Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department.

Introduction An "unscheduled absence" refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department's modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID­19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P [Formula: see text] 0.0002. For example, there was a 1% unscheduled absence rate among anesthesiologists working Mondays and Fridays early in the pandemic when the prevalence of COVID-19 among asymptomatic patients was 1.3%. At a 1% unscheduled absence rate, 67 would be the minimum scheduled to maintain a <5.0% risk for being unable to run all 65 anesthetizing locations. In contrast, there was a 3% unscheduled absence rate among nurse anesthetists working Mondays and Fridays during the Omicron variant surge when the prevalence was 4.5%. At a 3% unscheduled absence rate, 70 would be the minimum scheduled to maintain the same risk of not being able to run 65 rooms. Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

A Novel Approach to the Room of Errors (ROE): A Three-Dimensional Virtual Tour Activity to Spotlight Patient Safety Threats.

BACKGROUND: Live simulation-based activities are effective tools in teaching situational awareness to improve patient safety training in healthcare settings. The coronavirus disease 2019 (COVID-19) pandemic forced the discontinuation of these in-person sessions. We describe our solution to this challenge: an online interactive activity titled the "Virtual Room of Errors." The aim of this activity is to create an accessible and feasible method of educating healthcare providers about situational awareness in the hospital.  Materials and Methods: We applied existing three-dimensional virtual tour technology used in the real estate sector to a hospital patient room with a standardized patient and 46 intentionally placed hazards. Healthcare providers and students from our institution accessed the room online through a link where they independently navigate, and document observed safety hazards.  Results: In 2021 and 2022, a total of 510 learners completed the virtual Room of Errors (ROE). The virtual ROE increased annual participation in the activity, as compared to the in-person Room, and demonstrated learner satisfaction.  Conclusions: The virtual ROE is an accessible, feasible, and cost-effective method of educating healthcare workers on situational awareness of preventable hazards. Furthermore, the activity is a sustainable way to reach a larger number of learners from multiple disciplines, even as in-person activities resume.

Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department

Introduction An “unscheduled absence” refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department’s modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID‑19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

Scheduling staff for ambulatory anaesthesia.

PURPOSE OF REVIEW: In this study, we summarize six articles published from January 2020 through June 2022 covering anaesthesia staff scheduling and consider their relevance to ambulatory surgery. Staff scheduling refers to the planned shift length of each person working on specific dates. RECENT FINDINGS: Increasing shift lengths compensates for COVID-19 pandemic staffing issues by reducing patient queues and mitigating the impact of staff absence from SAR-CoV-2 infection. Reduced labour costs can often be achieved by regularly scheduling more practitioners than expected from intuition. Probabilities of unscheduled absences, estimated using historical data, should be incorporated into staff scheduling calculations. Anesthetizing locations, wherein anaesthesiologists are scheduled, may need to be revised if the practitioner is lactating to facilitate uninterrupted breast milk pumping sessions. If room assignments are based on the educational value for residents, then schedule other practitioners based on residents' expected work hours, not their planned shift lengths. Mixed integer programming can be used effectively to reduce variability among resident physicians in workloads during their rotations. SUMMARY: Readers can reasonably select among these studies and benefit from the one or two applicable to their facilities' characteristics and work hours.

Average and longest expected treatment times for ultraviolet light disinfection of rooms.

BACKGROUND: Planning Ultraviolet-C (UV-C) disinfection of operating rooms (ORs) is equivalent to scheduling brief OR cases. The study purpose was evaluation of methods for predicting surgical case duration applied to treatment times for ORs and hospital rooms. METHODS: Data used were disinfection times with a 3-tower UV-C disinfection system in N=700 rooms each with ≥100 completed treatments. RESULTS: The coefficient of variation of mean treatment duration among rooms was 19.6% (99% confidence interval [CI] 18.2%-21.0%); pooled mean 18.3 minutes among the 133,927 treatments. The 50th percentile of coefficients of variation among treatments of the same room was 27.3% (CI 26.3%-28.4%), comparable to variabilities in durations of surgical procedures. The ratios of the 90th percentile to mean differed among rooms. Log-normal distributions had poor fits for 33% of rooms. Combining results, we calculated 90% upper prediction limits for treatment times by room using a distribution-free method (e.g., third longest of preceding 29 durations). This approach was suitable because, once UV-C disinfection started, the median difference between the duration estimated by the system and actual time was 1 second. CONCLUSIONS: Times for disinfection should be listed as treatment of a specific room (e.g., "UV-C main OR16"), not generically (e.g., "UV-C"). For estimating disinfection time after single surgical cases, use distribution-free upper prediction limits, because of considerable proportional variabilities in duration.

