ELEMENTS FOR ASSESSMENT OF TELEMEDICINE APPLICATIONS
Bibliographic record
Abstract
OBJECTIVES: As an initiative of the International Network of Agencies for Health Technology Assessment, an approach to assessment of telemedicine applications was prepared to assist decision makers who are considering introduction and use of this technology. METHODS: Review and commentary drawing on published assessment frameworks and reports of primary evaluations of telemedicine, with particular reference to experience in Finland and Canada. RESULTS: Elements of the approach included development of a business case (considering population and services, personnel and consumers, delivery arrangements, specifications and costs); subsequent evaluation of the telemedicine application; and follow-up (covering the domains of technical assessment, effectiveness, user assessment of the technology, costs of telemedicine, trials, economic evaluation methods, and sensitivity analysis). CONCLUSIONS: Decision makers should link introduction of new and often costly technology to appraisal of its feasibility, followed by evaluation of the application, including longer term consideration of its sustainability and impact on the healthcare system. As the effectiveness and efficiency of telemedicine applications will often be strongly influenced by local issues, results of assessments may not be generalizable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".