An examination of policy implications for scope of services and geography for telehealth
Bibliographic record
Abstract
Background. Why, despite enthusiasm, is telehealth still a relatively minor part of healthcare delivery in many health systems? We examined two less-considered policy issues: the scope of services being offered by telehealth and how this matches existing arrangements for insured services; and how telehealth services, which allow barriers associated with geography to be minimized, are managed in a system organized and financed on provincial/regional boundaries. Specifically, this study addressed the following research questions: (1) How do telehealth programs address the complexities that arise from the conflict between a health care system oriented towards geographically-based coverage and the ability of telehealth to erode distance and access barriers? (2) What types of services do Canadian telehealth programs currently offer? (3) How well does the scope of services currently being offered match existing definitions of insured services? (4) How are telehealth services currently financed? (5) What do these findings mean for the ability to integrate telehealth services into the Canadian health care environment?Conclusion. Despite high hopes that telehealth would improve access to care for rural/remote areas, gatekeeping inherent in certain telehealth systems imposes barriers to unfettered use by rural/remote areas, although it does facilitate other valued activities. Policy approaches are needed to promote a closer match between the expectations for telehealth and the realities reflected by many existing models. Methods. Fifty-three semi-structured interviews with key stakeholders involved in the management of 43 Canadian telehealth programs were conducted. As well, a document review was undertaken. Quantitative activity data was analyzed from 33 telehealth programs. Results. Two telehealth models emerged: telephone-based (N=3), and videoconferencing-based (N=40). Most programs reflected, rather than superseded, existing geographical boundaries; with the technology being used, the videoconferencing models imposed significant barriers to unfettered access by outlying communities because they required sites to acquire expensive technology, be affiliated with an existing telehealth network, and schedule visits in advance. In consequence, much activity was administrative and educational, rather than clinical, and often extended beyond the set of mandatory insured services.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".