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.

Low-Volume Bowel Preparation Is Associated With Reduced Time to Colonoscopy in Hospitalized Patients: A Propensity-Matched Analysis.

INTRODUCTION: Delays in inpatient colonoscopy are commonly caused by inadequate bowel preparation and result in increased hospital length of stay (LOS) and healthcare costs. Low-volume bowel preparation (LV-BP; sodium sulfate, potassium sulfate, and magnesium sulfate ) has been shown to improve outpatient bowel preparation quality compared with standard high-volume bowel preparations (HV-BP; polyethylene glycol ). However, its efficacy in hospitalized patients has not been well-studied. We assessed the impact of LV-BP on time to colonoscopy, hospital LOS, and bowel preparation quality among inpatients. METHODS: We performed a propensity score-matched analysis of adult inpatients undergoing colonoscopy who received either LV-BP or HV-BP before colonoscopy at a quaternary academic medical center. Multivariate regression models with feature selection were developed to assess the association between LV-BP and study outcomes. RESULTS: Among 1,807 inpatients included in this study, 293 and 1,514 patients received LV-BP and HV-BP, respectively. Among the propensity score-matched population, LV-BP was associated with a shorter time to colonoscopy (ß: -0.43 [95% confidence interval: -0.56 to -0.30]) while having similar odds of adequate preparation (odds ratio: 1.02 [95% confidence interval: 0.71-1.46]; P = 0.92). LV-BP was also significantly associated with decreased hospital LOS among older patients (age ≥ 75 years), patients with chronic kidney disease, and patients who were hospitalized with gastrointestinal bleeding. DISCUSSION: LV-BP is associated with decreased time to colonoscopy in hospitalized patients. Older inpatients, inpatients with chronic kidney disease, and inpatients with gastrointestinal bleeding may particularly benefit from LV-BP. Prospective studies are needed to further establish the role of LV-BP for inpatient colonoscopies.

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.

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.

Achieving Surgical Supply Savings through Preference Card Standardization.

Among high volume procedures considerable variation exists in the average cost per case (ACPC) of surgical supplies used between surgeons. A contributing factor to these cost differences are divergences in surgeons' preference cards, which act as a guide to hospital staff for the supplies a surgeon requires to successfully perform a procedure. This article documents efforts and results of an initiative to standardize preference cards for Laparoscopic Cholecystectomies. Data collected for this project outlined differences between surgeon's preference card composition, utilization of selected supplies and associated procedure costs. Reports were developed that grouped surgical supplies based on United Nations Standard Products and Services Code (UNSPC) product classes and highlighted classes with the highest per case standard deviations. Based on these findings and feedback from clinical partners, a composite set of supplies for use across all preference cards was developed in conjunction with the Chief of General Surgery. The net result of moving to a standardized set of supplies was an estimated $21,650 in annual supply expenses associated with Laparoscopic Cholecystectomies. Results suggest that standard deviation-based reports organized by product class facilitate effective surgeon-to-surgeon comparisons and make apparent readily available supply substitutes that are less expensive.

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.

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

Importance: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. Objective: To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. Design, Setting, and Participants: This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded. Main Outcomes and Measures: The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days. Results: The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable. Conclusions and Relevance: This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.

Non-clinical delays in transfer out of the surgical ICU are associated with increased hospital length of stay and delayed progress of care.

PURPOSE: The impact of non-clinical transfer delay (TD) from the ICU to a general care unit on the progress of the patient's care is unknown. We measured the association between TD and: (1) the patient's subsequent hospital length of stay (LOS); (2) the timing of care decisions that would advance patient care. METHODS: This was a single center retrospective study in the United States of patients admitted to the surgical and neurosurgical ICUs during 2013 and 2015. The primary outcome was hospital LOS after transfer request. The secondary outcome was the timing of provider orders representing care decisions (milestones) that would advance the patient's care. Patient, surgery, and bed covariates were accounted for in a multivariate regression and propensity matching analysis. RESULTS: Out of the cohort of 4,926 patients, 1,717 met inclusion criteria. 670 (39%) experienced ≥12 hours of TD. For each day of TD, there was an average increase of 0.70 days in LOS (P < 0.001). The last milestone occurred on average 0.35 days later (P < 0.001). Propensity matching analyses were confirmatory (P < 0.001, P < 0.001). CONCLUSIONS: TD is associated with longer LOS and delays in milestone clinical decisions that progress care. Eliminating delays in milestones could mitigate TD's impact on LOS.

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.

Financial and operational impact of a direct-from-PACU discharge pathway for laparoscopic cholecystectomy patients.

