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探讨数据服务在智慧医疗高危孕产妇及婴幼儿数据健康管理中的应用。

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care.

机构信息

Maternity Group Healthcare Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China.

Healthcare Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China.

出版信息

Scanning. 2022 Aug 25;2022:5957697. doi: 10.1155/2022/5957697. eCollection 2022.

DOI:10.1155/2022/5957697
PMID:36082174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9436624/
Abstract

OBJECTIVE

In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city.

METHODS

A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared.

RESULTS

After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant ( < 0.05). The scores of anxiety and depression in the group were lower, with statistical significance ( < 0.05); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant ( < 0.05); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant ( > 0.05). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant ( < 0.05).

CONCLUSION

The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women's self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.

摘要

目的

为了提高医院精细化管理水平,促进医疗机构智慧医院的科学发展,解决国家、省、市逐年增加的数据填报和上报问题。

方法

选取我院 2020 年 1 月至 2021 年 10 月收治的 84 例高危孕妇,对高危孕妇进行筛查,将高危孕妇分为常规干预组和 DS 医疗组,每组 42 例。常规干预组高危孕妇给予常规干预,DS 医疗组在常规干预的基础上应用数据服务进行智慧医疗服务。比较两组自我护理、焦虑、抑郁评分,分析应对方式,记录高危产妇的满意度和不良情况发生率,并比较两组的分娩方式。

结果

干预后,DS 医疗组的日常生活活动、随访、胎儿监护、自我保护行为均高于常规干预组,差异有统计学意义( < 0.05)。组内焦虑、抑郁评分较低,差异有统计学意义( < 0.05);干预后,DS 医疗组消极应对方式评分低于常规干预组,而积极应对方式评分高于常规干预组;DS 医疗组高危孕妇满意度明显高于常规干预组,差异有统计学意义( < 0.05);DS 医疗组高危孕妇不良母婴结局总发生率低于常规干预组,但差异无统计学意义( > 0.05)。与常规组相比,DS 医疗组阴道分娩率较高,剖宫产率较低,差异有统计学意义( < 0.05)。

结论

数据服务在智慧医疗高危产妇相关数据管理平台中的应用,促进了高危孕妇自我护理行为的提升,改善了负面情绪,使其能够以积极的行为配合分娩,降低剖宫产率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/26a9e20f13ee/SCANNING2022-5957697.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/3b28042fee1d/SCANNING2022-5957697.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/5c2b8c545c0c/SCANNING2022-5957697.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/26a9e20f13ee/SCANNING2022-5957697.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/3b28042fee1d/SCANNING2022-5957697.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/5c2b8c545c0c/SCANNING2022-5957697.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9963/9436624/26a9e20f13ee/SCANNING2022-5957697.003.jpg

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