Bibliographic record
Abstract
Telehealth technologies show tremendous promise in helping reduce health care costs by bridging distance and time. However, neither of the two competing visions for how telehealth should be used is scalable. On the one hand, remote telehealth care providers who triage or monitor patients, are not integrated into the health care system. They are outsiders, lacking access to the records of patients. On the other hand, face-to-face providers who provide the bulk of care in the health care system, are increasingly being asked to keep track of remote monitoring data and to manage patients remotely. They are insiders, but lack the time or training to manage patients remotely. In this paper, we propose a third way: integrate telehealth care providers into the primary care team as a virtual team. Being virtual, they can provide complementary services that patients need, but are not getting, such as peak and off hours care, off hours disease management advice, between-visit support and follow-up and remote device monitoring. We also describe the design requirements, issues and solutions for integrating telehealth technology into electronic medical record systems.
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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| 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".