Evidence map›Paper›PMID 40373078›Full record

ArticlePloS one2025

Health care professionals intention to use digital health data hub working in East Gojjam Hospitals, Northwest Ethiopia: Technology acceptance modeling.

Ayenew Sisay Gebeyew, Sefefe Birhanu Tizie, Bayou Tilahun Assaye, Afework Edmealem, Temesgen Feyu, Habtamu Mekonen, Tirsit Ketsela Zeleke, Melese Getachew, Andualem Fentahun

Abstract read
In one paragraph

Article in PloS one, 2025. 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. Article
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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

9 authors.

Ayenew Sisay GebeyewDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.ORCID https://orcid.org/0000-0002-2099-7106
Sefefe Birhanu TizieDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Bayou Tilahun AssayeDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Afework EdmealemDepartment of Nursing, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Temesgen FeyuDepartment of Health Informatics, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Habtamu MekonenDepartment of Nutrition, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Tirsit Ketsela ZelekeDepartment of Pharmacy, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.ORCID https://orcid.org/0000-0003-3528-0703
Melese GetachewDepartment of Pharmacy, College of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Andualem FentahunDepartment of Health Informatics, 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

backgroundDigital health data hubs contribute significantly to finding the right solutions to health problems, which forms the basis for achieving sustainable development goals. However, in Ethiopia, the health system has been coming to one central hub for all data, there is limited evidence of health professionals' intentions to use these systems. Understanding their intentions is crucial, as this can significantly improve the advancement of digital health in healthcare organizations. This study assessed health professionals' intention to use digital health data hubs in hospitals in East Gojjam, northwest Ethiopia, in 2024.

methodsA cross-sectional study design was used to conduct the study. Eleven hospitals were included in the study area. Using an a priori structural equation modeling sample size calculator, the total sample size was 616. Stratified proportional allocation sampling was performed. The study participants were selected using a systematic sample. Structural equation modeling (SEM) was used for the analysis. Because it is a more powerful multivariate technique for testing and evaluating multivariate causal relationships. The assumptions of SEM-like normality, average variance extracted (AVE), composite reliability (CR), Cronbach's alpha, confirmatory factor analysis (CFA), and model specifications were checked using Amos and Stata version 16.

resultsThis study was conducted with a sample size of 616 healthcare professionals; 591 (95.94%) responded to the survey. The results showed that 57.69% (n = 341) of the healthcare professionals intended to use the digital health data hub. Further analysis showed that perceived usefulness (PU: β = 0.576, p = 0.000), perceived trust (PT: β = 0.116, p = 0.022), and attitude (β = 0.143, p = 0.043) significantly and positively influenced health professionals' intention to use digital health data hubs.

conclusionOverall, the findings showed that 42.31% of health professionals have low intention to use digital health data hubs. These shall be needed to improve their intentions to use digital health data hubs through targeted interventions. Therefore, focusing on critical factors, such as perceived usefulness, trust, and attitude are crucial factors to reinforce their intention to use the system. Additionally, overcoming implementation challenges and building trust is critical to the successful integration and use of digital health data hubs.

Indexed as

Attitude of Health PersonnelHealth PersonnelAdultCross-Sectional StudiesDigital HealthEthiopiaFemaleHospitalsHumansIntentionMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40373078
PMCPMC12080794

What Socratic holds

Textmetadata
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 Socratic graph.