Construction and practice of big data platform for self-monitoring and follow-up of patients after artificial mechanical valve replacement with chatGPT
10.3760/cma.j.cn211501-20230328-00746
- VernacularTitle: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的建设与实践
- Author:
Haoran XIA
1
;
Xiaoyan CHEN
;
Huiming ZHAO
;
Li SU
;
Ting CHEN
;
Tianwen WU
;
Xingyue LENG
;
Yali WANG
Author Information
1. 川北医学院护理学院，南充 637000
- Keywords:
ChatGPT;
Big Data;
Heart Mechanical Valve Replacement;
Anticoagulation therapy;
Community Nursing
- From:
Chinese Journal of Practical Nursing
2023;39(29):2276-2284
- CountryChina
- Language:Chinese
-
Abstract:
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.
