Implications of Perioperative Team Setups for Operating Room Management Decisions.

BACKGROUND: Team performance has been studied extensively in the perioperative setting, but the managerial impact of interprofessional team performance remains unclear. We hypothesized that the interplay between anesthesiologists and surgeons would affect operating room turnaround times, and teams that worked together over time would become more efficient. METHODS: We analyzed 13,632 surgical cases at our hospital that involved 64 surgeons and 48 anesthesiologists. We detrended and adjusted the data for potential confounders including age, American Society of Anesthesiologists physical status, and surgical list (scheduled cases of specific surgical specialties). The surgical lists were categorized as ear, nose, and throat surgery; trauma surgery; general surgery; and gynecology. We assessed the relationship between turnaround times and assignment of different anesthesiologists to specific surgeons using a Monte Carlo simulation. RESULTS: We found significant differences in team performances among the different surgical lists but no team learning. We constructed managerial decision tables for the assignment of anesthesiologists to specific surgeons at our hospital. We defined a decision algorithm based on these tables. Our analysis indicated that had this algorithm been used in staffing the operating room for the surgical cases represented in our data, median turnaround times would have a reduction potential of 6.8% (95% confidence interval 6.3% to 7.1%). CONCLUSIONS: A surgeon is usually predefined for scheduled surgeries (surgical list). Allocation of the right anesthesiologist to a list and to a surgeon can affect the team performance; thus, this assignment has managerial implications regarding the operating room efficiency affecting turnaround times and thus potentially overutilized time of a list at our hospital.

Resident Physicians Improve Nontechnical Skills When on Operating Room Management and Leadership Rotation.

BACKGROUND: Anesthesiology residency primarily emphasizes the development of medical knowledge and technical skills. Yet, nontechnical skills (NTS) are also vital to successful clinical practice. Elements of NTS are communication, teamwork, situational awareness, and decision making. METHODS: The first 10 consecutive senior residents who chose to participate in this 2-week elective rotation of operating room (OR) management and leadership training were enrolled in this study, which spanned from March 2013 to March 2015. Each resident served as the anesthesiology officer of the day (AOD) and was tasked with coordinating OR assignments, managing care for 2 to 4 ORs, and being on call for the trauma OR; all residents were supervised by an attending AOD. Leadership and NTS techniques were taught via a standardized curriculum consisting of leadership and team training articles, crisis management text, and daily debriefings. Resident self-ratings and attending AOD and charge nurse raters used the Anaesthetists' Non-Technical Skills (ANTS) scoring system, which involved task management, situational awareness, teamwork, and decision making. For each of the 10 residents in their third year of clinical anesthesiology training (CA-3) who participated in this elective rotation, there were 14 items that required feedback from resident self-assessment and OR raters, including the daily attending AOD and charge nurse. Results for each of the items on the questionnaire were compared between the beginning and the end of the rotation with the Wilcoxon signed-rank test for matched samples. Comparisons were run separately for attending AOD and charge nurse assessments and resident self-assessments. Scaled rankings were analyzed for the Kendall coefficient of concordance (ω) for rater agreement with associated χ and P value. RESULTS: Common themes identified by the residents during debriefings were recurrence of challenging situations and the skills residents needed to instruct and manage clinical teams. For attending AOD and charge nurse assessments, resident performance of NTS improved from the beginning to the end of the rotation on 12 of the 14 NTS items (P < .05), whereas resident self-assessment improved on 3 NTS items (P < .05). Interrater reliability (across the charge nurse, resident, and AOD raters) ranged from ω = .36 to .61 at the beginning of the rotation and ω = .27 to .70 at the end of the rotation. CONCLUSIONS: This rotation allowed for teaching and resident assessment to occur in a way that facilitated resident education in several of the skills required to meet specific milestones. Resident physicians are able to foster NTS and build a framework for clinical leadership when completing a 2-week senior elective as an OR manager.

Management implications for the perioperative surgical home related to inpatient case cancellations and add-on case scheduling on the day of surgery.

