Telemedicine Use for Oncology Clinic Visits at a Large Medical Center During the Onset of COVID-19.

Introduction: The COVID-19 pandemic created an unprecedented surge toward telemedicine. This project investigated oncology clinic telemedicine utilization across the Vanderbilt University Medical Center (VUMC) from January to October 2020. Poorer prognosis and care of oncology patients is expected to be associated with increased emergency department (ED) visits. Methods: January to October 2020 clinic visits were identified from the VUMC's Electronic Data Warehouse (EDW). Oncology patients were identified by ICD-10 code and their EDW ED visit data were extracted. Joinpoint piecewise linear regression evaluated trends in tele-oncology visits. VUMC ED visits were compared for patients who did versus did not use telemedicine for oncology clinic visits. A Welch's two-tailed t-test investigated differences in ED visits/patient between these cohorts (α < 0.05). Results: A sharp increase in tele-oncology clinic visits from January to April 2020 (Monthly Percent Change = 396.26%) was followed by a steady decrease from April to October 2020 (Monthly Percent Change = -20.93%). The difference between these two trends was significant (p < 0.002). Of 18 cancer sites, breast cancers had the highest proportion (29.04%) of tele-oncology visits. There was no significant difference in January to October 2020 ED usage for oncology patients who did (0.40 ED visits/patient) versus did not (0.38 ED visits/patient) utilize telemedicine (p = 0.69). A total of 9.64% of oncology clinic visits from January to October 2020 were telemedicine visits, just below the 13.0% institutional average. Discussion: At the VUMC, tele-oncology spiked in March and April 2020 before decreasing from April to October 2020. Breast cancer clinics were most likely to use tele-oncology. Telemedicine use was not associated with increased ED visits for oncology patients, suggesting telemedicine as an alternative for routine oncology clinics. Oncology clinic telemedicine usage was 18th-highest among 33 specialties at our institutions, and among the lowest of nonsurgical specialties.

Automated Emails to Improve Evening Staffing for Anesthesiologists.

Scheduling flexibility and predictability to the end of a clinical workday are strategies aimed at addressing physician burnout. A voluntary relief shift was created to increase the pool of anesthesiologists providing end of the day relief. We hypothesized that an automated email reminder would improve the number of evening relief shifts filled and increase the number of anesthesiologists participating in the program. An automated email reminder was implemented, which selectively emailed anesthesiologists without a clinical assignment one day in advance when the voluntary relief shifts were not filled, and anticipated case volume past 4:00 PM was expected to exceed the capacity of the on-call team. After implementation of the automated email reminder, the median number of providers who worked the relief shift on a typical day was 2.6, compared to 1.75 prior to the intervention. After the initial increase in the number of volunteers post-intervention, the trend in the weekly average number of volunteers tended to decrease but remained higher than before the intervention. A total of 22 unique anesthesiologists chose to participate in this program after the intervention. An automated email reminder increased the number of anesthesiologists volunteering for a relief shift. Leveraging automation to match staffing needs with case volume allows for recruitment of additional personnel on the days when volunteers are most needed. Increasing the pool of anesthesiologists available to provide relief is one strategy to improve end of the day predictability and work-life balance.

Is newer always better?: comparing cost and short-term outcomes between laparoscopic and robotic right hemicolectomy.

BACKGROUND: Enthusiasm is high for expansion of robotic assisted surgery into right hemicolectomy. But data on outcomes and cost is lacking. Our objective was to determine the association between surgical approach and cost for minimally invasive right hemicolectomy. We hypothesized that a robot approach would have increased costs (both economic and opportunity) while achieving similar short-term outcomes. METHODS: We performed a retrospective cohort analysis with a simulation of operating room utilization at a quaternary care, academic institution. We enrolled patients undergoing minimally invasive right hemicolectomy from November 2017 to August 2019. Patients were categorized by the intended approach- laparoscopic or robotic. The primary outcome was the technical variable direct cost. Secondary outcomes included total cost, supply cost, operating room utilization, operative time, conversion, length of stay and 30-day post-operative outcomes. RESULTS: 79 patients were included in the study. A robotic approach was used in 22% of the cohort. The groups differed significantly only in etiology of surgery. Robotic surgery was associated with a 1.5 times increase in the technical variable direct cost (p < 0.001), increased supply cost (2.6 times; p < 0.001) and increased total cost (1.3 times; p < 0.001). Significant differences were observed in median room time (Robotic: 285 min vs. Laparoscopic: 170 min; p < 0.001) and procedure time (Robotic: 203 min vs. Laparoscopic: 118 min; p < 0.001). There were no differences observed in post-operative outcomes including length of stay or readmission. In a simulation of OR utilization, 45 laparoscopic right hemicolectomies could be performed in an OR in a month compared to 31 robotic cases. CONCLUSIONS: Robotic right hemicolectomy was associated with increased costs with no improvement in post-operative outcomes. In a simulation of operating room efficiency, a robotic approach was associated with 14 fewer cases per month. Practitioners and administrators should be aware of the increased cost of a robotic approach.

