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Impact of digital health technologies adoption on healthcare workers’ performance and workload: perspective with DOI and TOE models
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BACKGROUND: Digital health technologies (DHT) have become an integral component of modern healthcare systems, offering the potential to improve healthcare delivery and outcomes. Guided by the Technology-Organization-Environment (TOE) framework, this study examines the impact of DHT adoption on healthcare workers' performance and workload in public and private hospitals in Mogadishu, Somalia. METHOD: A stratified random sampling approach was used to collect data from 286 healthcare workers in public and private hospitals. Data were analyzed using Structural Equation Modeling (SEM) to test hypotheses and assess direct and mediating relationships among variables. RESULTS: The analysis revealed that DHT significantly improves healthcare workers' performance and workload respectively (β = 0.452, C.R. = 10.150, p < 0.001) and (β = 0.594, C.R. = 9.972, p < 0.001), organizational and environmental factors are positively impacting on healthcare workers' performance (β = 0.327, C.R. = 8.709, p < 0.001) and (β = 0.102, C.R. = 2.872, p = 0.004). Healthcare workers' performance significantly reduces workload (β = 0.594, C.R. = 9.972, p < 0.001) and the mediating role of healthcare workers' performance between DHT and workload were also confirmed (β = 0.281, C.R. = 5.9787, p < 0.001). CONCLUSION: The findings underscore the critical role of DHT in enhancing healthcare workers' performance and reducing workload, with the TOE framework and performance acting as significant mediators. These results provide actionable insights for healthcare administrators and policymakers to optimize DHT implementation, support workforce efficiency, and address workload challenges through strategic organizational and environmental adaptations.
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“The framework emphasizes identifying facilitators and barriers to the adoption of a specific technology, rather than providing guidance on governing the broader range of technologies implemented within organizations23–26.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [25]
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Metadata
- Authors
- Jeilani, A., & Hussein, A.
- Affiliations
- Mogadishu University (SO)
- Benadir University (SO)
- Corresponding author
- Jeilani, Abdulkadir
- ORCID
- Jeilani, Abdulkadir
- Venue
- BMC Health Services Research
- Published
- 2025-02-18
- Volume
- 25
- Issue
- 1
- Pages
- 271-271
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC11837663
- OpenAlex
- W4407701754
- Semantic Scholar
- 1bb1af77af8460f281c969d3010450fd75380622
- Cited by
- 105
- References
- 82
- Citations by year
- 2026: 73 · 2025: 32
- FWCI
- 63.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Psychology › Social Psychology › Technostress in Professional Settings
- Keywords
- digital health technologies, workload, technology-organization-environment framework, Doi model, technology adoption, public and private hospitals, mediating role of performance, Mogadishu
- MeSH terms
- Digital Technology, Digital Health, Adult, Female, Health Personnel, Hospitals, Public, Humans, Male, Middle Aged, Workload
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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