Concerns of Pregnant Women in “Prenatal
                    Screening/Diagnosis” Practice and Termination of
                    Pregnancy

Kasnakoğlu, Berna Tarı; Çakar, Mehmet Akif; Ökem, Zeynep Güldem; Fadıloğlu, Erdem; Örgül, Gökçen; Beksaç, Mehmet Sinan

doi:10.1055/a-1076-1039

Cited by 2 publications

(1 citation statement)

References 26 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recording individual medical information in the computer reduces the incidence of medical errors, thus improving the overall level of medical treatment and providing better medical services for patients [ 14 , 15 ]. The method of DS data extraction is parallelism between system instances and serial extraction of tables between system instances [ 16 ]. Compared with other data processing systems, DS has the characteristics of high speed and efficiency in processing big data [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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 ( P < 0.05 ). The scores of anxiety and depression in the group were lower, with statistical significance ( P < 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 ( P < 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 ( P > 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 ( P < 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.

show abstract