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who undergo coronary artery bypass grafting (CABG). MATERIALS AND METHODS: Using a single institution's Society of Thoracic Surgeons (STS) Registry data, 2121 patients with elective or urgent, isolated CABG were analyzed across 116 variables. Two machine learning techniques of random forest and artificial neural networks (ANNs) were used to search for the highest impact variables in predicting LOS, and results were compared against multiple linear regression. Out-of-sample validation of the models was performed on 105 patients. RESULTS: Of the 10 highest impact variables identified in predicting LOS, four of the most impactful variables were duration intubated, last preoperative creatinine, age, and number of intraoperative packed red blood cell transfusions. The best performing model was an ANN using the ten highest impact variables (testing sample mean absolute error (MAE) = 1.685 d, R2 = 0.232), which performed consistently in the out-of-sample validation (MAE = 1.612 d, R2 = 0.150). CONCLUSION: Using machine learning, this study identified several novel predictors of postsurgery LOS and reinforced certain known risk factors. Out of the entire STS database, only a few variables carry most of the predictive value for LOS in this population. With this knowledge, a simpler linear regression model has been shared and could be used elsewhere after further validation.

Transition from Private to Academic Anesthesia Provision Changes Process and Patient Centered Outcomes in an Ambulatory Surgery Center.

A recurring concern in the discussion of performance of anesthesia practices is that academic practitioners are slower, less efficient, or produce poorer operational outcomes than their private practice counterparts. A simple overnight 'swap' of a private anesthesia practice with an academic anesthesia practice took place in an outpatient surgery center where the case volume, case mix, surgeons, and staff remained the same. Operational and quality measures were analyzed for comparison between the practices over the span of two years. All patients who had a procedure at the outpatient surgery center in the year prior to the takeover and the year after were studied. Post-anesthesia care unit times, hospital transfer data, pain scores at discharge, opioids dispensed, and anesthesia control times were compared over two years. Charts were manually abstracted by non-clinical administrative staff who were unaware of the study hypothesis. Procedure data and clinical outcomes were compared between the two years using standard statistical techniques. After exchange to the academic group, the median (mean) pain score at post-anesthesia care unit (PACU) discharge was reduced from 2 (2.0) to 0 (1.7) (Wilcoxon rank sum test p < 0.001), and the odds of having moderate or severe pain was reduced by 32% (95% CI, 25, 39, p < 0.001) after adjusting for surgery type. The year-on-year average recovery room time was reduced by 13.9 min (95% CI, 12.5, 15.4, p < 0.001) after adjusting for surgery type. There was a significant reduction in hospital transfer rate after changing groups (0.45% vs. 0.07%, Pearson chi square test p = 0.005). Hospital transfer rates, dispensed opioids in PACU, pain scores at discharge, and PACU times were all improved after the conversion from a private practice to an academic one, without a compromise in efficiency or throughput.

A Statistical Model-driven Surgical Case Scheduling System Improves Multiple Measures of Operative Suite Efficiency: Findings From a Single-center, Randomized Controlled Trial.

