Implementing telehealth to support medical practice in rural/remote regions: what are the conditions for success?
Bibliographic record
Abstract
BACKGROUND: Telehealth, as other information and communication technologies (ICTs) introduced to support the delivery of health care services, is considered as a means to answer many of the imperatives currently challenging health care systems. In Canada, many telehealth projects are taking place, mostly targeting rural, remote or isolated populations. So far, various telehealth applications have been implemented and have shown promising outcomes. However, telehealth utilisation remains limited in many settings, despite increased availability of technology and telecommunication infrastructure. METHODS: A qualitative field study was conducted in four remote regions of Quebec (Canada) to explore perceptions of physicians and managers regarding the impact of telehealth on clinical practice and the organisation of health care services, as well as the conditions for improving telehealth implementation. A total of 54 respondents were interviewed either individually or in small groups. Content analysis of interviews was performed and identified several effects of telehealth on remote medical practice as well as key conditions to ensure the success of telehealth implementation. RESULTS: According to physicians and managers, telehealth benefits include better access to specialised services in remote regions, improved continuity of care, and increased availability of information. Telehealth also improves physicians' practice by facilitating continuing medical education, contacts with peers, and access to a second opinion. At the hospital and health region levels, telehealth has the potential to support the development of regional reference centres, favour retention of local expertise, and save costs. Conditions for successful implementation of telehealth networks include the participation of clinicians in decision-making, the availability of dedicated human and material resources, and a planned diffusion strategy. Interviews with physicians and managers also highlighted the importance of considering telehealth within the broader organisation of health care services in remote and rural regions. CONCLUSION: This study identified core elements that should be considered when implementing telehealth applications with the purpose of supporting medical practice in rural and remote regions. Decision-makers need to be aware of the specific conditions that could influence telehealth integration into clinical practices and health care organisations. Thus, strategies addressing the identified conditions for telehealth success would facilitate the optimal implementation of this technology.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".