Bethany Daily

Deliberate Perioperative Systems Design Improves Operating Room Throughput

Background:New operating room (OR) design focuses more on the surgical environment than on the process of care. The authors sought to improve OR throughput and reduce time per case by goal-directed design of a demonstration OR and the perioperative processes occurring within and around it. Methods:The authors constructed a three-room suite including an OR, an induction room, and an early recovery area. Traditionally sequential activities were run in parallel, and nonsurgical activities were moved from the OR to the supporting spaces. The new workflow was supported by additional anesthesia and nursing personnel. The authors used a retrospective, case- and surgeon-matched design to compare the throughput, cost, and revenue performance of the new OR to traditional ORs. Results:For surgeons performing the same case mix in both environments, the new OR processed more cases per day than traditional ORs and used less time per case. Throughput improvement came from superior nonoperative performance. Nonoperative Time was reduced from 67 min (95% confidence interval, 64–70 min) to 38 min (95% confidence interval, 35–40 min) in the new OR. All components of Nonoperative Time were meaningfully reduced. Operative Time decreased by approximately 5%. Hospital and anesthesia costs per case increased, but the increased throughput offset costs and the global net margin was unchanged. Conclusions:Deliberate OR and perioperative process redesign improved throughput. Performance improvement derived from relocating and reorganizing nonoperative activities. Better OR throughput entailed additional costs but allowed additional patients to be accommodated in the OR while generating revenue that balanced these additional costs.

A model for understanding the impacts of demand and capacity on waiting time to enter a congested recovery room.

Background:When a recovery room is fully occupied, patients frequently wait in the operating room after emerging from anesthesia. The frequency and duration of such delays depend on operating room case volume, average recovery time, and recovery room capacity. Methods:The authors developed a simple yet nontrivial queueing model to predict the dynamics among the operating and recovery rooms as a function of the number of recovery beds, surgery case volume, recovery time, and other parameters. They hypothesized that the model could predict the observed distribution of patients in recovery and on waitlists, and they used statistical goodness-of-fit methods to test this hypothesis against data from their hospital. Numerical simulations and a survey were used to better understand the applicability of the model assumptions in other hospitals. Results:Statistical tests cannot reject the prediction, and the model assumptions and predictions are in agreement with data. The survey and simulations suggest that the model is likely to be applicable at other hospitals. Small changes in capacity, such as addition of three beds (roughly 10% of capacity) are predicted to reduce waiting for recovery beds by approximately 60%. Conversely, even modest caseload increases could dramatically increase waiting. Conclusions:A key managerial insight is that there is a sensitive relationship among caseload and number of recovery beds and the magnitude of recovery congestion. This is typical in highly utilized systems. The queueing approach is useful because it enables the investigation of future scenarios for which historical data are not directly applicable.

Automatic Detection and Notification of “Wrong Patient—Wrong Location” Errors in the Operating Room:

When procedures and processes to assure patient location based on human performance do not work as expected, patients are brought incrementally closer to a possible “wrong patient—wrong procedure” error. We developed a system for automated patient location monitoring and management. Realtime data from an active infrared/radio frequency identification tracking system provides patient location data that are robust and can be compared with an “expected process” model to automatically flag wrong-location events as soon as they occur. The system also generates messages that are automatically sent to process managers via the hospital paging system, thus creating an active alerting function to annunciate errors. We deployed the system to detect and annunciate “patientin-wrong-OR” events. The system detected all “ wrongoperating room (OR)” events, and all “wrong-OR” locations were correctly assigned within 0.50 ± 0.28 minutes (mean ± SD). This corresponded to the measured latency of the tracking system. All wrong-OR events were correctly annunciated via the paging function. This experiment demonstrates that current technology can automatically collect sufficient data to remotely monitor patient flow through a hospital, provide decision support based on predefined rules, and automatically notify stakeholders of errors.

Elective change of surgeon during the OR day has an operationally negligible impact on turnover time

STUDY OBJECTIVE To compare turnover times for a series of elective cases with surgeons following themselves with turnover times for a series of previously scheduled elective procedures for which the succeeding surgeon differed from the preceding surgeon. DESIGN Retrospective cohort study. SETTING University-affiliated teaching hospital. MEASUREMENTS The operating room (OR) statistical database was accessed to gather 32 months of turnover data from a large academic institution. Turnover time data for the same-surgeon and surgeon-swap groups were batched by month to minimize autocorrelation and achieve data normalization. Two-way analysis of variance (ANOVA) using the monthly batched data was performed with surgeon swapping and changes in procedure category as variables of turnover time. Similar analyses were performed using individual surgical services, hourly time intervals during the surgical day, and turnover frequency per OR as additional covariates to surgeon swapping. MAIN RESULTS The mean (95% confidence interval [CI]) same-surgeon turnover time was 43.6 (43.2 - 44.0) minutes versus 51.0 (50.5 - 51.6) minutes for a planned surgeon swap (P < 0.0001). This resulted in a difference (95% CI) of 7.4 (6.8 - 8.1) minutes. The exact increase in turnover time was dependent on surgical service, change in subsequent procedure type, time of day when the turnover occurred, and turnover frequency. CONCLUSIONS The investigated institution averages 2.5 cases per OR per day. The cumulative additional turnover time (far less than one hour per OR per day) for switching surgeons definitely does not allow the addition of another elective procedure if the difference could be eliminated. A flexible scheduling policy allowing surgeon swapping rather than requiring full blocks incurs minimal additional staffed time during the OR day while allowing the schedule to be filled with available elective cases.

Systematic Or Block Allocation at a Large Academic Medical Center: Comprehensive Review on a Data-driven Surgical Scheduling Strategy.

Objective: To alleviate the surgical patient flow congestion in the perioperative environment without additional resources. Background: Massachusetts General Hospital experienced increasing overcrowding of the perioperative environment in 2008. The Post-Anesthesia Care Unit would often be at capacity, forcing patients to wait in the operating room. The cause of congestion was traced back to significant variability in the surgical inpatient-bed occupancy across the days of the week due to elective surgery scheduling practices. Methods: We constructed an optimization model to find a rearrangement of the elective block schedule to smooth the average inpatient census by reducing the maximum average occupancy throughout the week. The model was revised iteratively as it was used in the organizational change process that led to an implementable schedule. Results: Approximately 21% of the blocks were rearranged. The setting of study is very dynamic. We constructed a hypothetical scenario to analyze the patient population most representative of the circumstances under which the model was built. For this group, the patient volume remained constant, the average census peak decreased by 3.2% (P < 0.05), and the average weekday census decreased by 2.8% (P < 0.001). When considering all patients, the volume increased by 9%, the census peak increased 1.6% (P < 0.05), and the average weekday census increased by 2% (P < 0.001). Conclusions: This work describes the successful implementation of a data-driven scheduling strategy that increased the effective capacity of the surgical units. The use of the model as an instrument for change and strong managerial leadership was paramount to implement and sustain the new scheduling practices.