What are the objective key elements for successful deployment of telemedicine in hospitals: A holistic approach after 2 years of using a connected tracking solution
Bibliographic record
Abstract
Objective: The number of telemedicine solutions is growing, and studies are focusing on feasibility assessments. It is time to consider the fundamentals of deploying telemedicine solutions and provide recommendations for effective implementation.Methods: A qualitative data collection through observation and interview was conducted at our tertiary academic hospital after 2 years of experience with a telemedicine solution. The data underwent semantic analysis, and hypotheses were compared with a literature review to provide recommendations for implementation. Between February 2021 and October 2022, patients’ opinions were gathered through feedback questionnaires using the institutional mHealth application, a key component of the deployed telemedicine solution. Satisfaction results guided conclusions and reevaluations.Results: During April 2021, 14 interviews were conducted with 7 medical department chairs, 2 head nurses and 5 administrative leaders. Between February 2021 and October 2022, a total of 760 surgical patients used the mobile application CHUV@home and 478 (62.9%) answered the feedback questionnaire. During this period, 1,226 surgical patients were included, and 760 used the mobile application, generating 1,693 alerts with an average resolution time of 130 minutes per alert. Feedback questionnaires were answered by 478 (62.9%) patients, with global satisfaction. Patients and healthcare workers opinions were aligned to foster a design of telemedicine experience. Results were presented in the form of a risk matrix. Five major risks and their mitigation recommendations were highlighted.Conclusions: With the growing number of telemedicine solutions, many studies focus on feasibility assessment. The present study suggests that a holistic approach, engaging healthcare workers and patients, is essential for developing a meaningful and sustainable telemedicine strategy at a broader systemic level.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".