• Markus M Luedi, Peter Kauf, Lisa Mulks, Katharina Wieferich, Ralf Schiffer, and Dietrich Doll.
• From the *Department of Anesthesiology, Bern University Hospital Inselspital, University of Bern, Bern, Switzerland; †PrognosiX AG, Richterswil, Switzerland; ‡Institute of Applied Simulation, Zurich University of Applied Sciences ZHAW, Waedenswil, Switzerland; §Department of Surgery, Saint Mary's Hospital Vechta, Teaching Hospital of Hannover University, Vechta, Germany; ∥Department of Finances and Controlling, Mary's Hospital Vechta, Teaching Hospital of Hannover University, Vechta, Germany; and ¶Department of Procto-Surgery, St. Marienhospital Vechta, Academic Teaching Hospital of the Medizinische Hochschule Hannover, Vechta, Germany.
• Anesth. Analg. 2016 Apr 1; 122 (4): 1169-77.

BackgroundIn elderly, high-risk patients, operating room (OR) turnaround times are especially difficult to estimate, and the managerial implications of patient age and ASA physical status for OR management decisions remain unclear. We hypothesized that evaluating patient age and ASA physical status in the right model would improve accuracy of turnaround time estimates and, thus, would have decisive implications for OR management.MethodsBy using various multivariate techniques, we modeled turnaround times of 13,632 OR procedures with respect to multiple variables including surgical list, age, ASA physical status, duration of the procedure, and duration of the preceding procedure. We first assessed correlations and general descriptive features of the data. Then, we constructed decision tables for OR management consisting of 50th and 95th percentiles of age/ASA-dependent estimates of turnaround times. In addition, we applied linear and generalized linear multivariate models to predict turnaround times. The forecasting power of the models was assessed in view of single cases but also in view of critical managerial key figures (50th and 95th percentile turnaround times). The models were calibrated on 80% of the data, and their predictive value was tested on the remaining 20%. We considered our data in a Monte Carlo simulation to deduce actual reductions of overutilized OR time when applying the results as presented in this work.ResultsUsing the best models, we achieved an increase in predictive accuracy of 7.7% (all lists), ranging from 2.5% (general surgery) to 21.0% (trauma surgery) relative to age/ASA-independent medians of turnaround times. All models decreased the forecasting error, signifying a relevant increase in planning accuracy. We constructed a management decision table to estimate age/ASA-dependent turnaround time for OR scheduling at our hospital.ConclusionsThe decision tables allow OR managers at our hospital to schedule procedures more accurately. Evaluation of patient age and ASA physical status as variables can help to better predict turnaround times, which can facilitate scheduling, for example, to schedule overlapping induction rooms, to reduce overutilized OR time by optimizing allocation of patients to several ORs, and to improve logistics of prioritizing transportation of advanced age/high ASA physical status patients to the OR.

30 keywords...

hide…