Application of predictive nursing based on root cause analysis in cesarean section patients
10.3760/cma.j.cn115682-20250110-00161
- VernacularTitle:基于根因分析法的预见性护理在剖宫产产妇中的应用
- Author:
Ran YUAN
1
;
Linlin YAO
;
Yan LIU
;
Ling GAO
;
Lili LE
Author Information
1. 济宁医学院附属医院麻醉科，济宁 272029
- Publication Type:Journal Article
- Keywords:
Cesarean section;
Root cause analysis;
Postoperative pain;
Complications
- From:
Chinese Journal of Modern Nursing
2025;31(31):4306-4309
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the effectiveness of predictive nursing based on root cause analysis in patients undergoing cesarean section.Methods:A convenience sampling method was used to select 180 women who underwent cesarean section under combined spinal-epidural anesthesia in the Affiliated Hospital of Jining Medical University from September 2021 to October 2022. According to the random number table method, they were divided into a control group ( n=90) and an observation group ( n=90). The control group received routine nursing care, while the observation group received predictive nursing based on root cause analysis. Compared the pain intensity at 24 hours after cesarean section and the incidence of postoperative complications between the two groups of parturients. Results:The Visual Analog Scale scores at 24 hours post-cesarean section and the overall incidence of postoperative complications were lower in the observation group than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Predictive nursing based on root cause analysis can effectively relieve postoperative pain and reduce the incidence of complications in patients undergoing cesarean section with combined spinal-epidural anesthesia.
