Telephone follow-up based on artificial intelligence technology among hypertension patients: Reliability study.

Journal: Journal of clinical hypertension (Greenwich, Conn.)
Published Date: May 22, 2024

Abstract

Artificial intelligence (AI) telephone is reliable for the follow-up and management of hypertensives. It takes less time and is equivalent to manual follow-up to a high degree. We conducted a reliability study to evaluate the efficiency of AI telephone follow-up in the management of hypertension. During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Community Health Service Center 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). The mean length time of two calls were compared by paired t-test, and Cohen's Kappa coefficient was used to evaluate the reliability of the results between the two follow-up visits. The mean length time of AI calls was shorter (4.15 min) than that of manual calls (5.24 min, P < .001). The answers related to the symptoms showed moderate to substantial consistency (κ:.465-.624, P < .001), and those related to the complications showed fair consistency (κ:.349, P < .001). In terms of lifestyle, the answer related to smoking showed a very high consistency (κ:.915, P < .001), while those addressing salt consumption, alcohol consumption, and exercise showed moderate to substantial consistency (κ:.402-.645, P < .001). There was moderate consistency in regular usage of medication (κ:.484, P < .001).

Authors

  • Siyuan Wang
    Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing, 100083, People's Republic of China.
  • Yan Shi
    Department of Burn, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Mengyun Sui
    Division of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Jing Shen
    Department of Physical Education, China University of Geosciences, Beijing, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Dongsheng Ren
    Department of Chronic Non-communicable Diseases Surveillance and Management, Jingan District Center for Disease Control and Prevention, Shanghai, China.
  • Yuheng Wang
  • Qinping Yang
    Division of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
  • Junling Gao
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Minna Cheng
    Division of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.

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