OBJECTIVE: We sought to determine whether a data-driven scheduling approach improves Operative Suite (OS) efficiency. BACKGROUND: Although efficient use of the OS is a critical determinant of access to health care services, OS scheduling methodologies are simplistic and do not account for all the available characteristics of individual surgical cases. METHODS: We randomly scheduled cases in a single OS by predicting their length using either the historical mean (HM) duration of the most recent 4 years; or a regression modeling (RM) system that accounted for operative and patient characteristics. The primary endpoint was the imprecision in prediction of the end of the operative day. Secondary endpoints included measures of OS efficiency; personnel burnout captured by the Maslach Burnout Inventory; and a composite endpoint of 30-day mortality, myocardial infarction, wound infection, bleeding, amputation, or reoperation. RESULTS: Two hundred and seven operative days were allocated to scheduling with either the RM or the HM methodology. Mean imprecision in predicting the end of the operative day was higher with the HM approach (30.8 vs 7.2 minutes, P = 0.024). RM was associated with higher throughput (379 vs 356 cases scheduled over the course of the study, P = 0.04). The composite rate of adverse 30-day events was similar (2.2% vs 3.2%, P = 0.44). The mean depersonalization score was higher (3.2 vs 2.0, P = 0.044), and mean personal accomplishment score was lower during HM weeks (37.5 vs 40.5, P = 0.028). CONCLUSIONS: Compared to the HM scheduling approach, the proposed data-driven RM scheduling methodology improves multiple measures of OS efficiency and OS personnel satisfaction without adversely affecting clinical outcomes.

An enhanced recovery program in colorectal surgery is associated with decreased organ level rates of complications: a difference-in-differences analysis.

BACKGROUND: Perioperative care has lacked coordination and standardization. Enhanced recovery programs (ERPs) have been shown to decrease aggregate complications across surgical specialties. We hypothesize that the sustained implementation of an ERP will be associated with a decrease in a broad range of complications at the organ system level. STUDY DESIGN: Adult patients undergoing elective colorectal procedures between 1/2011 and 10/2016 were included. Patients were stratified based on exposure to a sustained ERP (7/2014-10/2016) after an 18-month wash-in period in a pre-post analysis. The primary outcome was 30-day complication rate by organ category as collected by National Surgical Quality Improvement Program (NSQIP) abstractors. Demographic and other patient level data were collected. Complication rates were compared using multivariable regression employing a differences-in-differences (DiD) approach using the national NSQIP PUF file to account for secular trends. RESULTS: A total of 1182 patients were included in this study, with 47% treated in an ERP. The two groups were similar in age, gender, race, BMI, comorbidity index, and procedure type. In a multivariable DiD analysis, significant reductions were seen in surgical site infection (OR 0.30; 95% CI 0.20-0.43), postoperative pulmonary complications (OR 0.46; 95% CI 0.24-0.90), transfusion (OR 0.27; 95% CI 0.15-0.51), urinary tract infections (OR 0.34; 95% CI 0.18-0.66), sepsis (OR 0.35; 95% CI 0.20-0.61), and cardiac complications (OR 0.10; 95% CI 0.01-0.84). A reduction in return to the operating room and 30-day readmission was also observed. Median length of stay (LOS) decreased from 5.2 to 3.5 days (p < 0.001). No significant changes occurred for acute kidney injury and hematologic complications. CONCLUSION: An ERP was associated with reduced complication rates across a wide range of organ categories and > 1.5-day reduction in LOS in a colorectal surgery population.

A Novel Method of Evaluating Key Factors for Success in a Multifaceted Critical Care Fellowship Using Data Envelopment Analysis.

BACKGROUND: The current system of summative multi-rater evaluations and standardized tests to determine readiness to graduate from critical care fellowships has limitations. We sought to pilot the use of data envelopment analysis (DEA) to assess what aspects of the fellowship program contribute the most to an individual fellow's success. DEA is a nonparametric, operations research technique that uses linear programming to determine the technical efficiency of an entity based on its relative usage of resources in producing the outcome. DESIGN: Retrospective cohort study. SUBJECTS AND SETTING: Critical care fellows (n = 15) in an Accreditation Council for Graduate Medical Education (ACGME) accredited fellowship at a major academic medical center in the United States. METHODS: After obtaining institutional review board approval for this retrospective study, we analyzed the data of 15 anesthesiology critical care fellows from academic years 2013-2015. The input-oriented DEA model develops a composite score for each fellow based on multiple inputs and outputs. The inputs included the didactic sessions attended, the ratio of clinical duty works hours to the procedures performed (work intensity index), and the outputs were the Multidisciplinary Critical Care Knowledge Assessment Program (MCCKAP) score and summative evaluations of fellows. RESULTS: A DEA efficiency score that ranged from 0 to 1 was generated for each of the fellows. Five fellows were rated as DEA efficient, and 10 fellows were characterized in the DEA inefficient group. The model was able to forecast the level of effort needed for each inefficient fellow, to achieve similar outputs as their best performing peers. The model also identified the work intensity index as the key element that characterized the best performers in our fellowship. CONCLUSIONS: DEA is a feasible method of objectively evaluating peer performance in a critical care fellowship beyond summative evaluations alone and can potentially be a powerful tool to guide individual performance during the fellowship.