BACKGROUND: We assessed the operational and financial impact of discharging laparoscopic cholecystectomy (LC) patients directly from the postanesthetic care unit (PACU) in comparison with post-transfer discharge from a hospital bed in a busy academic hospital. METHODS: We retrospectively compared 6 months of performance (bed utilization; recovery room and hospital length of stay; complications; readmissions; hospital costs, revenue, and margin) after implementation of PACU discharges (case patients) to the corresponding 6 months in the prior year (control patients). RESULTS: After implementation, 66% of LC case patients were discharged on the day of surgery, compared with 29% in the control group (P < .05). Eighty percent of the day-of-surgery discharges were directly from the PACU. Shifting to PACU discharge saved 1 in-hospital bed transfer and 1 bed-day for each PACU discharge. Recovery room length of stay for PACU discharge patients was 26% longer than for hospital discharge patients (P = NS). Average hospital length of stay for all patients discharged on the day of surgery was 3.2 hours shorter (P < .05) for case patients (80% PACU discharge) than for control patients. There were no readmissions in the PACU discharge group and no difference in complications. While costs, revenue, and net margin for PACU discharge patients were reduced by 40% to 50% (P < .02) relative to floor discharge patients, the hospital's net margin for the combined case patient group was preserved relative to the control group. CONCLUSIONS: PACU discharge of LC patients significantly reduces bed utilization, decreases in-hospital transfers, and allows congested hospitals to better accommodate patient care needs and generate additional revenue.

Reorganizing patient care and workflow in the operating room: a cost-effectiveness study.

BACKGROUND: Many surgeons believe that long turnover times between cases are a major impediment to their productivity. We hypothesized that redesigning the operating room (OR) and perioperative-staffing system to take advantage of parallel processing would improve throughput and lower the cost of care. METHODS: A state of the art high tech OR suite equipped with augmented data collection systems served as a living laboratory to evaluate both new devices and perioperative systems of care. The OR suite and all the experimental studies carried out in this setting were designated as the OR of the Future Project (ORF). Before constructing the ORF, modeling studies were conducted to inform the architectural and staffing design and estimate their benefit. In phase I a small prospective trial tested the main hypothesized benefits of the ORF: reduced patient intra-operative flow-time, wait-time and operative procedure time. In phase II a larger retrospective study was conducted to explore factors influencing these effects. A modified process costing method was used to estimate costs based on nationally derived data. Cost-effectiveness was evaluated using standard methods. RESULTS: There were 385 cases matched by surgeon and procedure type in the retrospective dataset (182 ORF, 193 standard operating room [SOR]). The median Wait Time (12.5 m ORF vs 23.8 m SOR), Operative Procedure Time (56.1 m ORF vs 70.5 m SOR), Emergence Time (10.9 m ORF vs 14.5 m SOR) and Total Patient OR Flowtime (79.5 m ORF vs 108.9 m SOR) were all shorter in the ORF (P < .05 for all comparisons). The median cost/patient was $3,165 in the ORF (interquartile range, $1,978 to $4,426) versus $2,645 in SORs (interquartile range, $1,823 to $3,908) (P = ns). The potential change in patient throughput for the ORF was 2 additional patients/day. This improved throughput was primarily attributable to a marked reduction in the non-operative time (ie, those activities commonly accounting for "turnover time") rather than facilitation of faster operations. The incremental cost-effectiveness ratio of ORF was $260 (interquartile range, $180 to $283). CONCLUSION: The redesigned perioperative system improves patient flow, allowing more patients to be treated per day. Cost-effectiveness analysis suggests that the additional costs incurred by higher staffing ratios in an ORF environment are likely to be offset by increases in productivity. The benefits of this system are realized when performing multiple, short-to-medium duration procedures (eg, <120 m).

INCOMING!--A web tracking application for PACU and post-surgical patients.

BACKGROUND: Capacity constraints necessitate improving hospital efficiency. An integrated real time system facilitating patient flow between the post-anesthesia care unit (PACU) and surgical ward would ease PACU workload by reducing the effort of discharging patients. METHODS: We developed INCOMING!, a web-based platform that monitors patient progress from the operating room to the PACU. INCOMING! integrates available data, automatically determining when a patient enters the PACU. An automated paging system alerts clinical unit managers to 'pull' their patients from the PACU after a set recovery period. General surgery patients were included in the INCOMING! system in late 2004 with paging added in mid-March 2005. Mean PACU length of stay was calculated for the intervention group (general surgery patients with INCOMING!) and compared to a control group (general surgery patients without INCOMING!) and an orthopedic surgery group before and after paging. RESULTS: The system successfully gathers data and generates automated pages when events occur. After paging, there was a significant difference between the orthopedic surgery control group and the general surgery intervention group (235 min versus 185 min, P = 0.001). The mean PACU LOS decreased in the INCOMING! intervention group by 26 min while the mean LOS increased by 28 min in the general surgery control group (P = 0.27). CONCLUSION: Pilot implementation demonstrates that INCOMING! performs the desired integration and automatic notification. Given the minimal cost and potential large gains from a wider deployment, we plan to implement the system for all PACU patients and all post-PACU care units.

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. Real-time 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 "patient-in-wrong-OR'' events. The system detected all "wrong-operating 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.

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.