Implementation of a National Electronic Health Information System in Gabon: A Survey of Healthcare Providers’ Perceptions
Bibliographic record
Abstract
Abstract Background: Health information System(HIS) is a set of computerized toolsfor the collection, storage, management and transmission of health data.Their role in supporting the modernization of health systems, improving access to quality healthcare and reducing costs in developing countries is unquestionable; but their implementation faces several challenges. In Gabon, a unique national electronic HIShas been launched.It will connect healthcare institutions and providers at all levels in the whole country.Objective: This study aims to explore and identify the factors influencing healthcare providers’ perceptions of the national electronic HIS. Methods: We adapted a questionnairebased on the Information System Success Model (ISSM).Twenty six hundreds(2600) healthcare providers,recruited across the country, took part in the research. We checked the reliability and validity of the application and performed a logistic regression to identify the factors influencing healthcare providers’ perceptions towards the system.Results: A total of 2327 questionnaires were completed (i.e. 89.5% response rate). The logistic regression identified five elements that significantly influenced perceived system impact: System Quality (Odds Ratio–OR=1.70), Information Quality (OR=1.69), Actual Use (OR=1.41), Support Quality (OR=1.37), and Useful Functions (OR=1.14). The model explained 30% of the variance in providers’ perceptionshow that the HIS leads to positive impacts. Discussion: The results show that healthcare providers’ perceptions regarding the positive impact are influenced by their use of a previous HIS, the scope of their usage and the quality of the system, information and support provided to users. These results could inform the development of strategies to ensure adequate change of management and user experience for the implementation of the national electronic HIS in Gabon, and eventually in other low resources environment.
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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 | 0.006 | 0.010 |
| 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 | 0.001 | 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".