Use of simulation to assess a statistically driven surgical scheduling system.

BACKGROUND: To maximize operating room (OR) utilization, better estimates of case duration lengths are needed. We used computerized simulation to determine whether scheduling OR cases using a statistically driven system that incorporates patient and surgery-specific factors in the process of case duration prediction improves OR throughput and utilization. METHODS: We modeled surgical and anesthetic length of vascular surgical procedures as a function of patient and operative characteristics using a multivariate linear regression approach (Predictive Modeling System [PMS]). Mean historical operative time per surgeon (HMS) and mean anesthetic time were also calculated for each procedure type. A computerized simulation of scheduling in a single OR performing vascular operations was then created using either the PMS or the HMS. RESULTS: Compared to HMS, scheduling the operating room using the PMS increased throughput by a minimum of 15% (99.8% cumulative probability, P < 0.001). The PMS was slightly more likely to lead to overtime (mean 13% versus 11% of operative days during a calendar year, P < 0.001). However, the overtime lasted longer in the HMS group (mean 140 versus 95 min per day of overtime, P < 0.001). PMS was associated with lower OR underutilization rate (mean 23% versus 34% of operative days, P < 0.001) and less lengthy OR underutilization (mean 120 versus 193 min per day of underutilization, P < 0.001). CONCLUSIONS: This computerized simulation demonstrates that using the PMS for scheduling in a single operating room increases throughput and other measures of surgical efficiency.

Predicting case volume from the accumulating elective operating room schedule facilitates staffing improvements.

BACKGROUND: Precise estimates of final operating room demand can only be made 1 or 2 days before the day of surgery, when it is harder to adjust staffing to match demand. The authors hypothesized that the accumulating elective schedule contains useful information for predicting final case demand sufficiently in advance to readily adjust staffing. METHODS: The accumulated number of cases booked was recorded daily, from which a usable dataset comprising 146 consecutive surgical days (October 10, 2011 to May 7, 2012, after removing weekends and holidays), and each with 30 prior calendar days of booking history, was extracted. Case volume prediction was developed by extrapolation from estimates of the fraction of total cases booked each of the 30 preceding days, and averaging these with linear regression models, one for each of the 30 preceding days. Predictions were verified by comparison with actual volume. RESULTS: The elective surgery schedule accumulated approximately three cases per day, settling at a mean ± SD final daily volume of 117 ± 12 cases. The model predicted final case counts within 8.27 cases as far in advance as 14 days before the day of surgery. In the last 7 days before the day of surgery, the model predicted the case count within seven cases 80% of the time. The model was replicated at another smaller hospital, with similar results. CONCLUSIONS: The developing elective schedule predicts final case volume weeks in advance. After implementation, overly high- or low-volume days are revealed in advance, allowing nursing, ancillary service, and anesthesia managers to proactively fine-tune staffing up or down to match demand.

Calculating the true cost of robotic hysterectomy.

Although robotic hysterectomy does not produce significantly better outcomes than laparoscopic hysterectomy, hospitals may feel pressure from patients and clinicians to use the robotic procedure. Hospitals that opt for robotic hysterectomy over laparoscopic hysterectomy face not only higher variable costs, but also an opportunity cost in the form of lost surgical capacity. Estimating the opportunity cost of performing robotic hysterectomy provides crucial data for hospital executives in deciding whether to invest in the procedure.

Modeling anesthetic times. Predictors and implications for short-term outcomes.

BACKGROUND: Little is known about the predictors of anesthetic times and impact of anesthetic and operative times on patient outcomes. METHODS: We documented operative case length, anesthetic induction time length, and anesthetic recovery time length in 1713 consecutive patients who underwent elective vascular surgical interventions. We recorded patient and procedure-related characteristics that might influence the anesthetic time length, including a variable for possible July effect. Multivariate linear regression was used to model the length of anesthetic times. Multivariate logistic regression was used to model the impact of anesthetic and operative time lengths on a composite outcome of perioperative (30-d postoperative) death, myocardial infarction, cardiac arrhythmias, stroke, and congestive heart failure. RESULTS: Statistically significant predictors of anesthetic induction time included body mass index, anesthesia type, and procedure type. Statistically significant predictors of anesthetic recovery time included operative case length, procedure type, and anesthesia type. After adjusting for the statistically significant covariates of total blood transfusion, history of coronary artery disease, and procedure type, there was a trend for increased likelihood of the composite end point as a function of operative time (odds ratio, 1.14; 95% confidence interval, 0.97-1.33; P = 0.09), which did not reach statistical significance. Multivariate analysis showed no association between the anesthetic time and composite end point. CONCLUSIONS: Modeling individually anesthetic induction and recovery time on the basis of operative and anesthetic procedure characteristics is feasible. Anesthetic and operative times do not impact perioperative morbidity and mortality.

