Evidence map›Paper›PMID 39116433›Full record

ArticleJMIR pediatrics and parenting2024

Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.

Khulekani Sibanda, Patrick Ndayizigamiye, Hossana Twinomurinzi

Abstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

3 authors.

Khulekani Sibanda *Department of Applied Information Systems, University of Johannesburg, Johannesburg, South Africa.ORCID https://orcid.org/0000-0003-4434-8909
Patrick Ndayizigamiye *Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa.ORCID https://orcid.org/0000-0001-5721-6070
Hossana Twinomurinzi *Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa.ORCID https://orcid.org/0000-0002-9811-3358

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndustry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients.

objectiveThis study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations.

methodsA bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team).

resultsA total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022.

conclusionsThis bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

Indexed as

antenatal careartificial intelligencebibliometricsdata miningdeep learningdigital healthFourth Industrial RevolutionIndustry 4.0Internet of ThingsIoTlean operationsmachine learningmaternalmaternal carematernal health caremobile phonemonitoringpostnatal carepregnancypregnancy monitoringthematic analysiswearable

Identifiers

PMID39116433
PMCPMC11342010

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.