Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Using Real-Time Locating Systems to Optimize Endoscope Use at a Large Academic Medical Center.

Massachusetts General Hospital (MGH) manages a large inventory of surgical equipment which must be delivered to operating rooms on-time, efficiently, and according to a set of quality standards and regulatory guidelines. In recent years, flexible scope management has become a topic of interest for many hospitals, as they face pressure to reduce costs, prevent infections that can result from mismanagement, and are under increased regulatory oversight. This work conducted at MGH proposes a novel method for surgical equipment management in a hospital. The proposed solution uses a real-time locating system to track flexible scopes, a semantic reasoning engine to determine the state of each scope, and a user interface to inform staff about necessary interventions to avoid scope expirations while maximizing efficiency. This study aimed to accomplish three primary goals. First, the study sought to improve the hospital's compliance to quality standards in order to reduce risks of infection due to expired scopes. Second, the study aimed to improve the cost-efficiency of scope disinfecting processes through more efficient inventory management. Finally, the study served as an opportunity for the hospital to establish best practices for working with the newly installed real-time locating system. The system proposed in this work was implemented at MGH on a subset of the hospital's flexible scopes. The study results demonstrated a quality compliance increase from 88.9% to 94.5%. The study also showed an estimated $17,350 annual cost savings due to more efficient scope management. Finally, the study demonstrated the feasibility, increase in regulatory compliance, and cost savings that would make this technology valuable when scaled across the hospital to other types of scopes and medical devices.

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.

Pooled Open Blocks Shorten Wait Times for Nonelective Surgical Cases.

OBJECTIVE: Assess the impact of the implementation of a data-driven scheduling strategy that aimed to improve the access to care of nonelective surgical patients at Massachusetts General Hospital (MGH). BACKGROUND: Between July 2009 and June 2010, MGH experienced increasing throughput challenges in its perioperative environment: approximately 30% of the nonelective patients were waiting more than the prescribed amount of time to get to surgery, hampering access to care and aggravating the lack of inpatient beds. METHODS: This work describes the design and implementation of an "open block" strategy: operating room (OR) blocks were reserved for nonelective patients during regular working hours (prime time) and their management centralized. Discrete event simulation showed that 5 rooms would decrease the percentage of delayed patients from 30% to 2%, assuming that OR availability was the only reason for preoperative delay. RESULTS: Implementation began in January 2012. We compare metrics for June through December of 2012 against the same months of 2011. The average preoperative wait time of all nonelective surgical patients decreased by 25.5% (P < 0.001), even with a volume increase of 9%. The number of bed-days occupied by nonurgent patients before surgery declined by 13.3% whereas the volume increased by 4.5%. CONCLUSIONS: The large-scale application of an open-block strategy significantly improved the flow of nonelective patients at MGH when OR availability was a major reason for delay. Rigorous metrics were developed to evaluate its performance. Strong managerial leadership was crucial to enact the new practices and turn them into organizational change.

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.

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.