Évaluation des unités mobiles de télémédecine au centre hospitalier de Périgueux : bilan et état des lieux
Bibliographic record
Abstract
Background: emergency departments in France are increasingly overwhelmed due to rising demand, declining medical workforce, and population aging. To address these challenges, innovative models such as mobile telemedicine units (UMT) have been developed. In 2022, the centre hospitalier de Périgueux (Dordogne, France) was one of the first hospitals to implement such a device. Objectives: the main objective was to describe the activity of the UMT deployed between 2022 and 2025. The secondary objective was to assess healthcare professional’s perceptions of this organizational model. Methods: we conducted a descriptive, observational, single-center study. A total of 451 patients records were extracted from the EXOS software. Collected data included demographics, reason for call, type of medical regulation (emergency vs. private physicians), interventions carried out, prescriptions, and final patient orientation. In parallel, a structured 10-item questionnaire was distributed both on paper and online to physicians, nurses, nursing assistants, and ambulance staff. Thirty responses were analyzed. Results: a total of 451 patients were managed by the mobile telemedicine units (UMT) of Périgueux Hospital between 2022 and 2025. Patients were mainly elderly (mean age: 70 years) and predominantly treated at home. The most frequent reasons for intervention were general condition deterioration, urinary disorders, abdominal pain, and malaise. The most common procedures included ECG, urinary catheterization, analgesic administration, and peripheral venous access. Over half of the patients remained at home, while about one quarter required hospital transfer. UMT activity peaked in 2022–2023 but declined in 2024. The satisfaction survey showed a positive perception of the system, although staffing issues were highlighted. Conclusion: this work provides one of the first structured evaluations of an UMT in a rural French setting. Over half of patients managed by Mobile Telemedicine Units (UMT) were maintained at home, highlighting their relevance for non-urgent, unplanned care in a context of rising demand. The positive perception of healthcare professionals supports the acceptability of the system, which strengthens collaboration between hospital and community care. UMTs appear as a credible complementary tool within the healthcare system, requiring further optimization and evaluation of their long-term impact.It highlights the potential contribution of such units to improve access to care and strengthen healthcare organization in resource-limited areas.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".