TELEMED: Database on Evidence-Based Telemedicine in a Hospital Setting
Bibliographic record
Abstract
Background The use of telemedicine services has increased worldwide during recent years as a result of national strategies for the digitalization of health care and the COVID-19 pandemic. However, health care professionals often express uncertainty regarding the evidence and effectiveness of telemedicine interventions. Therefore, the Centre for Innovative Medical Technology at Odense University Hospital introduced the TELEMED database, an evidence-based telemedicine database. Objective This study aimed to ensure that hospital managers, health care professionals, and other stakeholders gain access to information about scientific studies of telemedicine interventions and their effectiveness. Methods The database constitutes a structured literature search in PubMed for randomized controlled trials or controlled trials on the effect of telemedicine for somatic diseases treated at hospitals. The search was conducted by staff members in the Health Technology Assessment unit at the Centre for Innovative Medical Technology. First, identified studies were sorted by screening titles and abstracts and, subsequently, by reading full-text versions. The data extracted from the studies included the setting, intervention, patient group, type of telemedicine, clinical effect, patient perception, and implementation challenges. Finally, the value of each study was assessed with respect to effectiveness. Results A total of 518 articles were included for data extraction and assessment. The database provides results from 22 different specialties and can be searched using the following criteria: medical specialty, country, technology, clinical effect, patient experience, and economic effect. The database serves as a platform for the dialogue with clinical departments who wish to implement telemedicine services and has a large potential for supporting the digital transformation during COVID-19 as evidence-based information on patient groups, relevant technologies, and their effect is easily accessible. Conclusions The TELEMED database provides an easily accessible overview of existing evidence-based telemedicine services. The database is freely available and is expected to be continuously improved and broadened over time. Conflicts of Interest None declared.
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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.037 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".