Evidence map›Paper›PMID 39397914›Full record

ArticleHeliyon2024

Readiness of big health data analytics by technology-organization-environment (TOE) framework in Ethiopian health sectors.

Bayou Tilahun Assaye, Bekalu Endalew, Maru Meseret Tadele, Gizaw Hailiye Teferie, Abraham Teym, Yidersal Hune Melese, Andualem Fentahun Senishaw, Sisay Maru Wubante, Habtamu Setegn Ngusie, Aysheshim Belaineh Haimanot

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 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. 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

10 authors.

Bayou Tilahun AssayeDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Bekalu EndalewDepartment of Public Health, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Maru Meseret TadeleDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Gizaw Hailiye TeferieDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Abraham TeymDepartment of Environmental Health, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Yidersal Hune MeleseDepartment of Human Nutrition, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Andualem Fentahun SenishawDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Sisay Maru WubanteDepartment of Health Informatics, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Habtamu Setegn NgusieDepartment of Health Informatics, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
Aysheshim Belaineh HaimanotDepartment of Public Health, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Big health data is a large and complex dataset that the health sector has collected and stored continuously to generate healthcare evidence for intervening the future healthcare uncertainty. However, data use for decision-making practices has been significantly low in developing countries, especially in Ethiopia. Hence, it is critical to ascertain which elements influence the health sector's decision to adopt big health data analytics in health sectors. The aim of this study was to identify the level of readiness for big health data analytics and its associated factors in healthcare sectors. Methods: A cross-sectional study design was conducted among 845 target employees using the structural equation modeling approach by using technological, organizational, and environmental (TOE) frameworks. The target population of the study was health sector managers, directors, team leaders, healthcare planning officers, ICT/IT managers, and health professionals. For data analysis, exploratory factor analysis using SPSS 20.0 and structural equation modeling using AMOS software were used. Result: 58.85 % of the study participants had big health data analytics readiness. Complexity (CX), Top management support (TMS), training (TR) and government law policies and legislation (GLAL) and government IT policies (GITP) had positive direct effect, compatibility (CT), and optimism (OP) had negative direct effect on BD readiness (BDR). Conclusion: The technological, organizational, and environmental factors significantly contributed to big health data readiness in the healthcare sector. The Complexity, compatibility, optimism, Top management support, training (TR) and government law and IT policies (GITP) had effect on big health data analytics readiness. Formulating efficient reform in healthcare sectors, especially for evidence-based decision-making and jointly working with stakeholders will be more relevant for effective implementation of big health data analytics in healthcare sectors.

Indexed as

Big health dataData analyticsData managementHealth information revolutionHealth sectorsReadiness

Identifiers

PMID39397914
PMCPMC11470786

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.