Value of a scheduled duration quantified in terms of equivalent numbers of historical cases.

BACKGROUND: Probabilistic estimates of case duration are important for several decisions on and soon before the day of surgery, including filling or preventing a hole in the operating room schedule, and comparing the durations of cases between operating rooms with and without use of specialized equipment to prevent resource conflicts. Bayesian methods use a weighted combination of the surgeon's estimated operating room time and historical data as a prediction for the median duration of the next case of the same combination. Process variability around that prediction (i.e., the coefficient of variation) is estimated using data from similar procedures. A Bayesian method relies on a parameter, τ, that specifies the equivalence between the scheduled estimate and the information contained in the median of a certain number of historical data. METHODS: Times from operating room entrance to exit ("case duration") were obtained for multiple procedures and surgeons at 3 U.S. academic hospitals. A new method for estimating the parameter τ was developed. RESULTS: (1) The method is reliable and has content, convergent, concurrent, and construct validity. (2) The magnitudes of the Somer's D correlations between scheduled and actual durations are small when stratified by procedure (0.05-0.14), but substantial when pooled among all cases and procedures (0.58-0.78). This pattern of correlations matches that when medians (or means) of historical durations are used. Thus, scheduled durations and historical data are essentially interchangeable for estimating the median duration of a future case. (3) Most cases (79%-88%) either have so few historical durations (0-2) that the Bayesian estimate is influenced principally by the scheduled duration, or so many historical durations (>10) that the Bayesian estimate is influenced principally by the historical durations. Thus, the balance between the scheduled duration versus historical data has little influence on results for most cases. (4) Mean absolute predictive errors are insensitive to a wide range of values (e.g., 1-10) for the parameter. The implication is that τ does not routinely need to be calculated for a given hospital, but can be set to any reasonable value (e.g., 5). CONCLUSIONS: Understanding performance of Bayesian methods for case duration is important because variability in durations has a large influence on appropriate management decisions the working day before and on the day of surgery. Both scheduled durations and historical data need to be used for these decisions. What matters is not the choice of τ but quantifying the variability using the Bayesian method and using it in managerial decisions.

Explanation for the near-constant mean time remaining in surgical cases exceeding their estimated duration, necessary for appropriate display on electronic white boards.

BACKGROUND: Consider a case that has been ongoing for longer than the scheduled duration. The anesthesiologist estimates that there is 1 hour remaining. Forty-five minutes later the case has not yet finished, and closure has not yet started. We showed previously that the mean (expected) time remaining is approximately 1 hour, not 15 minutes. The relationship is a direct mathematical consequence of the log-normal probability distributions of operating room (OR) case durations. We test the hypothesis that, with an accurate probabilistic model, until closure begins the estimated mean time remaining would be the mean time from the start of closure to OR exit. METHODS: Among the 311,940 OR cases in a 7-year time series from 1 hospital, there were 3962 cases for which (1) there had been previously at least 30 cases of the same combination of scheduled procedure(s), surgeon, and type of anesthetic and (2) the actual OR time exceeded the 0.9 quantile of case duration before the case started. A Bayesian statistical method was used to calculate the mean (expected) minutes remaining in the case at the 0.9 quantile. The estimate was compared with the actual minutes from the time of the start of closure until the patient exited the OR. RESULTS: The mean ± standard error of the pairwise difference was 0.2 ± 0.4 minutes. The Bayesian estimate for the 0.9 quantile was exceeded by 10.2% ± 0.01% of cases (i.e., very close to the desired 10.0% rate). CONCLUSIONS: If a case is taking longer than the expected (scheduled) duration, closure has not yet started, and someone in the OR is asked how much time the case likely has remaining, the value recorded on a clipboard for viewing later should be the estimated time remaining (e.g., "1 hour") not an end time (e.g., "5:15 pm"). Electronic whiteboard displays should not show that the estimated time remaining in the case is less than the mean time from start of closure to OR exit. Similarly, if closure has started, the expected time remaining that is displayed should not be longer than the mean time from closure to OR exit. Finally, our results match previous reports that, before a case starts, statistical methods can reliably be used to assist in decisions involving the longest amount of time that cases may take (e.g., conflict checking for resources, filling holes in the OR schedule, and preventing holes in the schedule).

