Application of Operational Research Techniques in Operating Room Scheduling Problems: Literature Overview

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

Developing a Cost-Effective Surgical Scheduling System Applying Lean Thinking and Toyota's Methods for Surgery-Related Big Data for Improved Data Use in Hospitals: User-Centered Design Approach

BACKGROUND

Surgical scheduling is pivotal in managing daily surgical sequences, impacting patient experience and hospital resources significantly. With operating rooms costing approximately US $36 per minute, efficient scheduling is vital. However, global practices in surgical scheduling vary, largely due to challenges in predicting individual surgeon times for diverse patient conditions. Inspired by the Toyota Production System's efficiency in addressing similar logistical challenges, we applied its principles as detailed in the book "Lean Thinking" by Womack and Jones, which identifies processes that do not meet customer needs as wasteful. This insight is critical in health care, where waste can compromise patient safety and medical quality.

OBJECTIVE

This study aims to use lean thinking and Toyota methods to develop a more efficient surgical scheduling system that better aligns with user needs without additional financial burdens.

METHODS

We implemented the 5 principles of the Toyota system: specifying value, identifying the value stream, enabling flow, establishing pull, and pursuing perfection. Value was defined in terms of meeting the customer's needs, which in this context involved developing a responsive and efficient scheduling system. Our approach included 2 subsystems: one handling presurgery patient data and another for intraoperative and postoperative data. We identified inefficiencies in the presurgery data subsystem and responded by creating a comprehensive value stream map of the surgical process. We developed 2 Excel (Microsoft Corporation) macros using Visual Basic for Applications. The first calculated average surgery times from intra- or postoperative historic data, while the second estimated surgery durations and generated concise, visually engaging scheduling reports from presurgery data. We assessed the effectiveness of the new system by comparing task completion times and user satisfaction between the old and new systems.

RESULTS

The implementation of the revised scheduling system significantly reduced the overall scheduling time from 301 seconds to 261 seconds (P=.02), with significant time reductions in the revised process from 99 seconds to 62 seconds (P<.001). Despite these improvements, approximately 21% of nurses preferred the older system for its familiarity. The new system protects patient data privacy and streamlines schedule dissemination through a secure LINE group (LY Corp), ensuring seamless flow. The design of the system allows for real-time updates and has been effectively monitoring surgical durations daily for over 3 years. The "pull" principle was demonstrated when an unplanned software issue prompted immediate, user-led troubleshooting, enhancing system reliability. Continuous improvement efforts are ongoing, except for the preoperative patient confirmation step, which requires further enhancement to ensure optimal patient safety.

CONCLUSIONS

Lean principles and Toyota's methods, combined with computer programming, can revitalize surgical scheduling processes. They offer effective solutions for surgical scheduling challenges and enable the creation of a novel surgical scheduling system without incurring additional costs.

Video-Assisted vs Robotic-Assisted Lung Lobectomies for Operating Room Resource Utilization and Patient Outcomes

Importance

With increased use of robots, there is an inadequate understanding of minimally invasive modalities' time costs. This study evaluates the operative durations of robotic-assisted vs video-assisted lung lobectomies.

Objective

To compare resource utilization, specifically operative time, between video-assisted and robotic-assisted thoracoscopic lung lobectomies.

Design, Setting, and Participants

This retrospective cohort study evaluated patients aged 18 to 90 years who underwent minimally invasive (robotic-assisted or video-assisted) lung lobectomy from January 1, 2020, to December 31, 2022, with 90 days' follow-up after surgery. The study included multicenter electronic health record data from 21 hospitals within an integrated health care system in Northern California. Thoracic surgery was regionalized to 4 centers with 14 board-certified general thoracic surgeons.

Exposures

Robotic-assisted or video-assisted lung lobectomy.

Main Outcomes and Measures

The primary outcome was operative duration (cut to close) in minutes. Secondary outcomes were length of stay, 30-day readmission, and 90-day mortality. Comparisons between video-assisted and robotic-assisted lobectomies were generated using the Wilcoxon rank sum test for continuous variables and the χ2 test for categorical variables. The average treatment effects were estimated with augmented inverse probability treatment weighting (AIPTW). Patient and surgeon covariates were adjusted for and included patient demographics, comorbidities, and case complexity (age, sex, race and ethnicity, neighborhood deprivation index, body mass index, Charlson Comorbidity Index score, nonelective hospitalizations, emergency department visits, a validated laboratory derangement score, a validated institutional comorbidity score, a surgeon-designated complexity indicator, and a procedural code count), and a primary surgeon-specific indicator.

