Institutional Surgical Response and Associated Volume Trends Throughout the COVID-19 Pandemic and Postvaccination Recovery Period.

Importance: The COVID-19 pandemic is associated with decreased surgical procedure volumes, but existing studies have not investigated this association beyond the end of 2020, analyzed changes during the post-vaccine release period, or quantified these changes by patient acuity. Objective: To quantify changes in the volume of surgical procedures at a 1017-bed academic quaternary care center from January 6, 2019, to December 31, 2021. Design, Setting, and Participants: In this cohort study, 129 596 surgical procedure volumes were retrospectively analyzed during 4 periods: pre-COVID-19 (January 6, 2019, to January 4, 2020), COVID-19 peak (March 15, 2020, to May 2, 2020), post-COVID-19 peak (May 3, 2020, to January 2, 2021), and post-vaccine release (January 3, 2021, to December 31, 2021). Surgery volumes were analyzed by subspecialty and case class (elective, emergent, nonurgent, urgent). Statistical analysis was by autoregressive integrated moving average modeling. Main Outcomes and Measures: The primary outcome of this study was the change in weekly surgical procedure volume across the 4 COVID-19 periods. Results: A total of 129 596 records of surgical procedures were reviewed. During the COVID-19 peak, overall weekly surgical procedure volumes (mean [SD] procedures per week, 406.00 [171.45]; 95% CI, 234.56-577.46) declined 44.6% from pre-COVID-19 levels (mean [SD] procedures per week, 732.37 [12.70]; 95% CI, 719.67-745.08; P < .001). This weekly volume decrease occurred across all surgical subspecialties. During the post-COVID peak period, overall weekly surgical volumes (mean [SD] procedures per week, 624.31 [142.45]; 95% CI, 481.85-766.76) recovered to only 85.8% of pre-COVID peak volumes (P < .001). This insufficient recovery was inconsistent across subspecialties and case classes. During the post-vaccine release period, although some subspecialties experienced recovery to pre-COVID-19 volumes, others continued to experience declines. Conclusions and Relevance: This quaternary care institution effectively responded to the pressures of the COVID-19 pandemic by substantially decreasing surgical procedure volumes during the peak of the pandemic. However, overall surgical procedure volumes did not fully recover to pre-COVID-19 levels well into 2021, with inconsistent recovery rates across subspecialties and case classes. These declines suggest that delays in surgical procedures may result in potentially higher morbidity rates in the future. The differential recovery rates across subspecialties may inform institutional focus for future operational recovery.

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.

Improving Transfusion Safety in the Operating Room With a Barcode Scanning System Designed Specifically for the Surgical Environment and Existing Electronic Medical Record Systems: An Interrupted Time Series Analysis.

BACKGROUND: Manual processes for verifying patient identification before blood transfusion and documenting this pretransfusion safety check are prone to errors, and compliance with manual systems is especially poor in urgent operating room settings. An automated, electronic barcode scanner system would be expected to improve pretransfusion verification and documentation. METHODS: Audits were conducted of blood transfusion documentation under a manual paper system from January to October 2014. An electronic barcode scanning system was developed to streamline transfusion safety checking and automate documentation. This system was implemented in 58 operating rooms between October and December 2014, with follow-up compliance audits through December 2015. The association of barcode scanner implementation with transfusion documentation compliance was assessed using an interrupted time series analysis. Anesthesia providers were surveyed regarding their opinions on the electronic system. In mid-2016, the scanning system was modified to transfer from the Metavision medical record system to Epic OpTime. Follow-up analysis assessed performance of this system within Epic during 2017. RESULTS: In an interrupted time series analysis, the proportion of units with compliant documentation was estimated to be 19.6% (95% confidence interval [CI], 10.7-25.6) the week before scanner implementation, and 74.4% (95% CI, 59.4-87.4) the week after implementation. There was a significant postintervention level change (odds ratio 10.80, 95% CI, 6.31-18.70; P < .001) and increase in slope (odds ratio 1.14 per 1-week increase, 95% CI, 1.11-1.17; P < .001). After implementation, providers chose to use the new electronic system for 98% of transfusions. Across the 2 years analyzed (15,997 transfusions), the electronic system detected 45 potential transfusion errors in 27 unique patients, and averted transfusion of 36 mismatched blood products into 20 unique patients. A total of 69%, 86%, and 88% of providers reported the electronic system improved patient safety, blood transfusion workflow, and transfusion documentation, respectively. When providers used the barcode scanner, no transfusion errors or reactions were reported. The scanner system was successfully transferred from Metavision to Epic without retraining staff or changing workflows. CONCLUSIONS: A barcode-based system designed for easy integration to different commonly used anesthesia information management systems was implemented in a large urban academic hospital. The system allows a single user with the assistance of a software system to perform and document pretransfusion safety verification. The system improved transfusion documentation compliance, averted potential transfusion errors, and became the preferred method of blood transfusion safety checking.

