Evidence map›Paper›PMID 38778548›Full record

ArticleJournal of clinical hypertension (Greenwich, Conn.)2024

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

Siyuan Wang, Yan Shi, Mengyun Sui, Jing Shen, Chen Chen, Lin Zhang, Xin Zhang, Dongsheng Ren, Yuheng Wang, Qinping Yang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical hypertension (Greenwich, Conn.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Siyuan WangDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.ORCID 0009-0006-1826-9990
Yan ShiDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Mengyun SuiDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Jing ShenProduct Department, Yicheng Information Technology Limited Corporation, Shanghai, China.
Chen ChenHealth Management Department, Pengpu Community Health Service Center, Shanghai, China.
Lin ZhangHealth Management Department, Pengpu Community Health Service Center, Shanghai, China.
Xin ZhangDepartment of Chronic Non-communicable Diseases Surveillance and Management, Jingan District Center for Disease Control and Prevention, Shanghai, China.
Dongsheng RenDepartment of Chronic Non-communicable Diseases Surveillance and Management, Jingan District Center for Disease Control and Prevention, Shanghai, China.
Yuheng WangDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Qinping YangDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Junling GaoDepartment of Prevention Medicine and Health Education, School of Public Health, Fudan University, Shanghai, China.
Minna ChengDivision of Chronic Non-communicable Disease and Injury, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.

Funding

Shanghai Municipal Health Commission 20214Y0488Shanghai Municipal Health Commission 20234Y0304Shanghai Municipal Health Commission GWVI-11.1-22Shanghai Municipal Health Commission GWVI-8
6 · The paper itself

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).

Indexed as

Artificial IntelligenceHypertensionTelephoneAdultAgedChinaFemaleFollow-Up StudiesHumansMaleMiddle AgedReproducibility of Resultsartificial Intelligencehypertensionreliabilityspeech recognitiontelephone follow‐up

Identifiers

PMID38778548
PMCPMC11180679

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.