Strengths, Weaknesses, Opportunities, and Threats Analysis of the Use of Digital Health Technologies in Primary Health Care in the Sub-Saharan African Region: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Digital health technologies (DHTs) have become increasingly commonplace as a means of delivering primary care. While DHTs have been postulated to reduce inequalities, increase access, and strengthen health systems, how the implementation of DHTs has been realized in the sub-Saharan Africa (SSA) health care environment remains inadequately explored. OBJECTIVE: This study aims to capture the multidisciplinary experiences of primary care professionals using DHTs to explore the strengths and weaknesses, as well as opportunities and threats, regarding the implementation and use of DHTs in SSA primary care settings. METHODS: A combination of qualitative approaches was adopted (ie, focus groups and semistructured interviews). Participants were recruited through the African Forum for Primary Care and researchers' contact networks using convenience sampling and included if having experience with digital technologies in primary health care in SSA. Focus and interviews were conducted, respectively, in November 2021 and January-March 2022. Topic guides were used to cover relevant topics in the interviews, using the strengths, weaknesses, opportunities, and threats framework. Transcripts were compiled verbatim and systematically reviewed by 2 independent reviewers using framework analysis to identify emerging themes. The COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist was used to ensure the study met the recommended standards of qualitative data reporting. RESULTS: A total of 33 participants participated in the study (n=13 and n=23 in the interviews and in focus groups, respectively; n=3 participants participated in both). The strengths of using DHTs ranged from improving access to care, supporting the continuity of care, and increasing care satisfaction and trust to greater collaboration, enabling safer decision-making, and hastening progress toward universal health coverage. Weaknesses included poor digital literacy, health inequalities, lack of human resources, inadequate training, lack of basic infrastructure and equipment, and poor coordination when implementing DHTs. DHTs were perceived as an opportunity to improve patient digital literacy, increase equity, promote more patient-centric design in upcoming DHTs, streamline expenditure, and provide a means to learn international best practices. Threats identified include the lack of buy-in from both patients and providers, insufficient human resources and local capacity, inadequate governmental support, overly restrictive regulations, and a lack of focus on cybersecurity and data protection. CONCLUSIONS: The research highlights the complex challenges of implementing DHTs in the SSA context as a fast-moving health delivery modality, as well as the need for multistakeholder involvement. Future research should explore the nuances of these findings across different technologies and settings in the SSA region and implications on health and health care equity, capitalizing on mixed-methods research, including the use of real-world quantitative data to understand patient health needs. The promise of digital health will only be realized when informed by studies that incorporate patient perspective at every stage of the research cycle.
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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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".