A stochastic programming approach to surgery scheduling under parallel processing principle

MP68-19 USE OF THE OR BLACK BOX® TO EVALUATE OPERATING ROOM ECONOMIC EFFICIENCY

Purpose: Efficient scheduling in Operating Rooms (ORs) is essential for optimizing corresponding costs and enhancing customer satisfaction in healthcare systems.Design/methodology/approach: Conventional static scheduling methods rely on fixed historical surgery times and often lead to inefficient resource utilization and increased costs due to inaccurate predictions of surgical durations. In this regard, this paper introduces an innovative method that employs Convolutional Neural Networks (CNNs) to predict the remaining intra-surgical time through binary classification for the Gallbladder Dissection phase and to dynamically manage OR schedules. The study, although focused on laparoscopic cholecystectomy procedures, demonstrates a method adaptable to other laparoscopic surgeries. The dataset comprises labeled laparoscopic cholecystectomy videos (time labels for different phases) used to train and evaluate the CNN.Findings: Results show that the proposed method reduces patient waiting times by an average of 87.3% and eliminates OR idle time compared to traditional fixed-time scheduling methods.Originality/value: The innovative aspect of this study lies in the use of CNNs to forecast surgery completion times, enabling nurses to prepare the next patient promptly and allowing for dynamic rescheduling when surgeries run late or finish early. This combination significantly improves OR utilization efficiency and boosts patient satisfaction.

Because most anesthesiologists work in the operating room setting, anesthesiologists are involved with the management of the operating room suite (or “theater”). Even if they are not responsible for the management, anesthesiologists still want to understand the decision-making process and metrics that affect their daily schedule and duties. In the United States, anesthesiologists can attend in-person conferences and seminars on leadership, practice management, and operating room management offered by national associations, online webinars and resources, and textbooks. Although these offerings will give anesthesiologists groundwork for calculations of metrics, e.g., productivity and efficiency, an in-depth discussion is not done.In contrast, Practical Operating Theater Management: Measuring and Improving Performance and Patient Experience provides the reader in-depth materials on data-driven metrics, including operating room efficiency, productivity, utilization, and scheduling. The book purposely is designed not to cover leadership or management styles but focuses on collecting and employing operating room data in a manner that is practical and applicable.The book has two functional sections. The first includes an introduction and eight chapters that cover the terms and principal content, as well as efficiency, productivity, scheduling, planning, staffing and contracts, operating room finances, and preoperative patient preparation. The second section contains five chapters examining operating room management in different countries, including Japan, The Netherlands, Switzerland, Australia, and the United States. Chapter 14 then covers operating room safety, and chapter 15 (the last) provides an overview of the book by a contributor.Some of the topics discussed include the different formats used to manage surgical block times, as well the importance of normalizing terms and definitions so the collected data can be reported, analyzed, and acted upon effectively. Among the data that proves useful to collect is operating room capacity, demand by surgeons, utilization rate, overruns, cancellation rate, start times, and turnover times, with some of these bundled into key performance indicators. Of these, efficiency (as defined in chapter 2), on-time start, and turnover times are considered fundamental.Chapter 2 is central to the book, and in it the author cautions that the much-used and maligned measurement of on-time starts or cancellation rates by themselves are a poor measure of overall operating room efficiency. Efficiency as a measurement is clearly defined in a mathematical formula, and the manner in which operating room management decisions affect it is discussed in detail. Subsequent chapters cover measures of productivity (chapter 3) and strategies for scheduling (chapter 4), with an emphasis on maintaining and utilizing a real-time database of facility-specific surgical times, capacity planning of schedules, and add-on operating room capacity (chapter 5). Operating room staff contracts and the prominent role of staffing in providing the capacity is covered in chapter 6. Chapters 7 and 8 cover operating room finances and preoperative patient preparation, respectively.Although the book’s editor and major contributors are from the United Kingdom, the concepts are applicable to operating room management in the United States as well as other countries. In fact, the latter half of the book explores the similarities and differences in operating room management in different countries, including New Zealand, Japan, The Netherlands, Switzerland, and the United States. It is amazing that anesthesiologists from many different countries face similar challenges.For data-driven metrics, it is not surprising that the book relies on a mathematical approach to data. This math-heavy approach would be more appealing to business-trained operating room managers rather than physicians, although the author did attempt to make it more accessible by incorporating these formulas into Excel spreadsheets. We believe anyone interested in implementing data-driven decision-making processes will be able to follow the mathematical progression that occurs in the first half of the book.Overall, this book is a useful resource to anesthesiologists, operating room managers, and financial analysts on the topics of data-driven metrics. It provides a unique educational resource for those who are new to the topic of operating room efficiency, utilization, and productivity. For those who have been working with these concepts, the book provides a resource to reexamine our formulas with an international perspective.

