The Impact of the COVID-19 Pandemic on eHealth Use in the Daily Practice and Life of Dutch-Speaking General Practitioners in Belgium: Qualitative Study With Semistructured Interviews
Bibliographic record
Abstract
BACKGROUND: The COVID-19 crisis has led to rapid and far-reaching changes in digital health care, but little is known about what, why, and how changes occurred in eHealth use in Flemish general practice during the pandemic. OBJECTIVE: This study aims to understand how general practitioners (GPs) perceive and evaluate eHealth solutions and their eHealth experience during the COVID-19 pandemic. METHODS: This qualitative study was conducted using in-depth 1-on-1 semistructured interviews with the help of an interview guide. Several areas were identified beforehand to help assess the impact of the COVID-19 pandemic: perceptions of digital technologies in GP practices; changes in the use of these technologies during and after the COVID-19 pandemic; GPs' adaptation to digitalization, benefits, risks, and challenges of eHealth; GPs motivations to change practice; and future perspectives on eHealth. In this study, purposive sampling and snowballing methods were used. Between October 2021 and April 2022, we interviewed 15 Dutch-speaking GPs in the Flemish region via the Zoom online conferencing tool. RESULTS: GPs indicated that eHealth was used more frequently during the COVID-19 pandemic than before, a change that helped them reduce their workload, enabling greater accessibility to health care services and the complementary use of digital and physical consultations. Our findings suggest that physicians underwent a significant cognitive shift in their perceptions, causing them to be more open and prepared to adopt eHealth solutions. However, there remains significant doubt and uncertainty about digital literacy for certain groups, privacy, data security, reimbursement, and the burden of technical information and communication technologies (ICT) issues. CONCLUSIONS: The COVID-19 pandemic seems to have been a turning point for eHealth by Flemish GPs. eHealth is an essential complementary health care service that can reduce pressure on health care as well as increase health care accessibility. Sensitive aspects, such as privacy, data security, digital literacy, reimbursement, and the burden of technical ICT issues, are particularly emphasized. With our results, we can offer recommendations to health IT policymakers and developers that will help maintain the continuity of eHealth solutions beyond the COVID-19 pandemic, considering the expectations and sensitivities presented in the study.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".