Implementing Telemedicine in Clinical Practice in the First Digital Hematology Unit: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Currently, there are no telemedicine models that fully integrate all areas of hematology into daily practice. OBJECTIVE: The objectives of this feasibility study were to assess the practicality of implementing telemedicine into our clinical practice in the first Digital Hematology Unit and propose an innovative integrative design for clinical practice. METHODS: We designed the Digital Hematology Unit, which is a specific physical space dedicated to carrying out telemedicine and monitoring patients in a holistic way. Also, a satisfaction questionnaire was performed and health care indicators were measured. RESULTS: In 2021, there were 1331 first visits and 7534 follow-up visits. Of the first visits, 12.2% (n=163) were face-to-face and 87.8% (n=1168) were telematic. For follow-up visits, 29.9% (n=2251) were face-to-face and 70.1% (n=5283) were telematic. The health care management indicators showed that we had a waiting time of less than 4 days and took less than 4 hours to answer interconsultations among specialists. Moreover, patients reported a high level of satisfaction with the services provided. CONCLUSIONS: Our Digital Hematology Unit, as a case of success, serves as an example of how innovative digital solutions can contribute to the quality of care and excellence in health care achieved through a digital transformation process led by hematologists.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".