Clinical Telehealth Across the Disciplines: Lessons Learned
Bibliographic record
Abstract
Videoconferencing technologies can vastly expand the reach of healthcare practitioners by providing patients (particularly those in rural/remote areas) with unprecedented access to services. While this represents a fundamental shift in the way that healthcare professionals care for their patients, very little is known about the impact of these technologies on clinical workflow practices and interprofessional collaboration. In order to better understand this, we have conducted a focused literature review, with the aim of providing policymakers, administrators, and healthcare professionals with an evidence-based foundation for decision-making. A total of 397 articles focused on videoconferencing in clinical contexts were retrieved, with 225 used to produce this literature review. Literature in the fields of medicine (including general and family practitioners and specialists in neurology, dermatology, radiology, orthopedics, rheumatology, surgery, cardiology, pediatrics, pathology, renal care, genetics, and psychiatry), nursing (including hospital-based, community-based, nursing homes, and home-based care), pharmacy, the rehabilitation sciences (including occupational and physical therapy), social work, and speech pathology were included in the review. Full utilization of the capacity of videoconferencing tools in clinical contexts requires some basic necessary technical conditions to be in place (including basic technological infrastructure, site-to-site technological compatibility, and available technical support). The available literature also elucidates key strategies for organizational readiness and technology adoption (including the development of a change management and user training plan, understanding program cost and remuneration issues, development of organizational protocols for system use, and strategies to promote interprofessional collaboration).
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".