Key Enablers to Boost Digital Health Solutions in Latin America
Bibliographic record
Abstract
Background COVID-19 has exposed the fragility of global health systems. However, the pandemic is perceived to have boosted the use of technology and accelerated digital health solutions (DHS). In Latin America, DHS can increase accessibility and provide more efficient health services. Various key players have relevant roles for innovation within the health care systems. For this study, we focused on health-tech start-ups (developers) and health care providers (implementers) who can cocreate and develop new health care solutions. Objective This research aimed to explore the aspects that boost innovation in the health care ecosystem in Latin America, based on the 5 key aspects of the Innovation Readiness Levels: market, technology, organization, partnerships, and risk. Methods For this research, a qualitative study was conducted using the 5 key aspects of the Innovation Readiness Levels. Two types of organizations were selected: health-tech start-ups (developers) and health care providers (implementers). A total of 12 professionals from Latin America were interviewed. For each interview, quotes related to the 5 aspects were selected and subclustered to find relationships. Results Based on the discovered relationships, 7 aspects to boost DHS in Latin America were identified: agility to respond, facilitating collaboration, building and sharing knowledge, creating user-centered solutions, economic resources and sustainability, ease of technological development and adoption, and reaching beyond hospitals. The first 4 aspects could apply to other regions outside Latin America. The last 3 are related to regional challenges in Latin America. Obstacles and calls to action were identified for each aspect. Conclusions To boost DHS in Latin America, it is necessary to have a complete overview of the patient’s journey and consider all the users involved to understand their needs and identify opportunities to develop new solutions. This will contribute to the improvement of health solutions and patient outreach. Future research is suggested to develop a better understanding of these aspects in the Latin American countries that were not included in this research and to validate whether these are the only key aspects needed. Conflicts of Interest None declared.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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 | 0.008 |
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".