| 朱烨,朱立颖,杜瑞,王育璠,陈兰.人工智能语音随访系统在2型糖尿病患者中的应用[J].上海护理,2023,23(7): |
| 人工智能语音随访系统在2型糖尿病患者中的应用 |
| Study on the application of artificial intelligence voice follow-up system in type 2 diabetes patients |
| 投稿时间：2023-01-17 修订日期：2023-07-10 |
| DOI： |
| 中文关键词: 人工智能语音随访系统 2型糖尿病 随访 代谢指标 血糖管理 |
| 英文关键词:Artificial intelligence voice follow-up system type 2 diabetes mellitus follow-up metabolic index glucose management |
| 基金项目:护理高原学科建设百人计划(Hlgy1903kygg) |
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| 中文摘要: |
| 目的 探讨人工智能语音随访系统在2型糖尿病患者中的应用效果。方法 采用历史对照的方法。选取2019年6月~2019年11月在上海市第一人民医院国家标准化代谢性疾病管理中心入组的566例2型糖尿病患者做为对照组,2020年7月~2020年10月(入组实施人工智能语音随访系统后的352例2型糖尿病患者为干预组,比较两组患者的电话成功接通率、电话随访耗费时间、6个月时的门诊随访率,以及6个月时两组患者各项代谢指标。结果 干预6个月后,干预组和对照组患者电话接通率分别为84.94%、80.92%,差异无统计学意义(P＞0.05)；干预组患者电话随访时长(49.31±13.62)秒短于对照组(71.87±17.22)秒,差异有统计学意义(P＜0.05)；与对照组相比,人工智能随访系统干预后,患者空腹血糖和血压差异有统计学意义(P＜0.05)；干预组患者半年随访率(67.0%)高于对照组(56.9%),差异有统计学意义(P＜0.05)。结论 人工智能语音随访系统可节约人力成本,改善2型糖尿病患者空腹血糖和TG,提高其随访率,为糖尿病患者的院外随访提供高效智慧随访新模式。 |
| 英文摘要: |
| Objective To investigate the effectiveness of an artificial intelligence voice follow-up system in patients with type 2 diabetes. Methods A historical control method was used. 566 patients with type 2 diabetes who were enrolled in the National Standardized Metabolic Disease Management Center of Shanghai First People"s Hospital from June 2019 to November 2019 were selected as the control group, and from July 2020 to October 2020. The 352 patients with type 2 diabetes who were enrolled after the implementation of the AI voice follow-up system were the intervention group, comparing the successful telephone connection rate, the time spent on telephone follow-up, the outpatient follow-up rate at 6 months, and the metabolic indexes of the two groups at 6 months Results After 6 months of intervention, the telephone connection rates of patients in the intervention group and control group were 84.94% and 80.92%, respectively, with no statistically significant difference (P> 0.05); the telephone follow-up time of patients in the intervention group was shorter (49.31 ± 13.62) than that in the control group (71.87 ± 17.22), with statistically significant difference (P< 0.05); compared with the control group, the intervention of the artificial intelligence follow-up system had significant difference on patients with fasting blood glucose and blood pressure (P<0.05); the six-month follow-up rate of patients in the intervention group (67.0%) was higher than that in the control group (56.9%), and the difference was statistically significant (P< 0.05). Conclusion The artificial intelligence voice follow-up system can shorten the call length to save labor cost, improve fasting glucose and TG of type 2 diabetes patients, and increase their follow-up rate, which can provide a reference for out-of-hospital follow-up of chronic disease patients. |
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