Impact of a Repatriation Program Between Quaternary and Community Hospitals.

BACKGROUND: Capacity challenges at quaternary hospitals cause delays or denials in patient transfers from community hospitals that can compromise quality and safety. Repatriation is an innovative approach to increase capacity at the quaternary hospital by transferring a patient back to their originating community hospital after the quaternary portion of their care is completed. METHODS: A repatriation program was implemented at a large quaternary care teaching hospital over a one-year period (2020 to 2021). The authors characterized the rate of successful repatriation and associated patient characteristics, determined the impact on quaternary hospital capacity in terms of bed days saved, and estimated the resultant number of backfilled admissions that could be accommodated. The research team also monitored the rate of readmissions for repatriations back to the quaternary hospital. RESULTS: Overall, 215 repatriations were attempted, and 103 (47.5%) were successful. The most common diagnoses were sepsis (13, 12.6%), stroke (12, 11.7%), intracranial bleed (10, 9.7%), gastrointestinal perforation/obstruction (9, 8.7%), and trauma (9, 8.7%). The median length of stay at the quaternary hospital was 13 days (interquartile range [IQR] 7-20) and 12 days (IQR 4-26) at the community hospital. There were 2,842 bed days saved at the quaternary hospital, with a backfill opportunity of 431 admissions. The readmission rate to the quaternary hospital was 1.9%. CONCLUSION: By dynamically matching patient need with hospital capability at different phases of the patient's care, Repatriation can save bed days at the quaternary hospital, creating capacity to improve access for patients needing timely transfer. The low observed readmission rate suggests that repatriation is safe.

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.

Level-loading a health system by transferring emergency department patients to a community hospital: Prospective cohort study.

BACKGROUND: Emergency department boarding and crowding lead to worse patient outcomes and patient satisfaction. OBJECTIVE: We describe the implementation of a program to transfer patients requiring medical admission from an academic emergency department to a community hospital's medical floor and analyze its effects on patient outcomes. METHODS: A prospective cohort study was performed. Data was collected on patient flow through the transfer program. Patient characteristics, boarding time in the emergency department, and hospital-based outcome measures were compared between patients in the transfer program who were successfully transferred to the community hospital and patients who were admitted to the academic medical center. RESULTS: 79 patients were successfully transferred to the community hospital between November 23, 2020 and August 5, 2021, resulting in 279 bed days in the community hospital. Successfully transferred patients experienced a statistically shorter ED boarding time (5.7 vs. 10.9 h, p < 0.0001), ED length of stay (10.5 vs 16.1 h, p < 0.0001), and hospital length of stay (3.5 vs 5.7 days, p < 0.0001) compared to patients initially referred to the transfer program who were admitted to the academic medical center. There were no reported adverse events during transfer, upgrades to the ICU within 24 h of admission, or inpatient deaths for patients who were transferred. CONCLUSION: We implemented an academic emergency department to partner community hospital transfer program that safely level-loads medical patients in a healthcare system.

The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals.

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.

COVID-19 Level-Loading: Transferring Emergency Department Patients to a Partner Academic Medical Center Within a Healthcare System.

COVID-19 continues to challenge bed capacity and the ability of hospitals to provide quality care for patients around the country. However, the COVID-19 pandemic at a given point in time does not impact all hospitals equally-even within a single healthcare system, one hospital may be caring for patients in the hallways, while another has available inpatient beds. Here, we demonstrate a program to level-load COVID-19 patients between 2 academic medical centers in a healthcare system by transferring patients at the time of admission from the emergency department of one institution directly to an inpatient bed of the other institution. Over 42 days, 50 patients were transferred which saved 432 bed-days at the home academic medical center without any adverse events during transfer or upgrades to the ICU within the first 24 hours of admission. Programs like this can expand a healthcare system's ability to allocate personnel and resources efficiently for patients and maximize the quality of care delivered even during a pandemic.

An algorithm for the sedation of patients with obstructive sleep apnea by non-anesthesiologists.

