Perceived Benefits and Disadvantages Associated with the Use of the Electronic Consultation Register by Health Providers in the Health District of Toma, Burkina Faso
Bibliographic record
Abstract
Background and Objective: Most countries in sub-Saharan Africa need to catch up in integrating information and communication technologies (ICT) into their health systems. This is mainly because of the need for more infrastructure that allows for reasonable use of the technologies. To support the actions of the Ministry of Health of Burkina Faso, a Non-governmental Organization (NGO) has implemented the integrated electronic diagnostic approach (IeDA) Project. The project includes the deployment of an electronic consultation register (ECR). This article aims to explore the perceptions of healthcare providers on the benefits and disadvantages of using the ECR. Methods: We conducted a qualitative, descriptive study through individual semi-structured interviews with healthcare providers. Data were collected in the Toma health district in December 2021. In addition, a thematic analysis was performed using NVivo software. Results: Thirty-five healthcare workers were interviewed (19 nurses, 7 midwives, 6 mobile community health and hygiene workers, and 3 birth attendants). Two main themes emerged from our analyses, which are the advantages and disadvantages perceived by ECR users. Our data suggest that using the ECR had many benefits ranging from improving healthcare providers' knowledge and performance in terms of patients' care, assisting and helping in patient diagnosis and treatment and improving patient satisfaction. However, the participants also shared their negative perceptions about the ECR, mentioning that it increased their workload. They also reported lengthened consultation time and work duplication as the tool was still in its trial phase and was used along with the paper consultation register. Conclusion and Global Health Implications: The ECR is an effective tool for diagnosis and management, which has several advantages and reasonably satisfies patients. However, disadvantages, including increased workload and lack of fluidity and stability of the system, must be considered to ensure better usability.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".