Barriers and facilitators in the implementation of a telemedicine-based outpatient brain tumor surgery program
Bibliographic record
Abstract
OBJECTIVE: Despite growing evidence on the benefits of outpatient oncological neurosurgery (OON), it is only performed in a few specialized centers and there are no previous descriptions of established OON programs in Europe. Moreover, increasing application of telemedicine strategies, especially after the start of the coronavirus disease 2019 (COVID-19) pandemic, is drastically changing neurosurgical management, particularly in the case of vulnerable populations such as neuro-oncological patients. In this context, the authors implemented an OON program in their hospital with telematic follow-up. Herein, they describe the protocol and qualitatively analyze the barriers and facilitators of the development process. METHODS: An OON program was developed through the following steps: assessment of hospital needs, specific OON training, multidisciplinary team organization, and OON protocol design. In addition, the implementation phase included training sessions, a pilot study, and continuous improvement sessions. Finally, barriers and facilitators of the protocol's implementation were identified from the feedback of all participants. RESULTS: An OON protocol was successfully designed and implemented for resection or biopsy of supratentorial lesions up to 3 cm in diameter. The protocol included the patient's admission to the day surgery unit, noninvasive anesthetic monitoring, same-day discharge, and admission to the hospital-at-home (HaH) unit for telematic and on-site postoperative care. After a pilot study including 10 procedures in 9 patients, the main barriers identified were healthcare provider resistance to change, lack of experience in outpatient neurosurgery, patient reluctance, and limitations in the recruitment of patients. Key facilitators of the process were the patient education program, the multidisciplinary team approach, and the HaH-based telematic postoperative care. CONCLUSIONS: Initiating an OON program with telematic follow-up in a European clinical setting is feasible. Nevertheless, it poses several barriers that can be overcome by identifying and maximizing key facilitators of the process. Among them, patient education, a multidisciplinary team approach, and HaH-based postoperative care were crucial to the success of the program. Future studies should investigate the cost-effectiveness of telemedicine to assess potential cost savings, from reduced travel and wait times, and the impact on patient satisfaction.
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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) | 0.001 | 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".