OBJECTIVE: Patients with obstructive sleep apnea (OSA) are at risk for adverse events when moderate sedation is administered by nurse protocols (NAMS) under the guidance of non-anesthesiologists. An algorithm was applied for the appropriate section of patients to receive NAMS and the application of continuous positive airway pressure (CPAP). METHODS: An algorithm was developed for patients with OSA who were scheduled for gastroenterology, radiology, and cardiology procedures using NAMS. Those with normal airways and without contraindications for NAMS were classified as CPAP-independent (CPAP-I; not routinely used) or CPAP-dependent (CPAP-D; always used). CPAP machines were brought in by CPAP-D patients or supplied by the hospital and set at a patient's routine setting or 10 cm H2O if not known. CPAP-D patients for procedures for which CPAP could not be applied were done under anesthesia care. We retrospectively examined this program for the 2008-2018 period. RESULTS: Since the inception of this protocol in 2008, 803 patients with OSA safely underwent procedures using either personal CPAP or CPAP provided by the hospital. CONCLUSIONS: Patients with OSA can safely have NAMS for procedures when CPAP is applied based on a protocol that considers airway evaluation, the procedure, and whether there is dependence upon CPAP.

Design and Performance of a COVID-19 Hospital Recovery Model.

To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background: During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods: At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results: There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions: The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return.

Direct admission to improve timely access to care for patients requiring transfer to a level 1 trauma center.

BACKGROUND: Emergency departments (EDs) at level 1 trauma centers are often overcrowded and deny ED-to-ED transfers from lower-tiered centers. Lack of access to timely level 1 care is associated with increased mortality. We evaluated the feasibility of a direct admission (DA) protocol as a method to increase timely access to a level 1 trauma center during periods of ED overcrowding. METHODS: During periods of ED overcrowding between 1 May and 31 December 2019, we admitted patients from referring EDs directly to the intensive care unit (ICU) or inpatient ward using the DA protocol. In a prospective comparative study design, we compared their outcomes to patients during the same period who were admitted through the ED when the ED was not overcrowded. RESULTS: During periods of ED overcrowding, transfer was requested and clinically accepted for 28 patients, of which 23 (82.1%, age 63±20.3 years, men 52.2% men) were successfully admitted via the DA protocol. Five (17.9%) were not successfully transferred due to lack of available inpatient beds. During periods when the ED was not overcrowded, 106 patients (age 62.8±23.1 years, men 52.8%) were admitted via the ED. There were no morbidity or mortality events attributed to the DA process. Time to patient arrival was 2.7 hours (95% CI 2.3 to 3.1) in the DA cohort and 1.9 hours (95% CI 1.5 to 2.4) in the ED-to-ED cohort (p=0.104). Up-triage to the ICU within 24 hours was performed in only one patient (4.3%). In-hospital mortality did not differ (3 (13%) vs. 8 (7.6%), p=0.392). DISCUSSION: The DA pathway is a feasible method to safely transfer patients from a referring ED to a higher-care trauma center when its ED is overcrowded. LEVEL OF EVIDENCE: Level III, care management.

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.

Optimizing Operating Room Scheduling.

This article reviews the management of an operating room (OR) schedule and use of the schedule to add value to an organization. We review the methodology of an OR block schedule, daily OR schedule management, and post anesthesia care unit patient flow. We discuss the importance of a well-managed OR schedule to ensure smooth patient care, not only in the OR, but throughout the entire hospital.

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.

Modeling the impact of changing patient transportation systems on peri-operative process performance in a large hospital: insights from a computer simulation study.

Transportation of patients is a key hospital operational activity. During a large construction project, our patient admission and prep area will relocate from immediately adjacent to the operating room suite to another floor of a different building. Transportation will require extra distance and elevator trips to deliver patients and recycle transporters (specifically: personnel who transport patients). Management intuition suggested that starting all 52 first cases simultaneously would require many of the 18 available elevators. To test this, we developed a data-driven simulation tool to allow decision makers to simultaneously address planning and evaluation questions about patient transportation. We coded a stochastic simulation tool for a generalized model treating all factors contributing to the process as JAVA objects. The model includes elevator steps, explicitly accounting for transporter speed and distance to be covered. We used the model for sensitivity analyses of the number of dedicated elevators, dedicated transporters, transporter speed and the planned process start time on lateness of OR starts and the number of cases with serious delays (i.e., more than 15 min). Allocating two of the 18 elevators and 7 transporters reduced lateness and the number of cases with serious delays. Additional elevators and/or transporters yielded little additional benefit. If the admission process produced ready-for-transport patients 20 min earlier, almost all delays would be eliminated. Modeling results contradicted clinical managers' intuition that starting all first cases on time requires many dedicated elevators. This is explained by the principle of decreasing marginal returns for increasing capacity when there are other limiting constraints in the system.