Comparison of Incremental Costs and Medicare Reimbursement for Simple vs Complex Cataract Surgery Using Time-Driven Activity-Based Costing.

Importance: Cataract surgery is one of the most commonly performed surgeries across medicine and an integral part of ophthalmologic care. Complex cataract surgery requires more time and resources than simple cataract surgery, yet it remains unclear whether the incremental reimbursement for complex cataract surgery, compared with simple cataract surgery, offsets the increased costs. Objective: To measure the difference in day-of-surgery costs and net earnings between simple and complex cataract surgery. Design, Setting, and Participants: This study is an economic analysis at a single academic institution using time-driven activity-based costing methodology to determine the operative-day costs of simple and complex cataract surgery. Process flow mapping was used to define the operative episode limited to the day of surgery. Simple and complex cataract surgery cases (Current Procedural Terminology codes 66984 and 66982, respectively) at the University of Michigan Kellogg Eye Center from 2017 to 2021 were included in the analysis. Time estimates were obtained using an internal anesthesia record system. Financial estimates were obtained using a mix of internal sources and prior literature. Supply costs were obtained from the electronic health record. Main Outcomes and Measures: Difference in day-of-surgery costs and net earnings. Results: A total of 16 092 cataract surgeries were included, 13 904 simple and 2188 complex. Time-based day-of-surgery costs for simple and complex cataract surgery were $1486.24 and $2205.83, respectively, with a mean difference of $719.59 (95% CI, $684.09-$755.09; P < .001). Complex cataract surgery required $158.26 more for costs of supplies and materials (95% CI, $117.00-$199.60; P < .001). The total difference in day-of-surgery costs between complex and simple cataract surgery was $877.85. Incremental reimbursement for complex cataract surgery was $231.01; therefore, complex cataract surgery had a negative earnings difference of $646.84 compared with simple cataract surgery. Conclusions and Relevance: This economic analysis suggests that the incremental reimbursement for complex cataract surgery undervalues the resource costs required for the procedure, failing to cover increased costs and accounting for less than 2 minutes of increased operating time. These findings may affect ophthalmologist practice patterns and access to care for certain patients, which may ultimately justify increasing cataract surgery reimbursement.

Operating room efficiency of orthopedic surgery during the COVID-19 era.

OBJECTIVE: The COVID-19 pandemic forced operating rooms (ORs) to adopt new safety protocols. Although these measures protected the health of patients and providers, their impact on OR efficiency remains unclear. Our objective was to further elucidate the effects of COVID-19 on orthopedic surgery OR efficiency. STUDY DESIGN: This was a retrospective study of 14,856 orthopedic surgeries performed between December 1, 2019, and October 31, 2021. METHODS: Institutional perioperative databases were used to identify relevant orthopedic surgeries. The onset of the COVID-19 period was set as March 12, 2020, when a state of emergency was declared in Tennessee. Both 90-day periods before and after this date were used for comparative analysis of the pre-COVID-19, peak-restrictions, and post-peak-restrictions time periods. Delay of first case start time and turnover time between cases were used as primary measures of efficiency. RESULTS: There were 1853 pre-COVID-19 cases, 1299 peak-restrictions cases, and 11,704 post-peak-restrictions cases analyzed. Delay of first case start time was found to be significantly different among the time periods (mean [SD] minutes, 7 [14] vs 8 [18] vs 7 [17], respectively; P < .001). Turnover time between cases was also significantly different among the time periods (62 [49] vs 66 [51] vs 64 [51]; P = .002). CONCLUSIONS: Although significant, there was minimal absolute change in orthopedic OR efficiency during the onset of the pandemic. These results suggest that the protocols enacted at our institution appropriately maintained orthopedic OR efficiency, even in the context of the rapidly increasing COVID-19 burden.

Should I Buy This? A Decision-Making Tool for Surgical Value-Based Purchasing.

