Discrepancies Between Planned and Actual Operating Room Turnaround Times at a Large Rural Hospital in Germany.

OBJECTIVES: While several factors have been shown to influence operating room (OR) turnaround times, few comparisons of planned and actual OR turnaround times have been performed. This study aimed to compare planned and actual OR turnaround times at a large rural hospital in Northern Germany. METHODS: This retrospective study examined the OR turnaround data of 875 elective surgery cases scheduled at the Marienhospital, Vechta, Germany, between July and October 2014. The frequency distributions of planned and actual OR turnaround times were compared and correlations between turnaround times and various factors were established, including the time of day of the procedure, patient age and the planned duration of the surgery. RESULTS: There was a significant difference between mean planned and actual OR turnaround times (0.32 versus 0.64 hours; P <0.001). In addition, significant correlations were noted between actual OR turnaround times and the time of day of the surgery, patient age, actual duration of the procedure and staffing changes affecting the surgeon or the medical specialty of the surgery (P <0.001 each). The quotient of actual/planned OR turnaround times ranged from 1.733-3.000. CONCLUSION: Significant discrepancies between planned and actual OR turnaround times were noted during the study period. Such findings may be potentially used in future studies to establish a tool to improve OR planning, measure OR management performance and enable benchmarking.

Teaching Advanced Trauma Life Support (ATLS): A nationwide retrospective analysis of 8202 lessons taught in Germany.

OBJECTIVE: To examine whether faculty who teach the Advanced Trauma Life Support (ATLS) course would improve with experience and, correspondingly, ratings from course evaluations would increase. DESIGN: Retrospective analysis of student evaluations of 262 ATLS courses held between 2008 and 2012. SETTING: All ATLS courses held between 2008 and 2012 nationwide in Germany. PARTICIPANTS: All ATLS student course evaluations covering 8202 lessons, 81 instructors, 36 course directors, and 5 coordinators. RESULTS: ATLS courses in Germany attained high levels of student satisfaction. Satisfaction levels increased steadily over the 5-year period studied. The entire staff influenced this finding. Teaching quality improved the most within the first 100 lessons taught. Skill stations received better evaluations than lectures, and local courses were less satisfactory than national course formats. The 2 demonstrations that open the course were the top rated events. Skill stations, including a human phantom, were highly rated; the cricothyrotomy station was top rated. CONCLUSION: The German ATLS course evaluations indicated steady improvement over the 5-year study. The level of experience of course coordinators, directors, and instructors influenced this finding. Teaching quality improved most within the first 100 lessons taught, and then reached a steady state. Skill stations received better evaluations than lectures, and local courses were less satisfactory than national course formats.

Implications of Perioperative Team Setups for Operating Room Management Decisions.

BACKGROUND: Team performance has been studied extensively in the perioperative setting, but the managerial impact of interprofessional team performance remains unclear. We hypothesized that the interplay between anesthesiologists and surgeons would affect operating room turnaround times, and teams that worked together over time would become more efficient. METHODS: We analyzed 13,632 surgical cases at our hospital that involved 64 surgeons and 48 anesthesiologists. We detrended and adjusted the data for potential confounders including age, American Society of Anesthesiologists physical status, and surgical list (scheduled cases of specific surgical specialties). The surgical lists were categorized as ear, nose, and throat surgery; trauma surgery; general surgery; and gynecology. We assessed the relationship between turnaround times and assignment of different anesthesiologists to specific surgeons using a Monte Carlo simulation. RESULTS: We found significant differences in team performances among the different surgical lists but no team learning. We constructed managerial decision tables for the assignment of anesthesiologists to specific surgeons at our hospital. We defined a decision algorithm based on these tables. Our analysis indicated that had this algorithm been used in staffing the operating room for the surgical cases represented in our data, median turnaround times would have a reduction potential of 6.8% (95% confidence interval 6.3% to 7.1%). CONCLUSIONS: A surgeon is usually predefined for scheduled surgeries (surgical list). Allocation of the right anesthesiologist to a list and to a surgeon can affect the team performance; thus, this assignment has managerial implications regarding the operating room efficiency affecting turnaround times and thus potentially overutilized time of a list at our hospital.

Implications of Patient Age and ASA Physical Status for Operating Room Management Decisions.

BACKGROUND: In 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. METHODS: By 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. RESULTS: Using 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. CONCLUSIONS: The 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.