Multidisciplinary team streamlines hospital schedules, patient care.

Massachusetts General Hospital staff members use data to reassess patient flow, optimize facilities and enhance patient experience.

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.

Surgeon profiling: a key to optimum operating room use.

HYPOTHESIS: A high-efficiency Pod, composed of 3 parallel-processing operating rooms (ORs) and a dedicated 3-bed miniature postanesthesia care unit, can be filled with surgeons capable of converting time saved from parallel processing into incremental volume. DESIGN: Statistical and mathematical modeling. SETTING: Academic medical center with 52 serial-processing ORs, 1 parallel-processing OR, and a congested postanesthesia care unit. PARTICIPANTS: Elective surgical cases (N = 58 356) performed by a single surgical service without a preoperative intensive care unit bed request from April 1, 2004, through March 31, 2006. INTERVENTIONS: Results from our parallel-processing OR (n = 1729) were extrapolated to all other cases (n = 56 627) to estimate the duration of key process time intervals as if they were performed using parallel processing. Cases that could yield incremental throughput using parallel processing were labeled "good." Total good case hours per week were then aggregated for each surgeon. Main Outcomes Measures Surgeons with 4.5 hours per week or more of good case time had a "profile" suitable for a 9-hour block in The Pod every 2 weeks. RESULTS: Of the 352 profiled surgeons, 30 had 4.5 hours per week or more of good case time, more than filling the 15 blocks per week. CONCLUSIONS: The high-efficiency OR Pod can fill each of its 3 ORs with case/surgeon combinations that should yield additional throughput. Surgeon profiles based on stringent efficiency targets maximize the throughput potential of The Pod's active ORs and more than compensate for the OR turned miniature postanesthesia care unit.

Acute adrenal insufficiency after large-dose glucocorticoids for spinal cord injury.

A 24- to 48-h course of large-dose glucocorticoid therapy is often used in the acute management of spinal cord injury. We describe a patient who developed adrenal insufficiency (AI) after this protocol. Although a definitive causal relationship between the steroids and AI was not established, their temporal association and the exclusion of other possible etiologies led us to postulate that AI was a complication of the steroid protocol. Clinicians should, therefore, consider AI in patients with spinal cord injury receiving glucocorticoids, a population in whom it may otherwise go undiagnosed and untreated.

Thoracoabdominal aneurysm repair: results with 337 operations performed over a 15-year interval.

OBJECTIVE: To review perioperative results and late survival after thoracoabdominal aneurysm repair (TAA), in particular to assess the impact over time of epidural cooling (EC) on spinal cord ischemic complications (SCI). SUMMARY BACKGROUND DATA: A variety of operative approaches and protective adjuncts have been used in TAA to minimize the major complications of perioperative death and SCI. There is no consensus with respect to the optimal approach. METHODS: From January 1987 to November 2001, 337 consecutive TAA repairs were performed by a single surgeon. Clinical features included prior aortic grafts in 97 (28.8%) and emergent operation in 82 (24.6%), including rupture in 46 (13.6%) and dissection in 63 (19%). Operative management consisted of a clamp/sew technique with adjuncts in 93%. EC (since July 1993) to prevent SCI was used in 194 (57.6%) repairs. Variables associated with the end points of operative mortality and postoperative SCI were assessed with the Fisher exact test and logistic regression; late survival was estimated with the Kaplan-Meier method. RESULTS: Operative mortality was 8.3% and was associated with nonelective operation, intraoperative hypotension, total transfusion requirement, and the postoperative complications of paraplegia, renal failure, and pulmonary insufficiency. Postoperative renal failure and transfusion requirement were independent correlates of mortality. SCI of any severity occurred in 38 of 334 (11.4%) operative survivors, with 22/38 (6.6% of cohort) sustaining total paraplegia. EC reduced the risk of SCI in patients with types I-III TAA (10.6% vs. 19.8%, =.04). Independent correlates of SCI over the entire study interval included types I/II TAA, rupture, cross-clamp duration, sacrifice of T9-L1 intercostal vessels, and intraoperative hypotension. Late survival rates at 2 and 5 years were 81.2 +/- 3% and 67.2 +/- 5%. CONCLUSIONS: EC has decreased the risk of SCI after TAA repair. Decreasing the substantial proportion (nearly 25%) of patients requiring nonelective operation will improve results. Late survival is equal to that after routine AAA repair, indicating that the considerable resource expenditure required for TAA repair is worthwhile.