BACKGROUND: The American Society of Anesthesiologists has embraced the concept of the Perioperative Surgical Home as a means through which anesthesiologists can add value to the health systems in which they practice. One key listed element of the Perioperative Surgical Home is to support "scheduling initiatives to reduce cancellations and increase efficiency." In this study, we explored the potential benefits of the Perioperative Surgical Home with respect to inpatient cancellations and add-on case scheduling. We evaluated 6 hypotheses related to the timing of inpatient cancellations and preoperative anesthesia evaluations. METHODS: Inpatient cancellations were studied during 26 consecutive 4-week intervals between July 2012 and June 2014 at a tertiary care academic hospital. All timestamps related to scheduling, rescheduling, and cancellation activities were retrieved from the operating room (OR) case scheduling system. Timestamps when patients were seen by anesthesia residents were obtained from the preoperative evaluation system database. Batch mean methods were used to calculate means and SE. For cases cancelled, we determined whether, for "most" (>50%) cancellations, a subsequent procedure (of any type) was performed on the patient within 7 days of the cancellation. Comparisons with most and other fractions were assessed using the 1 group, 1-sided Student t test. We evaluated whether a few procedures were highly represented among the cancelled cases via the Herfindahl (Simpson's) index, comparing it with <0.15. The rate of scheduling activity was assessed by computing the number of OR scheduling office decisions in each 1-hour bin between 6:00 AM and 3:59 PM. These values were compared with ≥1 decision per hour at the study hospital. RESULTS: Data from 24,735 scheduled inpatient cases were assessed. Cases cancelled after 7 AM on the day before or at any time on the scheduled day of surgery accounted for 22.6% ± 0.5% (SE) of the scheduled minutes all scheduled cases, and 26.8% ± 0.4% of the case volume (i.e., number of cases). Most (83.1% ± 0.6%, P < 10) cases performed were evaluated on the day before surgery. Most (67.6% ± 1.6%, P < 10) minutes of cancelled cases were evaluated on the day before surgery. Most (62.3% ± 1.5%, P < 10) cases were seen earlier than 6:00 PM of the day before surgery. The Herfindahl index among cancelled procedures was 0.021 ± 0.001 (P < 10 compared not only to <0.15 but also to <0.05), showing large heterogeneity among the cancelled procedures. A subsequent procedure was not performed for most cancelled cases (50.6% ± 0.9% compared with >50%, P = 0.12), implying that the indication for the cancelled procedure no longer existed or the patient/family decided not to proceed with surgery. When only cancellations on the scheduled day of surgery were considered, the cancellation rate was 14.0% ± 0.3% of scheduled inpatient minutes and 11.8% ± 0.2% of scheduled inpatient cases. There were 0.59 ± 0.02 OR schedule decisions per hour per 10 ORs between 6:00 AM and 3:59 PM (P < 10, corresponding to ≥1 decision per hour at the 36 OR study hospital). CONCLUSIONS: The study hospital had a high inpatient cancellation rate, despite the fact that most patients whose cases were cancelled were seen by an anesthesia resident by 6:00 PM of the day before surgery. This finding suggests that further efforts to reduce the cancellations by seeing patients sooner on the day before surgery, or seeing even more patients the day before surgery, would not be an economically useful focus of the Perioperative Surgical Home. The wide heterogeneity among cancelled cases indicates that focusing on a few procedures would not materially affect the overall cancellation rate. The relatively low rate of subsequent performance of a procedure on patients whose cases had been cancelled suggests that trying to decrease the cancellation rate might be medically counterproductive. The hourly rate of decisions in the scheduling office during regular work hours on the day of surgery highlights the importance of decisions made at the OR control desk and scheduling office throughout the day to reduce the hours of overused OR time. These data suggest that efforts of the Perioperative Surgical Home related to inpatient cancellations should focus on management decision-making to mitigate the disruptions to the planned OR schedule caused by inpatient case cancellations and add-on cases, more so than on efforts to reduce inpatient cancellation rates.

