Challenges and Opportunities of the Use and Adoption of Telemedicine for Diabetes Care and Management During the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
Background More than half of the world’s population deals with noncommunicable diseases causing premature death. Leveraging digital solutions like telemedicine, health care providers (HCPs) can provide medical care remotely. Yet, there is little known about the contextual challenges and opportunities of leveraging telemedicine solutions in varying socioeconomic and cultural contexts, including Kuwait. Objective The aims of this paper were as follows: (1) uncover the challenges and opportunities of adopting and using telemedicine for diabetes care and management from the viewpoints of HCPs and patients with diabetes; (2) explore nonfunctional requirements for telemedicine applications for diabetes care and management; and (3) offer recommendations to improve the adoption of telemedicine in Kuwait’s health care system for diabetes care and management. Methods Through semistructured interviews, this study employed a qualitative and exploratory design to uncover rich context-specific findings. Participants were recruited via social media platforms. The analysis followed a thematic analysis approach and used the framework method. Researchers used the “diffusion of innovation” model as a lens to guide the analysis and interpretation of the results. Results A total of 20 interviews were conducted—10 (50%) HCPs and 10 (50%) patients with diabetes. The participants were familiar with and interested in adopting telemedicine. Challenges included a lack of telemedicine infrastructure and how to increase patients’ technology awareness. Patients with diabetes mentioned that telemedicine would save time and effort. The participants suggested developing a secure, user-friendly telemedicine solution. They stated the importance of telemedicine during the pandemic, as many diabetes cases can be followed up online, which reduces virus spread and increases patients’ safety. Conclusions The findings from this study can give a better understanding of what HCPs and patients with diabetes need to accept the adoption of telemedicine in resource-rich countries like Kuwait. The COVID-19 pandemic impacted the ways HCPs deliver medical care to patients and encouraged both HCPs and patients to explore the digital platform for continuous care and management of diabetes. Conflicts of Interest None declared.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".