An integrated approach for surgery scheduling under uncertainty

Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

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.

Scheduling elective surgeries is a dynamic, sequential decision-making process that must balance the costs of deferring waiting cases and blocking higher-priority cases. Although other surgery scheduling problems have received extensive treatment in the literature, this paper presents the first single-day scheduling problem formulation to capture this aspect of the scheduling process while also incorporating surgical block schedules, block release policies, and waiting lists. Theoretical results for the special case in which all cases have the same duration motivate a range of threshold-based heuristics for the general problem with multiple case durations. Our computational results demonstrate the effectiveness of the proposed heuristics and show how block release dates affect the quality of the scheduling decisions. Based on these results, we propose a new approach to surgery scheduling. In particular, to make more equitable waiting list decisions, operating room (OR) managers should gradually release unused OR time over the course of several days leading up to the day of surgery.

Objective:This study examines the 2009 patient operating room (OR) length of stay data for a medical center in Taipei City. Using a statistical method based on regression analysis, the crucial factors that influence OR efficiency and performance were identified. The analytical results indicated that surgical procedures with general anesthesia and high surgical complexity incurred high surgical fees. Additionally, the anesthesia fees, operating time, and operating time variance rises as the squared number of surgical procedures increases. Therefore, for the hospitals 2010 OR scheduling management strategy, we adjusted the priority regulations for daily OR use to enhance the safety and quality of surgical patient health care and improve OR efficiency.Method:We collected 11 case ORs, that is, 9 inpatient and 2 outpatient ORs, for this study. The pre-implementation data were collected between January 1, 2009, and December 31, 2009. The post-implementation data were collected between January 1, 2010, and December 31, 2010; both forms of data included patients OR length of stay information. Furthermore, the indicators that were compared and analyzed for this study included variances in the number of cases in which general anesthesia was administered, the number of scheduled surgical procedures, the number of emergency surgical procedures, and OR use rates.Results:A total of 20,731 surgical operations were performed in 2009, among which general anesthesia was administered 6,925 times, 10,122 scheduled surgical procedures were performed, 1,305 emergency surgical procedures were performed, and the OR use rate was 70.30%. A total of 21,105 surgical operations were performed in 2010, among which general anesthesia was administered 7,106 times, 10,549 scheduled surgical procedures were performed, 1,319 emergency surgical procedures were performed, and the OR use rate was 71.05%.

We develop algorithms for a stochastic two-machine single-server sequencing problem with waiting time, idle time and overtime costs. Scheduling surgeries for a single surgeon operating in two parallel operating rooms (ORs) motivates the work. The basic idea is that staff perform cleanup and setup in one OR while the surgeon is operating in the other. The benefit is less waiting time for the surgeon between surgeries, but may also result in added idle time for staff if cleanup and set-up are completed prior to completion of surgery in the other OR. When surgeries are long relative to cleanup and setup times, parallel OR scheduling is not attractive as significant OR and staff idle time will result. The problem we address consists of assigning surgeries to ORs and sequencing them, and is formulated as an integer stochastic program using sample average approximation. A decomposition based solution approach is developed. Computational testing based on real data shows that the proposed methods solve the problems to optimality in acceptable processing times. Using the solution methodology, we conduct additional experiments to provide insight as to the conditions when scheduling a single surgeon in parallel operating rooms is cost effective.

<p><strong><em>Abstract</em></strong></p><p><strong><em>Introduction</em></strong><em> : Operating room eficiency can be assessed using 6 indicators, one of which is the delay in starting the first elective surgery. From the results of preliminary research in the operating room of the Central Surgical Installation (CSI) R</em><em>SUD dr. Achmad Mochtar </em><em>(RSAM) Bukittinggi in January and February 2019 showed that 81% of elective surgeries were delayed. Delay ranged from 30 minutes to several hours. </em></p><p><strong><em>Objective</em></strong><em>: To find out what causes the delay in begining the first elective surgery in the operating room of the Central Surgery Installation at RSAM in 2019 in terms of input, process and output.</em></p><p><strong><em>Method</em></strong><em>: Qualitative research using the Framework Analysis method was conducted in the operating room of the CSI RSAM. The study began in April until October 2019</em></p><p><strong><em>Conclusion</em></strong><em>: The reason for the delay in begining the surgery in terms of HR input is that the leadership in CSI is not maximized, lack of discipline and commitment of the operator doctor and anesthesiologist, the anesthesiologist's schedule is not optimized, lack of registration officers, no optimal settings for radiographer for operating rooms and the lack of skills. The problem with SOP inputs is that there are still incomplete SOPs and not yet optimal implementation of existing SOPs. In the infrastructure input the problem is in the distance from the patient's ward to the operating room, CSSD is not integrated to the operating room and picking up surgery clothes and linen to CSSD is late. From the process factor, there are obstacles in the process of delivering patients to the operating room, the arrival time of the surgeon/operator and anesthesiologist are behind schedule, the lack of optimal monitoring and evaluation, and the lack of optimal implementation of reward and punishment. Output shows that from 40 firsts surgery schedules, 35 surgeries were delayed (87.5%) and only 5 operations were carried out on time according to the schedule (12.5%).</em></p><p><strong><em>Keywords</em></strong><em>: </em><em> Efficiency, Operating Room, Monitoring, Reward, Punishment</em></p>

