Medical-Economic and Ecological Impact of Anesthesia Teleconsultation: Retrospective Observational Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».