Telemedicine: New horizons in healthcare
Bibliographic record
Abstract
Objective: Telemedicine allows physicians to provide medical care remotely through audiovisual technology. Telemedicine may address many challenges facing our society: an aging population, chronic disease management, and healthcare cost. With this work, we attempt to evaluate how telemedicine can effect a change in these challenges, and evaluate what obstacles prevent some providers from using it.Methods: In this work, the cost-effectiveness, success of telemedicine care, usefulness in reaching developing and underdeveloped areas, difficulties preventing the use of telemedicine, and proposals to overcome these challenges were reviewed and analyzed.Results: Cost of telemedicine was reported 19% less expensive than traditional face-to-face care. In several studies, telemedicine was documented to have had equal or better outcomes for obstructive sleep apnea, geriatrics, heart failure, preventative medicine, and patient compliance. Difficulties in using telemedicine include affordability of equipment, lack of technical support in developing or underdeveloped areas, legality of licensure and patient privacy and satisfaction.Conclusions: Although cost savings and convenience are major advantages of this technology, concerns with delivery barriers and challenges require cautious embracement of telemedicine. A great deal of research is needed to show that telemedicine improves patient centered outcomes.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".