Implementation of a national electronic health information system in Gabon: a survey of healthcare providers’ perceptions
Bibliographic record
Abstract
BACKGROUND: The Health Information System (HIS) is a set of computerized tools for the collection, storage, management, and transmission of health data. The role of such tools 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 HIS has 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 in Gabon. METHODS: A 44-item questionnaire based on the Information System Success Model (ISSM) was administered between February and April 2018 among 2600 healthcare providers across the country. The questions assessed the different aspects of the HIS that could influence its perceived impact on a 5-level Likert scale (from fully agree to totally disagree). The reliability and construct validity of the questionnaire were checked using Cronbach alpha and congeneric reliability coefficients. A logistic regression was used to identify the factors influencing healthcare providers' perceptions of 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' perception that the national HIS leads to positive impacts. DISCUSSION: The results show that healthcare providers' perceptions regarding the positive impact of the national HIS in Gabon are influenced by their previous use of an 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 HIS in Gabon, and eventually in other low resource environments.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".