Digital Health Opportunities to Improve Primary Health Care in the Context of COVID-19: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic brought social, economic, and health impacts, requiring fast adaptation of health systems. Although information and communication technologies were essential for achieving this objective, the extent to which health systems incorporated this technology is unknown. OBJECTIVE: The aim of this study was to map the use of digital health strategies in primary health care worldwide and their impact on quality of care during the COVID-19 pandemic. METHODS: We performed a scoping review based on the Joanna Briggs Institute manual and guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Extension for Scoping Reviews. A systematic and comprehensive three-step search was performed in June and July 2021 in multidisciplinary health science databases and the gray literature. Data extraction and eligibility were performed by two authors independently and interpreted using thematic analysis. RESULTS: A total of 44 studies were included and six thematic groups were identified: characterization and geographic distribution of studies; nomenclatures of digital strategies adopted; types of information and communication technologies; characteristics of digital strategies in primary health care; impacts on quality of care; and benefits, limitations, and challenges of digital strategies in primary health care. The impacts on organization of quality of care were investigated by the majority of studies, demonstrating the strengthening of (1) continuity of care; (2) economic, social, geographical, time, and cultural accessibility; (3) coordination of care; (4) access; (5) integrality of care; (6) optimization of appointment time; (7) and efficiency. Negative impacts were also observed in the same dimensions, such as reduced access to services and increased inequity and unequal use of services offered, digital exclusion of part of the population, lack of planning for defining the role of professionals, disarticulation of actions with real needs of the population, fragile articulation between remote and face-to-face modalities, and unpreparedness of professionals to meet demands using digital technologies. CONCLUSIONS: The results showed the positive and negative impacts of remote strategies on quality of care in primary care and the inability to take advantage of the potential of technologies. This may demonstrate differences in the organization of fast and urgent implementation of digital strategies in primary health care worldwide. Primary health care must strengthen its response capacity, expand the use of information and communication technologies, and manage challenges using scientific evidence since digital health is important and must be integrated into public service.
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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 | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".