Medical-Economic and Ecological Impact of Anesthesia Teleconsultation: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine, particularly teleconsultation, has emerged as a viable alternative to in-person consultation, especially following the COVID-19 pandemic. Preanesthetic consultations are mandatory before surgery to assess perioperative risk. However, little data exists regarding the combined economic and ecological impacts of replacing in-person consultation with teleconsultation in this context. OBJECTIVE: The primary aim was to evaluate the financial and environmental benefits of teleconsultation for preanesthetic consultation. Secondary objectives included assessing patient satisfaction and perioperative safety. METHODS: This retrospective, single-center observational study included patients scheduled for orthopedic surgery between September 2020 and October 2020 at Toulouse University Hospital. Eligible patients completed a preconsultation questionnaire via the MyAnesth digital agent. Patients were allocated to teleconsultation or in-person consultation groups based on predefined criteria. Postoperative data on demographics, transportation, consultation modality, time off work, and patient satisfaction were collected. Economic analysis included travel costs, income loss, and health insurance reimbursements. Ecological analysis quantified greenhouse gas (GHG) emissions based on transportation mode and digital infrastructure use. Statistical comparisons between the teleconsultation and in-person consultation groups used appropriate parametric and nonparametric tests, with significance set at P≤.05. RESULTS: A total of 401 patients were analyzed (teleconsultations: n=331, 82.5%; in-person consultations: n=70, 17.5%). Teleconsultations reduced the average travel distance by 46,000 km, corresponding to 9.7 tons of carbon dioxide equivalent saved. Mean cost savings per patient were €122 (SD €125; 1 US $=€1.17), with total savings of €42,840 for patients and the national health care system. Teleconsultations also significantly reduced time spent on travel and administrative processes (mean 22, SD 9 minutes vs mean 130, SD 16 minutes for in-person consultations; P<.001). No significant differences in postoperative complication rates were observed between groups (teleconsultations: 11/331, 3.3%; in-person consultations: 5/70, 7.1%; P=.24). Patient satisfaction scores were high and similar in both groups (median 9, IQR 8-10, of a possible 10), with most patients preferring teleconsultations or expressing no preference for consultation modality. Digital teleconsultation infrastructure contributed minimally to GHG emissions (2.3 kg of carbon dioxide equivalent for 331 teleconsultations), representing a 99% reduction compared to travel-based in-person consultations. CONCLUSIONS: Teleconsultations for preanesthetic assessment demonstrated significant economic and ecological advantages without compromising clinical safety or patient satisfaction. Patients reported high levels of satisfaction and minimal attachment to in-person consultations and appreciated the convenience of remote access. This model reduces unnecessary travel, limits health care-related GHG emissions, and generates considerable cost savings for both patients and public health systems. These findings support broader integration of teleconsultations into routine anesthetic care, particularly for low-risk outpatient surgical candidates. Expanding teleconsultation eligibility criteria could enhance health care system efficiency and contribute to sustainable medical practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".