Proportions of Surgical Patients Discharged Home the Same or the Next Day Are Sufficient Data to Assess Cases' Contributions to Hospital Occupancy.

Introduction When the hospital census is high, perioperative medical directors or operating room (OR) managers may need to consider postponing some surgical cases scheduled to be performed within the next three workdays. This scenario has arisen at hospitals in regions with large increases in admissions due to coronavirus disease 2019 (COVID-19). We compare summary measures for hospital length of stay (LOS) to guide the OR manager having to decide which cases may need to be postponed to ensure a sufficient reserve of available inpatient beds. Methods We studied the 1,201,815 ambulatory and 649,962 inpatient elective cases with a major therapeutic procedure performed during 2018 at all 412 non-federal hospitals in Florida. The data were sorted by the hospital, and then by procedure category. Statistical comparisons of LOS were made pairwise among all procedure categories with at least 100 cases at (the) each hospital, using the chi-square test (LOS ≤ 1 day versus LOS > 1 day), Student's t-test with unequal variances, and the Wilcoxon-Mann-Whitney test. The comparisons among the three tests then were repeated having sorted the data by procedure category and making statistical comparisons among all hospitals with at least 100 cases for the procedure category. Results Whether using a criterion for statistical significance of P < 0.05 or P < 0.01, and whether compared with Student's t-test with unequal variances or Wilcoxon-Mann-Whitney test, the chi-square test had greater odds (i.e., greater statistical power) to detect differences in LOS (all four with P < 0.0001 and all 95% lower confidence limits for odds ratios ≥ 3.00). The findings were consistent when the data, first sorted by procedure category and then by probability distributions of LOS, were compared between hospitals (all P < 0.0001 and the 95% lower confidence limits for odds ratio ≥ 3.72). Conclusions For purposes of comparing procedure categories pairwise at the same hospital, there was no loss of information by summarizing the probability distributions using single numbers, the percentages of cases among patients staying longer than overnight. This finding substantially simplifies the mathematics for constructing dashboards or summaries of OR information system data to help the perioperative OR manager or medical director decide which cases may need to be postponed, when the hospital census is high, to provide a sufficient reserve of inpatient hospital beds.

Policy Implications for the COVID-19 Pandemic in Light of Most Patients (≥72%) Spending at Most One Night at the Hospital After Elective, Major Therapeutic Procedures.

A large number of inpatients with Coronavirus disease 2019 (COVID-19) in some regions of the United States may interfere with the ability of hospitals to take care of patients requiring treatment for other conditions. Nonetheless, many patients need surgery to improve their quality of life and to prevent deterioration in health. Curtailment of services also negatively affects the financial health of hospitals and health systems. Broad policies to prohibit all "elective" surgical procedures to ensure that there is sufficient hospital capacity for pandemic patients may be unnecessarily restrictive because, for many such procedures, patients are rarely admitted following surgery or only stay overnight. We studied all elective inpatient and ambulatory cases involving major therapeutic procedures performed in the state of Florida in 2018. We mapped the primary procedure to the corresponding Clinical Classification Software (CCS) category. We determined the distributions of lengths of stay overall and as stratified by CCS category, then calculated the percentage of cases that had a hospital length of stay of ≤1 night (i.e., 0 or 1 day). A threshold of one night was selected because patients discharged home on the day of surgery have no effect on the inpatient census, and those staying overnight would either have a transient effect or no effect if observed overnight in the postoperative care unit. Among the 1,852,391 elective cases with one or more major therapeutic procedures, 65.2% (95% lower confidence limit [LCL] = 65.1%) of cases had a length of stay of 0 days and 72.9% (95% LCL = 72.8%) had stay ≤1 day. There were 38 different CCS categories for which at least 95% of patients had a length of stay of ≤1 day. There were 28 CCS codes that identified 80% of the patients who were discharged with a length of stay ≤1 day, showing representation of multiple surgical specialties. Our results show that even in the face of constraints imposed by a high hospital census, many categories of major therapeutic elective procedures could be performed without necessarily compromising hospital capacity. Most patients will be discharged on the day of surgery. If overnight admission is required, there would be an option to care for them in the postanesthesia care unit, thus not affecting the census. Thus, policies can reasonably be based on allowing cases with a substantial probability of at most an overnight stay rather than a blanket ban on "elective" surgery or creating a carve-out for specified surgical subspecialties. Such policies would apply to at least 72% of elective, major therapeutic surgical procedures.