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).

Lack of value of scheduling processes to move cases from a heavily used main campus to other facilities within a health care system.

BACKGROUND: Economically, the most important anesthesia group and operating room (OR) management decision is the choice made months before surgery of the allocated OR time (duration of the workday) for each service. Consider a health system with surgeons who practice at multiple hospitals and ambulatory surgery centers. The main campus' ORs are busy, with nearly 8 h of cases, including turnovers, per anesthetizing location per workday. The other (regional) facilities have substantial underutilized time. A surgeon wants to do one 3-hour case at the main campus and have an afternoon start. The anesthesia group's OR director could use the health systems' common OR information system to examine the surgeons' schedules at all facilities. In this study, we quantify the percentage of OR hours that can practically be off-loaded from a main campus with long duration workdays. METHODS: One year of cases were evaluated from a health system with a busy main campus, multiple (11) regional facilities with low workload per OR per day, and a common OR information system. RESULTS: The OR time was summed among surgeons meeting the following criteria: no first case start at the main campus that day; performing <4 hour of elective cases at the main campus that day; and doing at least 1 case at any of the regional facilities within the preceding or following week. The OR time potentially moveable was <0.8% (95% CI, 0.7% to 0.8%) of the total OR time used by all surgeons operating at the main campus, considerably less than the managerially important threshold of "≥ 5.0%" (P < 0.0001). The principal reason for the result was that few (10%) OR hours at the main campus were used by surgeons performing <4 hour of cases that day. To understand why so little OR time could be moved, we performed secondary analysis of different data from 21 facilities nationwide. Larger hours of cases per OR per workday (e.g., 7.8 hour at the main facility) were commonly associated with larger percentages of workdays for which single surgeons filled an OR for the day (r = 0.87 ± 0.05). CONCLUSIONS: For many health systems, investing in the software and personnel to coordinate case scheduling among facilities is unlikely to be of benefit, either operationally or financially.

Using mean duration and variation of procedure times to plan a list of surgical operations to fit into the scheduled list time.

BACKGROUND AND OBJECTIVE: It is important that a surgical list is planned to utilise as much of the scheduled time as possible while not over-running, because this can lead to cancellation of operations. We wished to assess whether, theoretically, the known duration of individual operations could be used quantitatively to predict the likely duration of the operating list. METHODS: In a university hospital setting, we first assessed the extent to which the current ad-hoc method of operating list planning was able to match the scheduled operating list times for 153 consecutive historical lists. Using receiver operating curve analysis, we assessed the ability of an alternative method to predict operating list duration for the same operating lists. This method uses a simple formula: the sum of individual operation times and a pooled standard deviation of these times. We used the operating list duration estimated from this formula to generate a probability that the operating list would finish within its scheduled time. Finally, we applied the simple formula prospectively to 150 operating lists, 'shadowing' the current ad-hoc method, to confirm the predictive ability of the formula. RESULTS: The ad-hoc method was very poor at planning: 50% of historical operating lists were under-booked and 37% over-booked. In contrast, the simple formula predicted the correct outcome (under-run or over-run) for 76% of these operating lists. The calculated probability that a planned series of operations will over-run or under-run was found useful in developing an algorithm to adjust the planned cases optimally. In the prospective series, 65% of operating lists were over-booked and 10% were under-booked. The formula predicted the correct outcome for 84% of operating lists. CONCLUSION: A simple quantitative method of estimating operating list duration for a series of operations leads to an algorithm (readily created on an Excel spreadsheet, http://links.lww.com/EJA/A19) that can potentially improve operating list planning.

Analysis of operating room allocations to optimize scheduling of specialty rotations for anesthesia trainees.