Results

The study included 1088 patients (median age, 70.1 years [IQR, 63.3-75.8 years]; 704 [64.7%] female), of whom 446 (41.0%) underwent robotic-assisted and 642 (59.0%) underwent video-assisted lobectomy. The median unadjusted operative duration was 172.0 minutes (IQR, 128.0-226.0 minutes). After AIPTW, there was less than a 10% difference in all covariates between groups, and operative duration was a median 20.6 minutes (95% CI, 12.9-28.2 minutes; P < .001) longer for robotic-assisted compared with video-assisted lobectomies. There was no difference in adjusted secondary patient outcomes, specifically for length of stay (0.3 days; 95% CI, -0.3 to 0.8 days; P = .11) or risk of 30-day readmission (adjusted odds ratio, 1.29; 95% CI, 0.84-1.98; P = .13). The unadjusted 90-day mortality rate (1.3% [n = 14]) was too low for the AIPTW modeling process.

Conclusions and Relevance

In this cohort study, there was no difference in patient outcomes between modalities, but operative duration was longer in robotic-assisted compared with video-assisted lung lobectomy. Given that this elevated operative duration is additive when applied systematically, increased consideration of appropriate patient selection for robotic-assisted lung lobectomy is needed to improve resource utilization.

Factors affecting operating room scheduling accuracy for primary and revision total hip arthroplasty: a retrospective study

INTRODUCTION

Optimizing operating room (OR) scheduling accuracy is important for OR efficiency, meeting patient expectations, and maximizing value for health systems. However, limited data exist on factors influencing the precision of Total Hip Arthroplasty (THA) OR scheduling. This study aims to identify the factors influencing the accuracy of OR scheduling for THA.

METHODS

A retrospective review of 6,072 THA (5,579 primary THA and 493 revision THA) performed between January 2020 and May 2023 at an urban, academic institution was conducted. We collected baseline patient characteristics, surgeon years of experience, and compared actual wheels in to wheels out (WIWO) OR time against scheduled OR time. Significant scheduling inaccuracies were defined as actual OR times deviating by at least 15% from scheduled OR times. Logistic regression analyses were employed to assess the impact of patient, surgeon, and intraoperative factors on OR scheduling accuracy.

RESULTS

Using adjusted odds ratios, primary THA patients who had a lower BMI and surgeons who had less than 10 years of experience were associated with overestimation of OR time. Whereas, higher BMI, younger age, general anesthesia, non-primary osteoarthritis indications, and afternoon procedure start times were linked to underestimation of OR time. For revision THA, lower BMI and fewer components revised correlated with overestimated OR time. Men, higher BMI, more components revised, septic indication for surgery, and morning procedure start times were associated with underestimation of OR time.

CONCLUSION

This study highlights several critical patient, surgeon, and intraoperative factors influencing OR scheduling accuracy for THA. OR scheduling models should consider these factors to enhance OR efficiency.

Operating Room Performance Optimization Metrics: a Systematic Review

Literature proposes numerous initiatives for optimization of the Operating Room (OR). Despite multiple suggested strategies for the optimization of workflow on the OR, its patients and (medical) staff, no uniform description of 'optimization' has been adopted. This makes it difficult to evaluate the proposed optimization strategies. In particular, the metrics used to quantify OR performance are diverse so that assessing the impact of suggested approaches is complex or even impossible. To secure a higher implementation success rate of optimisation strategies in practice we believe OR optimisation and its quantification should be further investigated. We aim to provide an inventory of the metrics and methods used to optimise the OR by the means of a structured literature study. We observe that several aspects of OR performance are unaddressed in literature, and no studies account for possible interactions between metrics of quality and efficiency. We conclude that a systems approach is needed to align metrics across different elements of OR performance, and that the wellbeing of healthcare professionals is underrepresented in current optimisation approaches.

BMI and pelvimetry help to predict the duration of laparoscopic resection for low and middle rectal cancer

BACKGROUND

In rectal cancer surgery, recent studies have found associations between clinical factors, especially pelvic parameters, and surgical difficulty; however, their findings are inconsistent because the studies use different criteria. This study aimed to evaluate common clinical factors that influence the operative time for the laparoscopic anterior resection of low and middle rectal cancer.

METHODS

Patients who underwent laparoscopic radical resection of low and middle rectal cancer from January 2018 to December 2020 were retrospectively analyzed and classified according to the operative time. Preoperative clinical and magnetic resonance imaging (MRI)-related parameters were collected. Logistic regression analysis was used to identify factors for predicting the operative time.

