Evidence map›Paper›PMID 33950900›Full record

ArticleComputers, informatics, nursing : CIN2021

Status and Influential Factors of Intelligent Healthcare in Nursing Homes in China.

Fanli Meng, Fengbin Song, Mengna Guo, Fujie Wang, Xiaoli Feng, Dahui Wang, Liangwen Xu

Abstract read
In one paragraph

Article in Computers, informatics, nursing : CIN, 2021. 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. Article
  3. Article
  4. Article
  5. Article
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

7 authors.

Fanli MengAuthor Affiliations: Medical Department, Hangzhou Normal University (Dr Meng, Ms Song, Ms Guo, Ms Wang, Dr Wang, and Dr Xu), Zhejiang; and China Association of Social Welfare and Senior Service (Dr Feng), Beijing, China.
Fengbin Song
Mengna Guo
Fujie Wang
Xiaoli Feng
Dahui Wang
Liangwen Xu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the support of the Chinese government, nursing homes have increasingly adopted Internet and intelligent information technology to provide daily healthcare services to residents. However, no research has reported the status of intelligent healthcare in nursing homes. From September 2017 to May 2018, we conducted a survey of 197 nursing homes and collected information on their general characteristics, the intelligent healthcare services provided, the effectiveness of the application products used, and the attitudes of the staff and residents toward intelligent healthcare. Overall, 79.69% of the surveyed nursing homes have provided intelligent healthcare services, including medical care services (eg, chronic disease management and intelligent nursing) and daily life services (eg, fall monitoring and wireless positioning). Portable health monitoring devices and data management and service platforms are the most used healthcare products. The attitudes of staff probably affected the development of intelligent healthcare. Financial investment and the attitudes of staff and residents are factors that influence the effectiveness of the application of intelligent healthcare products in nursing homes. With the support of national policies, nursing homes have implemented primary intelligent healthcare. Stakeholders play pivotal roles in the provision of intelligent healthcare services.

Indexed as

Delivery of Health CareNursing HomesChinaHumansSurveys and Questionnaires

Identifiers

PMID33950900
PMCPMC8115741

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.