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.

Improvements in Ureteroscopy Efficiency When Performed at an Ambulatory Surgery Center.

INTRODUCTION: We compared the perioperative efficiency and outcomes of ureteroscopy performed at an ambulatory surgery center versus a tertiary care academic medical center. METHODS: With institutional review board approval de-identified records were obtained for ureteroscopies performed by a single surgeon from April 2016 to June 2017 at an ambulatory surgery center and tertiary care academic medical center for patients who were American Society of Anesthesiologists® class 1 or 2. Controlling for patient, stone and case order characteristics, multiple linear regressions were used to evaluate differences in total, preoperative, operative, postoperative, delay and operating room turnaround times between the 2 facility types. Emergency department visits within 30 days were also assessed. RESULTS: All mean times were shorter at the ambulatory surgery center compared to the tertiary care academic medical center, including operative time (25 vs 36 minutes, p <0.001), postoperative time (42 vs 103 minutes, p <0.001) and operating room turnaround time (17 vs 58 minutes, p <0.001). On average, patients spent 147 fewer minutes in facility (p <0.001). On multiple linear regression adjusting for covariates significant on univariate analysis, all times were significantly shorter at the ambulatory surgery center than at the tertiary care academic medical center. There was no difference in 30-day emergency department visits (p=0.818). CONCLUSIONS: For the same procedure by the same surgeon, patients spent on average 2.5 hours less in facility if the procedure was performed at an ambulatory surgery center compared to an academic medical center. This difference was driven primarily by perioperative care.

Optimizing Endoscope Reprocessing Resources Via Process Flow Queuing Analysis.

The Massachusetts General Hospital (MGH) is merging its older endoscope processing facilities into a single new facility that will enable high-level disinfection of endoscopes for both the ORs and Endoscopy Suite, leveraging economies of scale for improved patient care and optimal use of resources. Finalized resource planning was necessary for the merging of facilities to optimize staffing and make final equipment selections to support the nearly 33,000 annual endoscopy cases. To accomplish this, we employed operations management methodologies, analyzing the physical process flow of scopes throughout the existing Endoscopy Suite and ORs and mapping the future state capacity of the new reprocessing facility. Further, our analysis required the incorporation of historical case and reprocessing volumes in a multi-server queuing model to identify any potential wait times as a result of the new reprocessing cycle. We also performed sensitivity analysis to understand the impact of future case volume growth. We found that our future-state reprocessing facility, given planned capital expenditures for automated endoscope reprocessors (AERs) and pre-processing sinks, could easily accommodate current scope volume well within the necessary pre-cleaning-to-sink reprocessing time limit recommended by manufacturers. Further, in its current planned state, our model suggested that the future endoscope reprocessing suite at MGH could support an increase in volume of at least 90% over the next several years. Our work suggests that with simple mathematical analysis of historic case data, significant changes to a complex perioperative environment can be made with ease while keeping patient safety as the top priority.

Reduction in Operating Room Plasma Waste After Evidence-Based Quality Improvement Initiative.

Anesthesiologists request units of plasma in anticipation of transfusion. The amount of plasma transfused intraoperatively is less than that issued (requested, thawed, and sent). We presented institutional-specific data on plasma usage including anesthesiologist-specific ratios of plasma issued-to-transfused. In month-to-month comparisons from the year before the presentation (June-December 2015) to 7 months after (June-December 2016), plasma issued to the operating room was reduced from 434.9 ± 81 to 327.3 ± 65 units, a change of 107.6 units per month (95% confidence interval [CI], 22-193); plasma discarded by the blood bank was reduced from 109.7 ± 48 units to 69.1 ± 9 units, a change of 40.6 units per month (95% CI, 0.2-81); and plasma transfused went from 188.4 ± 42 units to 160.7 ± 52 units, a nonsignificant change of 27.7 units per month (95% CI, -27 to 83).

Fresh-Frozen Plasma: Ordering Patterns and Utilization in the Operating Rooms of a Tertiary Referral Hospital.