RESULTS

In total, 214 patients with a mean age of 60.3 ± 8.9 years were divided into two groups: the long operative time group (n = 105) and the short operative time group (n = 109). Univariate analysis revealed that the male sex, a higher body mass index (BMI, ≥ 24.0 kg/m²), preoperative treatment, a smaller pelvic inlet (< 11.0 cm), a deeper pelvic depth (≥ 10.7 cm) and a shorter intertuberous distance (< 10.1 cm) were significantly correlated with a longer operative time (P < 0.05). However, only BMI (OR 1.893, 95% CI 1.064-3.367, P = 0.030) and pelvic inlet (OR 0.439, 95% CI 0.240-0.804, P = 0.008) were independent predictors of operative time. Moreover, the rate of anastomotic leakage was higher in the long operative time group (P < 0.05).

CONCLUSION

Laparoscopic rectal resection is expected to take longer to perform in patients with a higher BMI or smaller pelvic inlet.

Day Surgery Scheduling and Optimization in Large Public Hospitals in China: A Three-Station Job Shop Scheduling Problem

Day surgery scheduling allocates hospital resources to day surgical cases and decides on the time to perform the surgeries in the day surgery center (DSC). Based on the day surgery service process of large public hospitals in China, we found that the service efficiency of the process depends on the utilization of hospital resources efficiently and could be optimized through day surgery scheduling. We described it as a flexible flow shop owing to the three-station nature of surgery. Allocating all types of hospital resources to the three stations and determining the length of time for each stage during surgery are crucial to improving the efficiency of DSC. This paper integrates a three-station job shop scheduling problem (JSSP) into the day surgery scheduling and optimization problem. The JSSP was formulated as a mixed-integer linear programming model, and the elicitation of the model for scheduling surgeries with different priorities in the DSC is discussed. The model illustrated a case study of the DSC within West China Hospital (WCH). Numerical experiments based on the genetic algorithm design were conducted. Compared to the other optimization strategies, we proposed that the three-station job shop scheduling strategy (TSJS) could not only improve the efficiency and reduce the waiting time of the patients of the DSC in large public hospitals in China but also allow for timely scheduling adjustments during the advent of emergency surgeries.

Optimizing Operation Room Utilization—A Prediction Model

Background: Operating rooms are the core of hospitals. They are a primary source of revenue and are often seen as one of the bottlenecks in the medical system. Many efforts are made to increase throughput, reduce costs, and maximize incomes, as well as optimize clinical outcomes and patient satisfaction. We trained a predictive model on the length of surgeries to improve the productivity and utility of operative rooms in general hospitals. Methods: We collected clinical and administrative data for the last 10 years from two large general public hospitals in Israel. We trained a machine learning model to give the expected length of surgery using pre-operative data. These data included diagnoses, laboratory tests, risk factors, demographics, procedures, anesthesia type, and the main surgeon’s level of experience. We compared our model to a naïve model that represented current practice. Findings: Our prediction model achieved better performance than the naïve model and explained almost 70% of the variance in surgery durations. Interpretation: A machine learning-based model can be a useful approach for increasing operating room utilization. Among the most important factors were the type of procedures and the main surgeon’s level of experience. The model enables the harmonizing of hospital productivity through wise scheduling and matching suitable teams for a variety of clinical procedures for the benefit of the individual patient and the system as a whole.

Automatic Surgery and Anesthesia Emergence Duration Prediction Using Artificial Neural Networks

Cost control is becoming increasingly important in hospital management. Hospital operating rooms have high resource consumption because they are a major part of a hospital. Thus, the optimal use of operating rooms can lead to high resource savings. However, because of the uncertainty of the operation procedures, it is difficult to arrange for the use of operating rooms in advance. In general, the durations of both surgery and anesthesia emergence determine the time requirements of operating rooms, and these durations are difficult to predict. In this study, we used an artificial neural network to construct a surgery and anesthesia emergence duration-prediction system. We propose an intelligent data preprocessing algorithm to balance and enhance the training dataset automatically. The experimental results indicate that the prediction accuracies of the proposed serial prediction systems are acceptable in comparison to separate systems.

Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types – one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty’s autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

Scheduling of Anaesthesia Operations in Operating Rooms

This paper considers scheduling of surgical operations across multiple operating rooms subject to the limited availability of anaesthetists. The objective is to construct a feasible operations schedule that has the minimum makespan, i.e., the completion time of all operations. We abstract the problem into a theoretical server scheduling problem and formulate it in a mathematical form by proposing an integer programming model. Due to the intractability of its computing time, we circumvent the exact approaches and develop two approximation methods. Then, the steepest descent search is adopted for improving the solutions. Computational study suggests that the proposed methods can produce quality solutions in a few seconds.

