摘要
目的评价区域综合管理模式对提高癫痫患者生活质量的作用。方法从四川省自贡市癫痫患者管理互联网平台中随机选择成人癫痫患者200例作为病例组,随机选择医院门诊常规就诊的成人癫痫患者200例作为对照组。病例组给予区域综合管理,对照组为常规门诊诊治,比较两组患者2年后癫痫各项指标及生活质量的差异。结果随访2年后,病例组癫痫生活质量量表(QOLIE-31)总分高于对照组,合并焦虑的比例低于对照组;病例组随访后,QOLIE-31总分及子项目中对发作的担忧、生活满意度、情绪、精力/疲劳的评分均显著高于入组时,差异有统计学意义(P<0.01)。结论本研究所探索的基于互联网双向转诊、上下级医院协同随访及健康宣教的区域综合管理模式可显著提高癫痫患者的生活质量,尤其在对发作的担忧、生活满意度、情绪、精力/疲劳这几个方面改善明显。
Objective To explore the effect of regional comprehensive management model on improving the quality of life of patients with epilepsy.Methods A total of 200 epileptic patients in Zigong epilepsy system were randomly selected as the case group,and the epileptic patients receiving routine outpatient treatment were selected as the control group.The case group was given regional comprehensive management and the control group received routine outpatient treatment.The differences of epilepsy indicators and quality of life between the two groups were compared after 2 years.Results After two years of follow-up,the total score of QOLIE-31 in the case group was higher than that in the control group,and the proportion of anxiety was lower than that in the control group.After follow-up,the total score of QOLIE-31 scale and the scores of anxiety about seizures,life satisfaction,mood and energy/fatigue in sub items in case group were significantly higher than baseline in group,with statistical significance(P<0.01).Conclusion The regional comprehensive management model explored in this study can significantly improve the quality of life of patients with epilepsy,in particular,there were significant improvements in anxiety about seizures,life satisfaction,mood and energy/fatigue.
作者
郭晓聪
徐晓娅
王明金
余雪基
蒲洋洋
GUO Xiaocong;XU Xiaoya;WANG Mingjin;YU Xueji;PU Yangyang(Zigong First People's Hospital,Zigong 643000,Sichuan,China)
出处
《中国卫生信息管理杂志》
2023年第4期666-670,共5页
Chinese Journal of Health Informatics and Management
基金
四川省自贡市重点科技计划项目“综合管理对提高癫痫患者生活质量的研究”(项目编号:2019YLSF26)
四川省教育厅四川医院管理和发展研究中心项目“区域医疗协同下的癫痫病分级诊疗与患者长程管理模式研究”(项目编号:SCYG2019-29)。
关键词
癫痫
生活质量
互联网双向转诊
epilepsy
quality of life
Internet-based referral