A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report.

During the initial wave of the coronavirus disease 2019 (COVID-19) pandemic, many hospitals struggled to forecast bed capacity and the number of mechanical ventilators they needed to have available. Numerous epidemiological models forecast regional or national peak bed and ventilator needs, but these are not suitable for predictions at the hospital level. We developed an analytical model to assist hospitals in determining their census and ventilator requirements for COVID-19 patients during future periods of the pandemic, by using their data. This model is based on (1) projection of future daily admissions using counts from the previous seven days, (2) lengths of stay and duration of mechanical ventilation, and (3) the percentage of inpatients requiring mechanical ventilation. The implementation is done within an Excel (Microsoft, Redmond, WA) workbook without the use of add-ins or macro programming. The model inputs for each currently hospitalized patient with COVID-19 are the duration of hospitalization, whether the patient is currently receiving or has previously received mechanical ventilation, and the duration of the current ventilation episode, if applicable. Data validity and internal consistency are checked within the workbook, and errors are identified. Durations of care (length of hospital stay and duration of mechanical ventilation) are generated by fitting a two-parameter Weibull distribution to the hospital's historical data from the initial phase of the pandemic (incorporating censoring due to ongoing care), for which we provide source code in the R programming language (R Foundation for Statistical Computing, Vienna, Austria). Conditional distributions are then calculated using the hospital's current data. The output of the model is nearly instantaneous, producing an estimate of the census and the number of ventilators required in one, three, and seven days following the date on which the simulation is run. Given that the pandemic is ongoing, and a second surge of cases is expected with the reopening of the economy, having such a tool to predict resource needs for hospital planning purposes has been useful. A major benefit to individual hospitals from such modeling has been to provide reassurance to state and local governments that the hospitals have sufficient resources available to meet anticipated needs for new COVID-19 patients without having to set aside substantially greater numbers of beds or ventilators for such care. Such ongoing activity is important for the economic recovery of hospitals that have been hard-hit economically by the shutdown in elective surgery and other patient care activities. The modeling software is freely available at https://FDshort.com/COVID19, and its parameters can easily be modified by end-users.

Strategies for daily operating room management of ambulatory surgery centers following resolution of the acute phase of the COVID-19 pandemic.

We performed a narrative review to explore the economics of daily operating room management decisions for ambulatory surgery centers following resolution of the acute phase of the Coronavirus Disease 2019 (COVID-19) pandemic. It is anticipated that there will be a substantive fraction of patients who will be contagious, but asymptomatic at the time of surgery. Use multimodal perioperative infection control practices (e.g., including patient decontamination) and monitor performance (e.g., S. aureus transmission from patient to the environment). The consequence of COVID-19 is that such processes are more important than ever to follow because infection affects not only patients but the surgery center staff and surgeons. Dedicate most operating rooms to procedures that are not airway aerosol producing and can be performed without general anesthesia. Increase throughput by performing nerve blocks before patients enter the operating rooms. Bypass the phase I post-anesthesia care unit whenever possible by appropriate choices of anesthetic approach and drugs. Plan long-duration workdays (e.g., 12-h). For cases where the surgical procedure does not cause aerosol production, but general anesthesia will be used, have initial (phase I) post-anesthesia recovery in the operating room where the surgery was done. Use anesthetic practices that achieve fast initial recovery of the brief ambulatory cases. When the surgical procedure causes aerosol production (e.g., bronchoscopy), conduct phase I recovery in the operating room and use multimodal environmental decontamination after each case. Use statistical methods to plan for the resulting long turnover times. Whenever possible, have the anesthesia and nursing teams stagger cases in more than one room so that they are doing one surgical case while the other room is being cleaned. In conclusion, this review shows that while COVID-19 is prevalent, it will markedly affect daily ambulatory workflow for patients undergoing general anesthesia, with potentially substantial economic impact for some surgical specialties.