Preoperative Criteria Predict Operative Time Variability Within Tympanoplasty Procedures

OBJECTIVE

To identify preoperative patient and surgical parameters that predict operative time variability within tympanoplasty current procedural terminology (CPT) codes.

STUDY DESIGN

Retrospective.

SETTING

Tertiary referral center.

PATIENTS

One hundred twenty eight patients who underwent tympanoplasty (CPT code 69631) or tympanoplasty with ossicular chain reconstruction (69633) by a single surgeon over 3 years.

INTERVENTIONS

Procedures were preoperatively assigned a complexity modifier: Level 1 (small or posterior perforation able to be repaired via transcanal approach), Level 2 (large perforation or other factor requiring postauricular approach), or Level 3 (cholesteatoma or severe infection).

MAIN OUTCOME MEASURES

Total in-room time (nonoperative time plus actual operative time).

RESULTS

Consideration of preoperative parameters including surgical complexity, surgical facility, use of facial nerve monitoring, laser usage, resident involvement, revision surgery, and underlying patient characteristics (American Society of Anesthesiologists [ASA] score, body mass index [BMI]) accounted for up to 69% of surgical time variance. Across both CPT codes, surgical complexity levels accurately stratified operative times (p < 0.05). Total time was longer (by 30.0 min for 69631, 55.4 min for 69633) in Level 3 procedures compared with Level 2, while Level 1 cases were shorter (27.6, 33.9 min). Resident involvement added 25 and 32 minutes to total time (p < 0.02). Nonoperative preparation times were longer (22.1, 15.4 min) in the main hospital compared with ambulatory surgical center (p < 0.001).

CONCLUSIONS

There is significant surgical time variability within tympanoplasty CPT codes, which can be accurately predicted by the preoperative assignment of complexity level modifiers and consideration of patient and surgical factors. Application of complexity modifiers can enable more efficient surgical scheduling.

A Monte Carlo Simulation Approach to Optimizing Capacity in a High-Volume Congenital Heart Pediatric Surgical Center

Importance

Elective surgeries are primarily scheduled according to surgeon availability with less consideration of patients' postoperative cardiac intensive care unit (CICU) length of stay. Furthermore, the CICU census can exhibit a high rate of variation in which the CICU is operating at over-capacity, resulting in admission delays and cancellations; or under-capacity, resulting in underutilized labor and overhead expenditures.

Objective

To identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation.

Design

Monte Carlo simulation study of the daily and weekly CICU census at Boston Children's Hospital Heart Center. Data on all surgical admissions to and discharges from the CICU at Boston Children's Hospital between September 1, 2009 and November 2019 were included to obtain the distribution of length of stay for the simulation study. The available data allows us to model realistic length of stay samples that include short and extended lengths of stay.

Main Outcomes

Annual number of patient surgical cancellations and change in average daily census.

Results

We demonstrate that the models of strategic scheduling would result in up to 57% reduction in patient surgical cancellations, increase the historically low Monday census and decrease the historically higher late-mid-week (Wednesday and Thursday) censuses in our center.

Conclusions and Relevance

Use of strategic scheduling may improve surgical capacity and reduce the number of annual cancellations. The reduction of peaks and valleys in the weekly census corresponds to a reduction of underutilization and overutilization of the system.

Perceptions of UK clinicians towards postoperative critical care

Postoperative critical care is a finite resource that is recommended for high‐risk patients. Despite national recommendations specifying that such patients should receive postoperative critical care, there is evidence that these recommendations are not universally followed. We performed a national survey aiming to better understand how patients are risk‐stratified in practice; elucidate clinicians’ opinions about how patients should be selected for critical care; and determine factors which affect the actual provision of postoperative critical care. As part of the second Sprint National Anaesthesia Project, epidemiology of critical care after surgery study, we distributed a paper survey to anaesthetists, surgeons and intensivists providing peri‐operative care during a single week in March 2017. We collected data on respondent characteristics, and their opinions of postoperative critical care provision, potential benefits and real‐world challenges. We undertook both quantitative and qualitative analyses to interpret the responses. We received 10,383 survey responses from 237 hospitals across the UK. Consultants used a lower threshold for critical care admission than other career grades, indicating potentially more risk‐averse behaviour. The majority of respondents reported that critical care provision was inadequate, and cited the value of critical care as being predominantly due to higher nurse: patient ratios. Use of objective risk assessment tools was poor, and patients were commonly selected for critical care based on procedure‐specific pathways rather than individualised risk assessment. Challenges were highlighted in the delivery of peri‐operative critical care services, such as an overall lack of capacity, competition for beds with non‐surgical cases and poor flow through the hospital leading to bed ‘blockages’. Critical care is perceived to provide benefit to high‐risk surgical patients, but there is variation in practice about the definition and determination of risk, how patients are referred and how to deal with the lack of critical care resources. Future work should focus on evaluating ‘enhanced care’ units for postoperative patients, how to better implement individualised risk assessment in practice, and how to improve patient flow through hospitals.