Patient admission and surgery scheduling is a complex combinatorial optimization problem. It consists on defining patient admission dates, assigning them to suitable rooms, and schedule surgeries accordingly to an existing master surgical schedule. This problem belongs to the class of NP-hard problems. In this paper, we firstly formulate an integer programming model for offline patient admissions, room assignments, and surgery scheduling; then apply a matheuristic that combines exact methods with rescheduling approaches. The matheuristic is evaluated using benchmark datasets. The experimental results improve those reported in the literature and show that the proposed method outperforms existing techniques of the state-of-the-arts.

The impact of routine post-anesthesia care unit extubation for pediatric surgical patients on safety and operating room efficiency

The Operating Room (OR) is the financial engine of the modern hospital or surgical center. Over 40% of the hospital's cost is invested in the OR, but the facility recoups a monetary return of around 60-70%. Turnover time is a crucial factor in reducing costs. Uncontrolled, it can inflate operating costs, but when managed effectively, it can boost surgeon, patient, and staff satisfaction, enhance patient safety, and significantly reduce operational costs. The literature underscores the importance of reducing the time between operating room turnovers to improve OR efficiency. A key strategy that research consistently shows to be effective is the implementation of structured presurgical briefings, such as TeamSTEPPS. These briefings, with their structured approach, play a crucial role in reducing turnover time in an OR, thereby increasing efficiency. The initial investment in developing a standardized preoperative brief through the TeamSTEPPS program as part of the morning brief has the potential to boost OR productivity and revenue significantly. By reducing the time from wheels out to wheels in for each surgery during the operating room's surgical day, this approach can lead to substantial financial gains. The project information is collected from 308 surgical cases from various services over three weeks during the project to determine whether conducting a formal TeamSTEPPS style preoperative formatted brief would reduce turnover times (TOTS) in the operating room (OR). The data showed a mean average reduction of slightly over six minutes in mean average turnover times during the project. The data showed that 20% of all scheduled cases had a turnover time greater than 25 minutes. Of the 20% of the cases, 7.6% of the increased TOTs were attributed to processes controlled by the OR staff. The remaining 12.6% of turnover time was attributed to issues related to surgeons, SDS/ APU, and different services in the hospital. The data shows that the effects of a daily organized and standardized presurgical briefing will improve turnover times better than a presurgical briefing without structure or organization.

To reflect uncertain data in practical problems, stochastic versions of the mathematical program with complementarity constraints (MPCC) have drawn much attention in the recent literature. Our concern is the detailed analysis of convergence properties of a regularization sample average approximation (SAA) method for solving a stochastic mathematical program with complementarity constraints (SMPCC). The analysis of this regularization method is carried out in three steps: First, the almost sure convergence of optimal solutions of the regularized SAA problem to that of the true problem is established by the notion of epiconvergence in variational analysis. Second, under MPCC-MFCQ, which is weaker than MPCC-LICQ, we show that any accumulation point of Karash-Kuhn-Tucker points of the regularized SAA problem is almost surely a kind of stationary point of SMPCC as the sample size tends to infinity. Finally, some numerical results are reported to show the efficiency of the method proposed.

Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models

The various robust linear programming models investigated so far in the literature essentially appear to be based either on what is referred to as rowwise uncertainty models or on columnwise uncertainty models (these basically assume that the rows, or the columns, of the constraint matrix are subject to changes within a well-specified uncertainty set). In this chapter, we discuss a special case of columnwise uncertainty, namely the subclass of robust linear programming (LP) models with uncertainty limited to the right hand side (RHS) only (this subclass does not appear to have been significantly investigated so far). In this context we introduce the concept of a ‘two-stage robust LP model’ as opposed to the standard case (which might be referred to as a ‘single-stage robust LP model’) and we address the question of whether LP duality can be used to convert a LP problem with RHS uncertainty into a robust LP problem with uncertainty on the objective function. We show how to derive both statements of (a) the dual to the robust model and (b) the robust version of the dual. The resulting expressions of the objective function to be optimized in both cases, appear to be clearly distinct. Moreover, from a complexity point of view, one appears to be efficiently solvable (it reduces to a convex optimization problem), whereas the other, as a nonconvex optimization problem, is expected to be computationally difficult in the general case. As an application of the two-stage robust LP model introduced here, we next investigate the PERT (program evaluation and review technique) scheduling problem, considering two possible natural ways of specifying the uncertainty set for the task durations: the case where the uncertainty set is a scaled ball with respect to the L ∞ norm; the case where the uncertainty set is a scaled Hamming ball of bounded radius (which, though leading to a quite different model, bears some resemblance to the well-known Bertsimas–Sim approach to robustness). We show that in both cases the resulting robust optimization problem can be efficiently solved in polynomial time.

Sample average approximation (SAA) method has recently been applied to solve stochastic programs with second order stochastic dominance (SSD) constraints. In particular, Hu et al. (Math Program 133:171–201, 2012) presented a detailed convergence analysis of \(\epsilon \)-optimal values and \(\epsilon \)-optimal solutions of sample average approximated stochastic programs with polyhedral SSD constraints. In this paper, we complement the existing research by presenting convergence analysis of stationary points when SAA is applied to a class of stochastic minimization problems with SSD constraints. Specifically, under some moderate conditions we prove that optimal solutions and stationary points obtained from solving sample average approximated problems converge with probability one to their true counterparts. Moreover, by exploiting some recent results on large deviation of random functions and sensitivity analysis of generalized equations, we derive exponential rate of convergence of stationary points.

Sample Average Approximation in Data-Driven Newsvendor In the data-driven newsvendor problem, the manager makes sequential inventory decisions while learning the unknown demand distribution based on past demand samples. How does the widely used sample average approximation approach perform in this problem? In “Technical Note—Data-Driven Newsvendor Problem: Performance of the Sample Average Approximation,” Lin, Huh, Krishnan, and Uichanco analyze the performance of the sample average approximation as the time horizon grows, which turns out to be the best possible. The authors also examine how the local flatness of the demand distribution around the optimal order quantity affects the complexity of the problem. They show that the sample average approximation has the best achievable performance in terms of not only the time horizon, but also the local flatness of the demand distribution.

Monitored anesthesia care (MAC) has been increasingly used in lieu of general anesthesia (GA) for transcatheter aortic valve replacement (TAVR). We sought to compare outcomes and in-hospital costs between MAC and GA for TAVR at a Veterans Affairs Medical Center. A single-center retrospective review was performed of 349 patients who underwent transfemoral TAVR (MAC, n = 244 vs GA, n = 105) from January 2014 to December 2019. Baseline patient characteristics, operating room (OR) time, intensive care unit (ICU) length of stay (LOS), and cost, total LOS, hospital cost, total cost, and complication rates were collected. Propensity matching was performed and resulted in 83 matched pairs. In the unmatched TAVR cohort, MAC TAVR was associated with reduced OR time (146 vs 198 min, P < 0.001), ICU LOS (1.4 vs 1.8 days, P < 0.001), total hospital LOS (3.4 vs 5.4 days, P < 0.001), and lower index total cost ($81,300 vs $85,400, P = 0.010). After propensity matching, MAC TAVR patients had reduced OR time (146 vs 196 min, P < 0.05), ICU LOS (1.2 vs 1.7 days, P = 0.006), total LOS (3.5 vs 5.1 days, P = 0.001), and 180-day mortality (2.4% vs 12%, P < 0.03). There was no difference in total hospitalization cost or total cost. In propensity-matched groups, TAVR utilizing MAC is associated with improved OR time efficiency, decreased LOS, and a reduction in 180-day mortality but no significant difference in cost.