Many surgical suites allocate operating room (OR) block time to individual surgeons. If block time is allocated to services/groups and yet the same surgeon invariably operates on the same weekday, for all practical purposes block time is being allocated to individual surgeons. Organizational conflict occurs when a surgeon with a relatively low OR utilization has his or her allocated block time reduced. The authors studied potential limitations affecting whether a facility can accurately estimate the average block time utilizations of individual surgeons performing low volumes of cases. Discrete-event computer simulation. Neither 3 months nor 1 yr of historical data were enough to be able to identify surgeons who had persistently low average OR utilizations. For example, with 3 months of data, the widths of the 95% CIs for average OR utilization exceeded 10% for surgeons who had average raw utilizations of 83% or less. If during a 3-month period a surgeon's measured adjusted utilization is 65%, there is a 95% chance that the surgeon's average adjusted utilization is as low as 38% or as high as 83%. If two surgeons have measured adjusted utilizations of 65% and 80%, respectively, there is a 16% chance that they have the same average adjusted utilization. Average OR utilization can be estimated more precisely for surgeons performing more cases each week. Average OR utilization probably cannot be estimated precisely for low-volume surgeons based on 3 months or 1 yr of historical OR utilization data. The authors recommend that at surgical suites trying to allocate OR time to individual low-volume surgeons, OR allocations be based on criteria other than only OR utilization (e.g., based on OR efficiency).

Operating Room Leadership and Perioperative Practice Management is a revised second-edition textbook, intended to be an up-to-date resource for all aspects of operating room management. This edition has 11 new chapters and there has been a substantial revision to the 27 chapters that were retained from the first edition. The book is organized into 5 sections: (1) leadership and strategy; (2) economic considerations, efficiency, and design; (3) surgical and anesthesia practice management; (4) nursing; and (5) safety, standards, and information technology. The contributors to the book are experts in their fields and the majority of the 71 authors are department chairs, medical center chief executive officers, executive directors, or directors of coding or compliance. One of the strengths of this book is the clarity of writing. Each chapter is approximately 10–15 pages in length. The chapters deconstruct complex topics, provide clear definitions for commonly used terms, and, most importantly, provide concrete anesthesia-specific examples. Figures and tables supplement almost every chapter and highlight key learning points. The section on leadership and strategy is perhaps one of the best-written sections of the book, as many of the principles commonly used in the business world are not familiar to many practicing anesthesiologists, yet the reader is given a sufficient primer to have a functional understanding of the relevant tenets. The chapter on The Joint Commission, Center for Medicare and Medicaid Services, and other standards, in particular, is an excellent summary of the history of the modern health care system and the evolution of standards and accreditation over the past century. As the legislative landscape has changed significantly in a relatively short period of time, the authors provide a concise summary of key concepts affecting all anesthesiologists. Another strength of the book is the comprehensive discussion of all aspects of operating room management, from conception of operating room design to staffing and personnel relationships. The authors recognize that the operating room environment is complicated, with its own culture. Successful leaders must understand this complex environment and work collaboratively to effectively manage the operating room. While this overview works well for most chapters, some of the chapters do seem slightly superficial, in particular Chapters 5 (Implications of Emotional Intelligence and Collaboration for Operating Room Leadership and Management), 16 (Efficiency and Scheduling), and 27 (Operating Room Metrics). These chapters have a cursory discussion of 3 very complex topics, leave the reader wanting more information, and do not give the reader the necessary tools to act on the information presented. This might limit the usefulness of the book for someone without existing leadership experience. A further limitation is that the book is intended for use by a multidisciplinary audience, thus some of the information presented, particularly in the preoperative evaluation and management chapter, may seem basic to the established anesthesiologist. Finally, the title of Section 4, “Nursing,” is a bit misleading. While all 3 chapters in this section have nursing implications, the section delves into broader topics, such as staffing and throughput, which are relevant to a broader audience. Ultimately, successful operating room management involves not only administrative efficiency but also knowledge of the legislative landscape and development of high-performing teams who deliver patient care. This book is an excellent primer for physician anesthesiologists who would like to ascend to perioperative leadership roles. While the contents of this book are likely to be a review for experts or established leaders, it is useful for resident trainees, those stepping into leadership positions, or even anesthesiologists who are not in leadership but who would like to better understand the mechanics of operating room management. Nicole Higgins, MDDepartment of AnesthesiologyNorthwestern University Feinberg School of MedicineChicago, IllinoisPaloma Toledo, MD, MPHDepartment of AnesthesiologyNorthwestern University Feinberg School of MedicineChicago, IllinoisCenter for Healthcare StudiesNorthwestern University Feinberg School of MedicineChicago, Illinois[email protected]

Enhancement opportunities in operating room utilization; with a statistical appendix

