Use of Technology in Patient Care in Nigeria: A Systematic Review of Original Studies from 2020 to 2025
Bibliographic record
Abstract
The integration of digital technology in healthcare has become increasingly prominent in Nigeria, offering new possibilities for improving the delivery of patient care. With rising adoption of tools such as telemedicine, electronic health records (EHRs), and mobile health (mHealth) applications, there is a growing need to understand how these technologies are being used, what outcomes they produce, and the challenges faced in their implementation. This review aimed to: (1) identify and synthesize original empirical studies conducted in Nigeria that report the use of technologies in patient care, (2) evaluate the outcomes associated with their application, and (3) explore the barriers and enabling factors influencing their implementation. A systematic review was conducted following PRISMA 2020 guidelines. A total of 903 studies were retrieved from five databases—Google, Google Scholar, African Journals Online (AJOL), PubMed, and Scopus—using a structured search strategy. After removing duplicates and applying eligibility criteria, 22 original studies published between January 2020 and March 2025 were included. Data were extracted and synthesized narratively based on the three objectives, and each study was appraised using the CASP checklist. The review revealed diverse use of digital technologies, with telemedicine, EHRs, mHealth, and AI tools most commonly applied in hospitals and primary care settings. Reported outcomes included improved care efficiency, better access to services, and enhanced patient tracking and clinical decision-making. However, usage was inconsistent across settings. Major barriers included poor infrastructure, limited digital literacy, and lack of institutional support, while enabling factors included mobile device availability, positive user attitudes, and stakeholder engagement. Digital technologies are reshaping patient care delivery in Nigeria, but their full potential remains unrealized. Addressing systemic barriers through policy support, infrastructure investment, and training will be essential to scaling up digital health innovation and ensuring equitable healthcare access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".