INTRODUCTION: Because specialty workloads and corresponding operating room (OR) allocations vary among days of the week, anesthesia residents and student nurse anesthetists are sometimes assigned to cases off rotation (e.g., scheduled for cardiac surgery but assigned to urology for the day). We describe a method to create hybrid rotations of two specialties (e.g., cardiac and vascular surgery), thereby reducing the numbers of days that trainees are "pulled" from their scheduled rotations. METHODS: Raw data were the number of hours of OR time used by each surgical specialty on each workday for the preceding 9 months. These OR workloads were converted to the number of ORs to be allocated to each specialty for each day of the week on the basis of maximization of the efficiency of use of OR time. We considered all potential hybrid rotations of pairwise combinations of specialties to which trainees could be assigned. Integer linear programming was used to calculate the maximum number of trainees who could be scheduled to hybrid rotations and receive daily assignments matching those rotations. RESULTS: Validity of the results was shown by using data from a small facility for which the optimal solution could be discerned by inspection. Validity (appropriateness) of the constraints was demonstrated by the exclusion of each constraint, resulting in answers that are obviously incorrect. Novelty and usefulness of the method was evidenced by its choosing from among hundreds of thousands of potential combinations of specialties and its identifying appropriate assignments that were substantively different from current rotations. CONCLUSIONS: We developed a methodology to determine rotations consisting of combinations of specialties to be paired for purposes of trainee scheduling to reduce the incidence of daily assignments off rotation. Practically, with this method, anesthesia residents and student nurse anesthetists can be assigned cases within their scheduled rotations as often as possible.

Using clinical data to capture nurse workload: implications for staffing and safety.

The purpose of this project was to demonstrate how a hospital clinical database can be utilized to calculate individual nursing unit activities that affect nurses' workload. While research has established that staffing is associated with patient safety, few studies have examined ways to measure nurse workload and its impact on patient safety. The widely used midnight census does not account for the number of patients who occupy a bed in a 24-hour period. In this study, a hospital clinical data repository was used to calculate workload measures such as total treated patients, midnight census, and admission, discharges, and transfers, as well as a unit activity index. Unit activity indexes for intensive care and medical-surgical units were compared over time, by shift, day of week, and month. Admission, discharges, and transfers varied according to unit type. During 1994 to 2006, unit activity index increased. Fluctuations in unit activity index were noted according to shift, day of week, and month. Hospital clinical data repositories can be used to calculate workload measures, and these measures should be incorporated with other traditional measures in making staffing decisions.

Achieving effective staffing through a shared decision-making approach to open-shift management.

Managing costs while retaining qualified nurses and finding workforce solutions that ensure the delivery of high-quality patient care is of primary importance to nurse leaders and executive management. Leading healthcare organizations are using open-shift management technology as a strategy to improve staffing effectiveness and the work environment. In many hospitals, open-shift management technology has become an essential workforce management tool, nursing benefit, and recruitment and retention incentive. In this article, the authors discuss how a successful nursing initiative to apply automation to open-shift scheduling and fulfillment across a 3-hospital system had a broad enterprise-wide impact resulting in dramatic improvements in nurse satisfaction, retention, recruitment, and the bottom line.

Operational Efficiency and Productivity Improvement Initiatives in a Large Cardiac Catheterization Laboratory.

OBJECTIVES: This study sought to report outcomes from an efficiency improvement project in a large cardiac cath lab. BACKGROUND: Operational inefficiencies are common in the cath lab, yet solutions are challenging. A detailed report describing and providing solutions for these inefficiencies may be valuable in guiding improvements in productivity. METHODS: In this observational study, the authors report metrics of efficiency before and after a cath lab quality improvement program in June 2014. Main outcomes included lab room start times, room turnaround times, laboratory use, and employee satisfaction. Time series analysis was used to assess trend over time. Chi-square testing and analysis of variance were used to assess change before and after the initiative. RESULTS: The principal changes included implementation of a pyramidal nursing schedule, increased use of an electronic scheduling system, and increased utilization of a preparation and recovery area. Comparing before with after the program, start times improved an average of 17 min, and on-time starts improved from 61.8% to 81.7% (p = 0.0024). Turnaround times improved from 20.5 min to 16.4 min (trend p < 0.0001), and the proportion of days at full lab utilization improved from 7.7% to 77.3% (p < 0.00001). There were no increases in overtime, night, or weekend cases. There was a reduction in full time employees from 36.1 in 2013 to 29.6 in 2016, with an improvement in employee satisfaction. CONCLUSIONS: A systematic approach to reducing inefficiencies can improve cath lab start times, turnaround times, and overall productivity. This knowledge may be helpful in assisting other cath labs in similar efficiency improvement initiatives.