Three parallel processing operating rooms (ORs) (concurrent induction and turnover) with a dedicated 3-bed mini-recovery room (mini-postanesthesia care unit [PACU]) will optimize patient throughput and main PACU workload when compared with 4 traditional ORs or 4 parallel processing ORs. Statistical and mathematical models projected the impact of parallel processing on case throughput and PACU use. Academic medical center with 48 traditional ORs using serial induction and turnover and 1 experimental OR, the operating room of the future, with parallel processing. All surgical cases from October 2002 through March 2004 (N = 49 887). A statistical model projected the duration of induction, surgery, turnover, and PACU stay for cases performed in a traditional OR (n = 48 667) based on the operating room of the future (n = 1220) experience. A fluid queuing model compared each combination using specific probability density functions. Each OR configuration was evaluated for case throughput and minutes of work sent to the PACU. Although all cases save OR time with parallel processing, only select surgeon-case combinations translate time saved into additional cases per day (26%). Without additional PACU slots, output from 4 parallel processing ORs further stresses the PACU. Three parallel processing ORs and a mini-PACU balances incremental volume by offsetting PACU utilization in 84% of cases. In a PACU-constrained environment, 3 parallel processing ORs with a mini-PACU configuration offers increased throughput and decreased PACU workload.

Because of rising costs and shrinking reimbursements, hospitals must continually find ways to improve efficiency and productivity. This study attempts to increase caseloads in ambulatory surgery operating rooms while maintaining patient satisfaction and safety. In most hospitals, patients move through their operative day in a linear fashion, starting at registration and finishing in the recovery room. Given this pattern, only 1 patient may occupy the efforts of the operating room team at a time. By processing patients in a parallel fashion, operating room efficiency and patient throughput are increased while costs remain stable. Patients undergoing hernia repairs under local anesthesia with intravenous sedation were divided into a control group and an experimental group. Patients in the control group received their local anesthesia in the operating room at the start of the surgery. The experimental group patients received their local anesthesia in the induction room by the surgeon while the operating room was being cleaned and set up. While operative time for the control group and the experimental group were nearly identical, the turnover time and the induction time were significantly shorter for the experimental group. The cumulative reduction in time during the operative day was sufficient to allow the addition of new operative cases. This study demonstrates a system of increasing operating room efficiency by changing patient flow rather than simply working to streamline existing steps. This increase in efficiency is not associated with the expansion of hospital budgets or a decrease in patient safety or satisfaction.

Effective leadership is critical to ensuring safety, efficiency and maximum productivity in the operating room (OR). This practical, evidence-based book unpicks the dynamics of a successful OR environment to underline the key techniques for management of policies, systems, staff members and teams. Fully updated to include recent clinical guidelines, the book provides the 'A-Z' of OR management, including sections on metrics, scheduling, human resource management, leadership principles, economics, quality assurance, recovery and ambulatory practice. New chapters include future healthcare models, emergency preparedness and budgets amongst other topics. Written by authors with unrivalled experience in the field, chapters are laid out in an easy and clinically helpful format to assist learning, and real-life case studies cover 73 different clinically relevant pain topics. This book is an essential guide for anyone working in the OR including anaesthesiologists, surgeons, nurses, and administrators.

The operating room is a vital service in hospitals. The quality of service from theoperating room can be assessed from one indicator, namely operating room utilization.Utilization is the actual time required to carry out additional operations turnover (operationtransfer time) divided by the available time, where turnover averages over 15 minutes.Estimation of the duration of surgery is an important factor in operating room utilization.The aim of this study was to determine the relationship between the accuracy of estimatingthe duration of surgery and the utilization of elective operating rooms in the Central SurgeryInstallation at RSUD Dr. Saiful Anwar Malang. This research method is a prospective cohortfor 19 working days. The sampling technique uses total sampling. The results of the studywere that almost all 284 operations (93.4%) were accurate in estimating the duration of theoperation according to estimates, and the majority of 197 operating rooms (64.8%) hadoperating room utilization in the poor category. Data analysis uses Chi-Square which showsthat the p-value is 0.007 <0.05, which means there is a relationship between the estimatedduration of surgery and operating room utilization. Operating room utilization can beoptimal if one of the factors can be met, namely the operation can be completed on time (onTime) which means the estimation of the duration of the operation must also be correct.Therefore, there is a need for synergy between multidisciplines in the operating room andmanagement in setting schedules and standards for estimating the duration of operations andit is necessary to re-socialize the quality of the operating room for users of the operatingroom and the rooms involved in it, because part of the quality of the operating room is afactors that can influence operating room utilization.

Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms

ABSTRACTThe perioperative services (periop) manager establishes policies to manage the operating room (OR) resources that affect both patient and surgeon satisfaction with the hospital's service. Both the surgeon and the patient are customers of the periop department, and their satisfaction is a measure of the service quality. The periop manager uses block scheduling to allocate OR time. This technique gives one surgical practice (or service specialty) priority access to a block of time for scheduling procedures in an OR. Hospitals seek to obtain more of the surgeon's practice by allocating blocks to the surgeon, but the hospital must balance this OR time allocation with the risk that the OR time may not be used. In doing so, the periop manager creates a schedule that balances the hospital's risk of staff working overtime (beyond the OR's scheduled hours of blocked time) with the risk of staff being idle during a block of time reserved for a surgical practice that did not use it. The authors propose an OR management policy that creates service-specific blocks of OR time as well as open-posting blocks (those blocks shared by all surgical practices) and propose that this policy can increase access to the OR for more surgeons and, consequently, increase both surgeon and patient satisfaction. Through the use of two quality metrics–averseness to overtime and flexibility in sharing block time across services–this article examines how the setting of these two metrics affects OR performance. Historical data were used as an input into a modeling framework that created a feasible block schedule for a set of ORs. The block allocation schedule was then evaluated, with a portion of the data set reserved for testing. In an experiment at a large teaching hospital, results indicated that for every 20 percent increase in a manager's averseness to overtime, the hours in reserved blocks increased by 3 percent and utilization decreased by 2 percent. Similarly, a 33 percent increase in open posting flexibility translated into a reduced need of one OR.

Operating Room (OR) is the core sector in hospital expenditure, the operation management of which involves a complete three-stage surgery flow, multiple resources, prioritization of the various surgeries, and several real-life OR constraints. As such reasonable surgery scheduling is crucial to OR management. To optimize OR management and reduce operation cost, a short-term surgery scheduling problem is proposed and defined based on the survey of the OR operation in a typical hospital in China. The comprehensive operation cost is clearly defined considering both under-utilization and overutilization. A nested Ant Colony Optimization (nested-ACO) incorporated with several real-life OR constraints is proposed to solve such a combinatorial optimization problem. The 10-day manual surgery schedules from a hospital in China are compared with the optimized schedules solved by the nested-ACO. Comparison results show the advantage using the nested-ACO in several measurements: OR-related time, nurse-related time, variation in resources' working time, and the end time. The nested-ACO considering real-life operation constraints such as the difference between first and following case, surgeries priority, and fixed nurses in pre/post-operative stage is proposed to solve the surgery scheduling optimization problem. The results clearly show the benefit of using the nested-ACO in enhancing the OR management efficiency and minimizing the comprehensive overall operation cost.

Gains in operating room (OR) scheduling may be obtained by using accurate statistical models to predict surgical and procedure times. The 3 main contributions of this article are the following: (i) the validation of Strum's results on the statistical distribution of case durations, including surgeon effects, using OR databases of 2 European hospitals, (ii) the use of expert prior expectations to predict durations of rarely observed cases, and (iii) the application of the proposed methods to predict case durations, with an analysis of the resulting increase in OR efficiency. We retrospectively reviewed all recorded surgical cases of 2 large European teaching hospitals from 2005 to 2008, involving 85,312 cases and 92,099 h in total. Surgical times tended to be skewed and bounded by some minimally required time. We compared the fit of the normal distribution with that of 2- and 3-parameter lognormal distributions for case durations of a range of Current Procedural Terminology (CPT)-anesthesia combinations, including possible surgeon effects. For cases with very few observations, we investigated whether supplementing the data information with surgeons' prior guesses helps to obtain better duration estimates. Finally, we used best fitting duration distributions to simulate the potential efficiency gains in OR scheduling. The 3-parameter lognormal distribution provides the best results for the case durations of CPT-anesthesia (surgeon) combinations, with an acceptable fit for almost 90% of the CPTs when segmented by the factor surgeon. The fit is best for surgical times and somewhat less for total procedure times. Surgeons' prior guesses are helpful for OR management to improve duration estimates of CPTs with very few (<10) observations. Compared with the standard way of case scheduling using the mean of the 3-parameter lognormal distribution for case scheduling reduces the mean overreserved OR time per case up to 11.9 (11.8-12.0) min (55.6%) and the mean underreserved OR time per case up to 16.7 (16.5-16.8) min (53.1%). When scheduling cases using the 4-parameter lognormal model the mean overutilized OR time is up to 20.0 (19.7-20.3) min per OR per day lower than for the standard method and 11.6 (11.3-12.0) min per OR per day lower as compared with the biased corrected mean. OR case scheduling can be improved by using the 3-parameter lognormal model with surgeon effects and by using surgeons' prior guesses for rarely observed CPTs. Using the 3-parameter lognormal model for case-duration prediction and scheduling significantly reduces both the prediction error and OR inefficiency.

Stochastic scheduling of chemotherapy appointments considering patient acuity levels