BACKGROUND: Blood product transfusion is the most commonly performed hospital procedure. Intraoperative blood product utilization varies between institutions and anesthesiologists. In the United States in 2011, nearly 4 million plasma units were transfused. METHODS: A retrospective analysis of intraoperative plasma ordering patterns and utilization (thawing and transfusing) was performed at a tertiary, academic hospital between January 2015 and March 2016. RESULTS: Over 15 months, 46,002 operative procedures were performed. In 1540 of them, plasma was thawed or transfused: 8297 plasma units were thawed and 3306 of those units were transfused. These 3306 plasma units were transfused in 749 cases with a median of 2 plasma units (interquartile range, 2-4) transfused. The percentage of average monthly procedures with plasma thawed and none transfused was 51.3% (confidence interval, 49.0%-53.6%). The cardiac surgery service requested the greatest number of plasma units to be thawed (2143) but only transfused 712 (33.2%) of them. Of all plasma units not transfused, 45% were generated by procedures with 1 to 4 units of plasma thawed; 95.7% of these units were thawed as even integers (ie, 2, 4). CONCLUSIONS: For operative procedures, far more plasma was thawed than was transfused and this practice occurred across surgical specialties and anesthesiologists. Considering the plasma that was not transfused, 45% occurred in procedures with 4 or fewer units of plasma requested suggesting these low-volume requests were a primary source of potential waste. Further studies are needed to examine associations between plasma utilization and clinical outcomes.

Communication Patterns in the Perioperative Environment During Epic Electronic Health Record System Implementation.

In April 2016, Massachusetts General Hospital (MGH) went live with the Epic electronic health records (EHR) system, replacing a variety of EHRs that previously existed in different departments throughout the hospital. At the time of implementation, the Vocera® Badge Communication System, a wireless hands-free communication device distributed to perioperative team members, had increased perioperative communication flow and efficiency. As a quality improvement effort to better understand communication patterns during an EHR go-live, we monitored our Vocera call volume and user volume before, during and after our go-live. We noticed that call volume and user volume significantly increased during our immediate go-live period and quickly returned to baseline levels. We also noticed that call volume increased during periods of unplanned EHR downtime long after our immediate go-live period. When planning the implementation of a new EHR, leadership must plan for and support this critical communication need at the time of the go-live and must also be aware of these needs during unplanned downtime.

Implementation of the Vocera Communication System in a Quaternary Perioperative Environment.

In the hospital, fast and efficient communication among clinicians and other employees is paramount to ensure optimal patient care, workflow efficiency, patient safety and patient comfort. The implementation of the wireless Vocera® Badge, a hands-free wearable device distributed to perioperative team members, has increased communication efficiency across the perioperative environment at Massachusetts General Hospital (MGH). This quality improvement project, based upon identical pre- and post-implementation surveys, used qualitative and quantitative analysis to determine if and how the Vocera system affected the timeliness of information flow, ease of communication, and operating room noise levels throughout the perioperative environment. Overall, the system increased the speed of information flow and eased communication between coworkers yet was perceived to have raised the overall noise level in and around the operating rooms (ORs). The perceived increase in noise was outweighed by the closed-loop communication between clinicians. Further education of the system's features in regard to speech recognition and privacy along with expected conversation protocol are necessary to ensure hassle-free communication for all staff.

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.

Reducing door-to-puncture times for intra-arterial stroke therapy: a pilot quality improvement project.

BACKGROUND: Delays to intra-arterial therapy (IAT) lead to worse outcomes in stroke patients with proximal occlusions. Little is known regarding the magnitude of, and reasons for, these delays. In a pilot quality improvement (QI) project, we sought to examine and improve our door-puncture times. METHODS AND RESULTS: For anterior-circulation stroke patients who underwent IAT, we retrospectively calculated in-hospital time delays associated with various phases from patient arrival to groin puncture. We formulated and then implemented a process change targeted to the phase with the greatest delay. We examined the impact on time to treatment by comparing the pre- and post-QI cohorts. One hundred forty-six patients (93 pre- vs. 51 post-QI) were analyzed. In the pre-QI cohort (ie, sequential process), the greatest delay occurred from imaging to the neurointerventional (NI) suite ("picture-suite": median, 62 minutes; interquartile range [IQR], 40 to 82). A QI measure was instituted so that the NI team and anesthesiologist were assembled and the suite set up in parallel with completion of imaging and decision making. The post-QI (ie, parallel process) median picture-to-suite time was 29 minutes (IQR, 21 to 41; P<0.0001). There was a 36-minute reduction in median door-to-puncture time (143 vs. 107 minutes; P<0.0001). Parallel workflow and presentation during work hours were independent predictors of shorter door-puncture times. CONCLUSIONS: In-hospital delays are a major obstacle to timely IAT. A simple approach for achieving substantial time savings is to mobilize the NI and anesthesia teams during patient evaluation and treatment decision making. This parallel workflow resulted in a >30-minute (25%) reduction in median door-to-puncture times.

Medication safety in the perioperative setting.

Drug administration errors are a major cause of morbidity and mortality in hospitalized patients. These errors result in major harm and incur dramatic costs to the delivery of health care. This article highlights this problem, especially as it deals with patients in the perioperative setting.

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.