Evidence map›Paper›PMID 40135704›Full record

ArticlePerspectives in health information managemen2024

Perspectives on Big Data and Big Data Analytics in Healthcare.

Egondu R Onyejekwe, Dasantila Sherifi, Hung Ching

Abstract read
In one paragraph

Article in Perspectives in health information managemen, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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.

Egondu R Onyejekwe
Dasantila Sherifi
Hung Ching

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Big data (BD) is of high interest for research and practice purposes because it has the potential to provide insights into the population served and healthcare practices. Much progress has been made in collecting BD and creating tools for big data analytics (BDA). However, healthcare organizations continue to experience challenges associated with BD characteristics and BDA tools. Utilization of BD impacts current decision-making, planning, and future use of artificial intelligence (AI) tools, which are trained on BD. This qualitative study focused on better understanding the reality of BD and BDA management and usage by healthcare organizations. Six structured interviews were conducted with individuals who work with healthcare BD and BDA. Findings confirmed the known challenges associated with BD/BDA and added rich insights into the structural, operational and utilization aspects, as well as future directions. Such perspectives are valuable for education and improvements in BD/BDA management and development.

Indexed as

Big DataData ScienceDelivery of Health CareArtificial IntelligenceData AnalyticsHumansInterviews as TopicQualitative Researchartificial intelligencebig databig data analyticsdigital datahealth recordspopulation health

Identifiers

PMID40135704
PMCPMC11102055

What Socratic holds

Textmetadata
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 Socratic graph.