A web-based programme for hospital and community student placements.

A web-based clinical placement information project was implemented through a partnership between one university, an acute trust and one primary care trust. The collaborative project was developed as a result of the concern regarding a lack of parity of information and learning opportunities for students between individual placements in the area and in recognition that both higher education institutions and service providers are responsible for the quality of the learning opportunities provided for students. Student nurses are given a 10-14 week hospital or community placement in an area that can be up to 30 miles away from the university campus. Problems arise from this geographical separation and the diversity of placements in the area. The web site provides a resource of contact details and information of possible learning experiences for students, outlining the aims and objectives of the placements and services in the area.

Analyzing fluctuating unit census for timely staffing intervention.

A computer information system, which can provide cost-saving measures useful in adequately allocating nursing staff in the face of decreasing patient stays, increasing patient admissions and discharges, and the unexpected transfers surrounding busy nursing units, is explored.

A multiuser MUMPS language patient/physician scheduling system for microcomputers.

A microcomputer based patient and physician scheduling system is described that accommodates the special needs unique to residency training programs. Appointments are scheduled automatically according to the type of problems the patient brings to the office as well as the differing time requirements of attending physicians and residents at various levels of training.

Self-scheduling with Microsoft Excel.

Excessive time was being spent by the emergency department (ED) staff, head nurse, and unit secretary on a complex 6-week manual self-scheduling system. This issue, plus inevitable errors and staff dissatisfaction, resulted in a manager-lead initiative to automate elements of the scheduling process using Microsoft Excel. The implementation of this initiative included: common coding of all 8-hour and 12-hour shifts, with each 4-hour period represented by a cell; the creation of a 6-week master schedule using the "count-if" function of Excel based on current staffing guidelines; staff time-off requests then entered by the department secretary; the head nurse, with staff input, then fine-tuned the schedule to provide even unit coverage. Outcomes of these changes included an increase in staff satisfaction, time saved by the head nurse, and staff work time saved because there was less arguing about the schedule. Ultimately, the automated self-scheduling method was expanded to the entire 700-bed hospital.

Statistical method using operating room information system data to determine anesthetist weekend call requirements.

We present a statistical method that uses data from surgical services information systems to determine the minimum number of anesthetists to be scheduled for weekend call in an operating room suite. The staffing coverage is predicted that provides for sufficient anesthetists to cover each hour of a 24-hour weekend period, while satisfying a specified risk for being understaffed. The statistical method incorporates shifts of varying start times and durations, as well as historical weekend operating room caseload data. By using this method to schedule weekend staff, an anesthesia group can assure as few anesthetists are on call as possible, and for as few hours as possible, while maintaining the level of risk of understaffing that the anesthesia group is willing to accept. An anesthesia group also can use the method to calculate its risk of being understaffed in the surgical suite based on its existing weekend staffing plan.

Determining staffing requirements for a second shift of anesthetists by graphical analysis of data from operating room information systems.

Some operating room (OR) managers face the dilemma whereby all cases in a surgical suite are not completed during a regularly scheduled (e.g., 8-hour) day. If the anesthesia group at the surgical suite plans for its employed anesthetists to work a fixed number of hours each day, then more than 1 shift of anesthetists may be needed to care for the patients in the ORs. We developed a graphical statistical method that anesthetists and anesthesiologists can use to determine how many anesthesia providers are required on the second shift to minimize labor costs. The method uses data from surgical services information systems or hospital information systems to compensate for seasonality or seasonal variation in the number of ORs running at different times of the day. We also consider application of our method to scheduling surgical nurses with multiple overlapping shifts throughout the day.