Many considerations affect the value that a new instrument or product may generate in a surgical practice. This review serves as a guide for surgeons considering new purchases and/or wishing to advocate for hospital acquisition of new items. A summary of data from academic and industry practices is presented, with pertinent examples using relevant surgical devices such as disposable devices, laparoscopic trocars, and otologic endoscopes. Surgeons considering incorporating a new instrument or technology within their practice should weigh the following factors before decision making: patient and clinical care factors, surgeon and care team factors, and hospital factors such as cost, revenue, and sourcing. A surgeon well-versed in stakeholder interests who is involved in the purchase of a new instrument may have significant influence in value-based decision making that not only affects his or her practice but ultimately maximizes value for the patient.

Association of Health Literacy With Postoperative Outcomes in Patients Undergoing Major Abdominal Surgery.

Importance: Low health literacy is known to adversely affect health outcomes in patients with chronic medical conditions. To our knowledge, the association of health literacy with postoperative outcomes has not been studied in-depth in a surgical patient population. Objective: To evaluate the association of health literacy with postoperative outcomes in patients undergoing major abdominal surgery. Design, Setting, and Participants: From November 2010 to December 2013, 1239 patients who were undergoing elective gastric, colorectal, hepatic, and pancreatic resections for both benign and malignant disease at a single academic institution were retrospectively reviewed. Patient demographics, education, insurance status, procedure type, American Society of Anesthesiologists status, Charlson comorbidity index, and postoperative outcomes, including length of stay, emergency department visits, and hospital readmissions, were reviewed from electronic medical records. Health literacy levels were assessed using the Brief Health Literacy Screen, a validated tool that was administered by nursing staff members on hospital admission. Multivariate analysis was used to determine the association of health literacy levels on postoperative outcomes, controlling for patient demographics and clinical characteristics. Main Outcomes and Measures: The association of health literacy with postoperative 30-day emergency department visits, 90-day hospital readmissions, and index hospitalization length of stay. Results: Of the 1239 patients who participated in this study, 624 (50.4%) were women, 1083 (87.4%) where white, 96 (7.7%) were black, and 60 (4.8%) were of other race/ethnicity. The mean (SD) Brief Health Literacy Screen score was 12.9 (SD, 2.75; range, 3-15) and the median educational attainment was 13.0 years. Patients with lower health literacy levels had a longer length of stay in unadjusted (95% CI, 0.95-0.99; P = .004) and adjusted (95% CI, 0.03-0.26; P = .02) analyses. However, lower health literacy was not significantly associated with increased rates of 30-day emergency department visits or 90-day hospital readmissions. Conclusions and Relevance: Lower health literacy levels are independently associated with longer index hospitalization lengths of stay for patients who are undergoing major abdominal surgery. The role of health literacy needs to be further evaluated within surgical practices to improve health care outcomes and use.

Measuring Emergency Department Acuity.

BACKGROUND: Emergency department (ED) acuity is the general level of patient illness, urgency for clinical intervention, and intensity of resource use in an ED environment. The relative strength of commonly used measures of ED acuity is not well understood. METHODS: We performed a retrospective cross-sectional analysis of ED-level data to evaluate the relative strength of association between commonly used proxy measures with a full spectrum measure of ED acuity. Common measures included the percentage of patients with Emergency Severity Index (ESI) scores of 1 or 2, case mix index (CMI), academic status, annual ED volume, inpatient admission rate, percentage of Medicare patients, and patients seen per attending-hour. Our reference standard for acuity is the proportion of high-acuity charts (PHAC) coded and billed according to the Centers for Medicare and Medicaid Service's Ambulatory Payment Classification (APC) system. High-acuity charts included those APC 4 or 5 or critical care. PHAC was represented as a fractional response variable. We examined the strength of associations between common acuity measures and PHAC using Spearman's rank correlation coefficients (rs ) and regression models including a quasi-binomial generalized linear model and linear regression. RESULTS: In our univariate analysis, the percentage of patients ESI 1 or 2, CMI, academic status, and annual ED volume had statistically significant associations with PHAC. None explained more than 16% of PHAC variation. For regression models including all common acuity measures, academic status was the only variable significantly associated with PHAC. CONCLUSION: Emergency Severity Index had the strongest association with PHAC followed by CMI and annual ED volume. Academic status captures variability outside of that explained by ESI, CMI, annual ED volume, percentage of Medicare patients, or patients per attending per hour. All measures combined only explained only 42.6% of PHAC variation.