Implementing telemedicine for cancer care in European healthcare organizations: lessons from the eCAN Joint Action
Bibliographic record
Abstract
PURPOSE: Telemedicine represents a promising innovation to complement conventional cancer care in Europe to improve treatment quality and patient outcomes. Funded between September 2022 and December 2024, the Joint Action "Strengthening eHealth including telemedicine and remote monitoring for health care systems for CANcer prevention and care" (eCAN JA) aimed to bring the benefits of telemedicine to cancer patients across the European Union (EU) Member States, with 35 partner organizations from 16 countries and explored its feasibility through multicenter clinical trials. This study sheds light on key factors to implement telemedicine services for cancer care in European healthcare organizations (HCOs) and offers a forward-looking perspective by conducting a foresight exercise as part of the Sustainability Work Package (WP4) of the eCAN JA. DESIGN/METHODOLOGY/APPROACH: Foresight is an umbrella term for innovative strategic planning, policy formulation and solution design methods that empower decision-makers. For our multi-country foresight study, we followed three sequential steps to gain qualitative and quantitative findings on the facilitators of telemedicine services for cancer patients in clinical settings, comprised of (1) literature review; (2) surveys to HCOs in different EU Member States and (3) a foresight workshop with survey respondents. FINDINGS: Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility and addressing resource constraints for hybrid care models for success. Future policies in the EU should focus on establishing telemedicine training for healthcare professionals and support interoperability as well as user-friendliness of telemedicine services in HCOs. Policies should also address clinical workload challenges, enable harmonized telemedicine protocols across Europe, provide incentives for implementation and invest in digital infrastructure in HCOs for a sustainable telemedicine adoption. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to conduct a foresight analysis on the feasibility of telemedicine for cancer care across different HCOs in EU Member States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".