Implementation of a National Electronic Health Information System in Gabon: A Survey of Healthcare Providers’ Perceptions.
Bibliographic record
Abstract
Abstract Background: Health information systems bring several benefits for the health system, healthcare providers, and the population. As a set of tools for the collection, storage, management and transmission of health data, their role to support the modernization of health systems, improve access to quality health care and reduce costs in developing countries is unquestionable. However, HIS implementation in low-income countries face several challenges. In Gabon, a unique initiative called eGabon has been launched in order to modernize the country’s infrastructures, notably through the deployment of a unique national electronic HIS that will connect health care institutions and providers at all levels in the whole country. Objective: This study aims to identify the factors influencing the optimal use of the national electronic HIS by healthcare providers in Gabon. Methods: We used an adaptation of the Information System Success Model ( and developed a questionnaire that was distributed to 2600 healthcare providers across the country). Reliability and validity of the instrument were tested, and we performed a logistic regression to identify the factors influencing healthcare providers’ perceptions towards the national electronic HIS. Results: A total of 2327 questionnaires were received from healthcare providers, of which 1930 were usable in the analyses. The reliability and validity of the questionnaire were supported. The logistic regression identified five constructs that significantly influence perceived system impact: System quality, Information quality, Support quality, Actual use and Useful functions. The model explains 30% of the variance in providers’ perception that the HIS leads to positive impacts. Discussion: This study provides support to the use of an adapted ISSM in the context of HIS implementation in a low-income setting. The results show that health care providers’ perceptions regarding the positive impact of the HIS are influenced by their previous use of a HIS, the extent of their use, their perceptions of system quality, information quality and quality of the support provided to users. These results could inform the development of strategies to ensure adequate change management and user experience for the implementation of the national electronic HIS, and eventually in other low-resources settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".