Efficiency of Operating Room Processes for Elective Cataract Surgeries Done by Residents in a National University Hospital

Purpose

Operating room processes must be efficient to boost profitability and minimize cost while retaining surgical care quality. This study aims to assess operating room efficiency for resident-performed elective phacoemulsification surgeries done under local anesthesia by measuring different key performance indicators and comparing this with international benchmark data.

Patients and Methods

This is a prospective cross-sectional study done in the Department of Ophthalmology of the Philippine General Hospital, the National University Hospital. The operating room milestones were noted and recorded by a single third-party observer in randomly selected operating rooms from April to June 2019.

Results

Fifty-six phacoemulsification cases in randomly selected rooms fulfilling both inclusion and exclusion criteria were observed. None of the cases started on or before the scheduled 6:30 a.m. cutting time, with an average of 34 (SD 8.53) minutes late. Entry lag was above the median, while exit lag and turnover time were above the 95th percentile compared to benchmarking data. Segment analysis also showed an increased entry lag (35.11% vs 21.5%), significantly higher than benchmarks (t: 10.99, df: 55, p<0.01). Comparison with proposed targets in other studies also showed an increased time for entry lag.

Conclusion

This study determined that entry lag is the performance indicator that should be addressed to improve efficiency. A multidisciplinary approach and group goal-setting are needed to implement changes in the operating room.

Identifying nurse managers' essential information needs in daily unit operation in perioperative settings

Aim

To identify nurse managers' essential information needs in daily unit operation in perioperative settings.

Design

Qualitative and quantitative descriptive design.

Methods

The study consisted of (I) generation of an item pool of potential information needs, (II) assessment of the item pool by an expert panel and (III) confirming the essential information needs of nurse managers in daily unit operation with a survey (N = 288). Content validity index values were calculated for the assessments by expert panel and in the survey. Internal consistency of the final item pool was explored with Cronbach's alpha. The data were collected from 2011-2015.

Results

During the study process, the number of essential information needs decreased from 92-41. The final item pool consisted of 12 subthemes, and they were categorized into four main themes: patient's care process, surgical procedure, human resources and tangible resources. The findings can be used to create a knowledge map for information system purposes.

Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study

The achievement of health organizations’ goals is critically important for profitability. For this purpose, their resources, materials, and equipment should be efficiently used in the services they provide. A hospital has sensitive and expensive equipment, and the use of its equipment and resources needs to be balanced. The utilization of these resources should be considered in its operating rooms, as it shares both expense expenditure and revenue generation. This study’s primary aim is the effective and balanced use of equipment and resources in hospital operating rooms. In this context, datasets from a state hospital were used via the goal programming and constraint programming methods. According to the wishes of hospital managers, three scenarios were separately modeled in both methods. According to the obtained results, schedules were compared and analyzed according to the current situation. The hospital-planning approach was positively affected, and goals such as minimization cost, staff and patient satisfaction, prevention over time, and less use were achieved.

Facility Layout Planning with SHELL and Fuzzy AHP Method Based on Human Reliability for Operating Theatre

A well-design facility layout planning refers to the reduction of the operation cost in the manufacturing and service industry. This work consists of reliability analysis of facility layout for an operating theatre; it aims at proposing a new evaluation approach, which integrated the fuzzy analytic hierarchy process and human reliability tool, for optimization of facility layout design with safety and human factors in an operating theatre. Firstly, the systematic layout planning is used to design the layout schemes on the basis of field investigations. Then, the criteria system is proposed based on human reliability analysis from four perspectives: software, hardware, environment, and liveware. Finally, the fuzzy analytic hierarchy process, a fuzzy extension of the multicriteria decision-making technique analytic hierarchy process, is used to compare these layout schemes based on the criteria system. The results that are obtained reveal interesting properties of facility layout planning in hospitals. It reveals that decision in selecting a suitable layout must meet not only the strategies and goals of the system but also meet the safety, security, and reliability of the system.