standard-rate group were(6.38±1.50)and 1.00(0,2.00)times,which were different than that in the low-standard-rate group(4.05±1.57)and 2.00(2.00,3.50)times,the differences were statistically significant(t=4.26,Z=-2.22,P<0.05).Conclusions The self-monitoring and follow-up big data platform for patients after artificial mechanical valve replacement equipped with ChatGPT can optimize and standardize the nursing follow-up workflow,improve nursing work efficiency,reduce the workload of medical staff.At the same time,it provides a better self-management platform for patients after artificial mechanical valve replacement.Assist patients in monitoring INR values and predicting possible changes in their condition,providing corresponding warnings and recommendations helps patients better participate in self-anticoagulation management,and improves the quality of life of patients.
作者
夏浩然
陈小艳
赵慧明
粟丽
陈婷
吴天文
冷兴悦
王亚莉
Xia Haoran;Chen Xiaoyan;Zhao Huiming;Su Li;Chen Ting;Wu Tianwen;Leng Xingyue;Wang Yali(School of Nursing,Chuanbei Medical College,Nanchong 637000,China;Department of Cardiovascular Surgery,Affiliated Hospital of North Sichuan Medical College,Nanchong 637000,China)
出处
《中国实用护理杂志》
2023年第29期2276-2284,共9页
Chinese Journal of Practical Nursing
基金
四川省基层卫生事业发展中心课题(HCPB202301W08)
南充市科技局市校合作项目(19SXHZ0372)。
关键词
ChatGPT
大数据
机械瓣膜置换术
抗凝治疗
社区护理
ChatGPT
Big Data
Heart Mechanical Valve Replacement
Anticoagulation therapy
Community Nursing
