Technology Integration in Nigeria's Healthcare Practice: A Review of Healthcare Workers’ Perspectives
Bibliographic record
Abstract
Healthcare services vary in availability, quality, and access across regions, while healthcare workers are overburdened. Healthcare technology integration improves service and outcomes. This article analyzed Nigeria's healthcare technology integration, including technology kinds, acceptance rates, hurdles to implementation, and policy implications for healthcare delivery. A systematic Google Scholar, PubMed/MEDLINE, BASE, and AJOL search yielded 12 relevant studies for an integrative literature review, which were analyzed and narratively discussed. The technologies used include Telemedicine for remote clinical diagnosis, management, and administration; Electronic Health Records; Mobile Health for patient monitoring and management; Cloud-Based Healthcare Platforms for improved healthcare delivery and data sharing; Patient Remote Monitoring Devices for facilitating healthcare services; and Artificial Intelligence in various applications. Wearable monitoring devices and telemedicine had the highest usage compared to lower e-health technology system uptake. Infrastructural issues like poor connectivity, unstable power supply, and inadequate ICT facilities; costs and lack of government funding; regulatory issues like lack of national policies and unclear guidelines; cultural and social issues like older generations' resistance and privacy concerns; and training and skill gaps slowed technology adoption. Finally, providers liked innovations but worried about the healthcare system's broad acceptance and tele-rehabilitation's efficacy compared to traditional methods. Enhancing the adoption of healthcare technologies in Nigeria requires infrastructure, financial, regulatory, and staff development. This study recommends swiftly investing in ICT infrastructure, training, education, rigorous national guidelines through government funding and public-private cooperation, and strategic implementation using adapted applications such as 'lite' telemedicine systems that use lesser internet bandwidth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".