IDENTIFYING THE OPTIMAL MODEL FOR THE DELIVERY OF PREOPERATIVE REGIONAL ANAESTHESIA IN THE CONTEXT OF THE ONGOING LABOUR CRISIS
Notice bibliographique
Résumé
Regional anesthesia (RA) is being increasingly utilized in orthopaedic surgery due to its clinical and perioperative efficiency benefits. It allows parallel processing of patients while preceding surgical cases are ongoing. Although several staffing models exist, they broadly fall into two categories: 1) a “back and forth” co-management of patients with allied health professionals such as anesthesia assistants (“back and forth” model) or 2) an extra anesthetist in a block room (“dedicated anesthetist” model). Each configuration has its unique cost-benefit profile and as such, the purpose of this study is to compare the two common models for perioperative RA delivery across key financial and efficiency metrics. A discrete-event simulation (DES) model of daily OR patient flow for arthroplasty procedures at a mid-sized academic-affiliated hospital performing approximately 10,000 procedures per year was developed. Data from the operating room (OR) management software, OR schedule, accounting department, and published literature was used to construct the model. To compare differences in performance across operational and financial outcome metrics, two scenarios were tested against the current state (baseline). Ten thousand simulations were run with common random numbers to reduce output variability. A comparison was made between the two scenarios across key performance metrics as follows: staffing requirements, hours required per day, and labour costs. The configuration of the number of ORs and cases varied from 2 to 6 ORs performing 3 to 5 cases each. These results were then used as the inputs of a Discounted Cash Flow (DCF) model, with additional model input assumptions based on literature and financial data provided by our accounting department. The configuration of the number of ORs and cases selected for the DCF model was three simultaneous ORs with four cases each per day. The difference in profit between no BR and BR represented the Cash Flow (CF) in per annum. Both scenarios resulted in time savings (mean: 68 min, range: 30–80 min) and incremental labour savings ($55,055–56,355 profit/day) over the current state. In the selected configuration (three ORs, four cases per day), the “back and forth” model was more profitable by $1300 per day than the dedicated BR model, and these incremental benefits over the “dedicated anesthetist” model increased by an additional $1930 with the addition of a fourth OR. This study demonstrates that both scenarios of administering RA are profitable to a baseline model without a block room. The “back and forth” model was financially preferable in all scenarios given the higher cost of a dedicated anesthetist. Notably, the DES model also demonstrated that an additional dedicated anesthetist was required with greater than three simultaneous ORs. As such, the incremental profits of the “back and forth” model over the “dedicated anesthetist” model nearly double with four concurrent ORs. Given the granular detail of our models, our methodology can be applied to hospitals irrespective of size or configuration to rapidly determine the ideal model for each individual hospital. In addition, the model can incorporate other proven efficiency strategies such as machine-learning case scheduling, the staggering of OR start times, and utilization of alternative staffing models.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».