Telephone Follow-up Based on Artificial Intelligence Technology Among Hypertension Patients: Reliability Study (Preprint)

Author:

Wang Siyuan,Shi Yan,Shen Jing,Chen Chen,Zhang Lin,Zhang Xin,Ren Dongsheng,Wang Yuheng,Yang Qinping,Cheng Minna,Fu Chen,Gao JunlingORCID

Abstract

BACKGROUND

Due to the large population of hypertensives in Shanghai, the limited manpower of community health services, and the uneven level of management services, the follow-up of hypertensives in the community is inefficient and lacks quality, especially the telephone follow-up. Improving the blood pressure control rate and management is challenging.

OBJECTIVE

To evaluate the efficiency and reliability of artificial intelligence (AI) telephone follow-up in the management of hypertension.

METHODS

During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Community Health Service Center of Jingan District in Shanghai were recruited for follow-up, once by AI and once by a human. The second follow-up was conducted within 3~7 days (mean 5.5 days) after the first survey. Cohen's Kappa coefficient was used to evaluate the reliability of the results between the two follow-up visits.

RESULTS

The mean length time of AI calls was shorter (4.15 minutes) than that of manual calls (5.22 minutes). The answers related to the hypertension symptoms showed moderate to substantial consistency (Kappa coefficient: 0.482–0.642), and those related to the complications showed fair consistency (Kappa coefficient: 0.363). In terms of lifestyle, the answer related to smoking showed a very high consistency (Kappa coefficient: 0.918), while those addressing salt consumption, alcohol consumption, and exercise showed moderate to substantial consistency (Kappa coefficient: 0.405–0.640). There was substantial consistency in regular usage of medication (Kappa coefficient: 0.609). The overall satisfaction of AI and manual follow-up was 93.1% and 99.5%, respectively.

CONCLUSIONS

These results indicate that AI telephone follow-up takes less time and is equivalent to manual follow-up to a high degree. Residents have high satisfaction, and AI telephone follow-up is reliable for the follow-up and management of hypertension patients.

Publisher

JMIR Publications Inc.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3