Multi-objective Operating Room Planning and Scheduling

abstract: Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.Dissertation/ThesisPh.D. Industrial Engineering 201

Simulation Modelling in Healthcare: Challenges and Trends

AbstractIn this paper, we describe simulation models in healthcare that have been developed in the past two decades. Simulation systems, ranging from simulation of patient flow in emergency rooms to simulation of populations with a specific chronic diseases, are reviewed. Simulation types included discrete event simulation (DES) and agent based simulation (ABS). A trend of variability and scalability were identified, and discussed in terms of platform used to develop the model, data sources, and computational power needed to run the simulation. In the synthesis of simulation models, programming languages and products emerged as clusters. Design models and systems engineering development processes are examined with a focus on requirements discovery, models and scenarios of simulation. Graphic user interfaces in the simulation tools in healthcare are reviewed in terms of visual design and human factors. Furthermore, interaction modes and trends of information visualization techniques used for the simulations are reported. Agent-based simulation models in particular were reviewed, and findings suggest agent characteristics varied across literature researched in aspects such as socio-demographic design considerations

A Design Thinking Framework for Human-Centric Explainable Artificial Intelligence in Time-Critical Systems

Artificial Intelligence (AI) has seen a surge in popularity as increased computing power has made it more viable and useful. The increasing complexity of AI, however, leads to can lead to difficulty in understanding or interpreting the results of AI procedures, which can then lead to incorrect predictions, classifications, or analysis of outcomes. The result of these problems can be over-reliance on AI, under-reliance on AI, or simply confusion as to what the results mean. Additionally, the complexity of AI models can obscure the algorithmic, data and design biases to which all models are subject, which may exacerbate negative outcomes, particularly with respect to minority populations. Explainable AI (XAI) aims to mitigate these problems by providing information on the intent, performance, and reasoning process of the AI. Where time or cognitive resources are limited, the burden of additional information can negatively impact performance. Ensuring XAI information is intuitive and relevant allows the user to quickly calibrate their trust in the AI, in turn improving trust in suggested task alternatives, reducing workload and improving task performance. This study details a structured approach to the development of XAI in time-critical systems based on a design thinking framework that preserves the agile, fast-iterative approach characteristic of design thinking and augments it with practical tools and guides. The framework establishes a focus on shared situational perspective, and the deep understanding of both users and the AI in the empathy phase, provides a model with seven XAI levels and corresponding solution themes, and defines objective, physiological metrics for concurrent assessment of trust and workload

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

The role of simulator training for skills aquisition in coronary angiography

Introduction Coronary angiography (CA) is one of our most common invasive techniques in medicine today and is used to investigate coronary anatomy and pathology. The method is crucial and lifesaving in diagnosing acute coronary syndromes and so far not interchangeable to any other modality. The skills of performing a CA are compulsory for the general cardiologist according to present international guidelines but the methods for achieving these skills are not well defined. CA is a relatively safe procedure but complications occur, particularly during training. Simulators are proposed to be safe alternatives to achieve necessary skills but the methods for their use are not described. The aim of this thesis was to demonstrate that simulator training improve CA skills in real life. To be able to recommend simulators for skills acquisition, transferability from virtual reality to real life catheterization lab must be demonstrated, i.e. transfer validity. Methods and results Study I: The aim was to explore factors related to proficiency in CA and to construct learning curves to describe the improvement in CA skills over time. Swedish Coronary Angiography and Angioplasty Registry (SCAAR) was used to track experts and novel operators in CA and to compare their performances. Fluoroscopy time turned out to be the only solid marker for proficiency demonstrating a learning curve in the beginners group who reached expert level after 150 CAs. Complications were more frequent during training and were associated to fluoroscopy time. Study II: The concept of simulator constructs validity, i.e. to demonstrate that the simulator can measure the differences it is supposed to measure was explored in study II. Twentyfour participants with three different levels of proficiency in CA performed five consecutive virtual reality CAs each in the simulator. Three different levels of skills in the simulator were demonstrated that corresponded to their proficiency level. Beginners had a fourfold increased risk of errors compared to the experts assessed by evaluating video recordings of their performances. Study III: It was investigated if a structured simulator-based two day course in CA had any impact on the learning curve in CA. Twelve course participants continued to training in invasive cardiology and were tracked in SCAAR. Compared to a matched beginners group without simulator experience in SCAAR the virtual reality trained group demonstrated a less consistent improvement in fluoroscopy time previously discussed to be associated to proficiency. The complication rate was higher in the simulator trained group. Course transfer validity from virtual reality to real life was therefore rejected. Study IV: In this randomized study it was explored if proficiency based training in CA could transfer skills achieved in virtual reality to real world. Sixteen senior cardiology residents were randomized to preparatory simulator training or control. The simulator group practiced in mean 10 hours in a CA simulator. Both groups performed thereafter two consecutive CAs on patients. The simulator trained residents outperformed the conventional trained residents in quality and safety of the procedure and had shorter fluoroscopy time reflecting higher proficiency. Conclusion Simulator training improves the performance in CA during training. The strongest factor related to proficiency demonstrating a learning curve was fluoroscopy time. The Mentice VIST™ simulator can differentiate between CA skills in different proficiency levels. Particularly fluoroscopy time demonstrated to correspond well to real life conditions. A structured course in CA involving nonproficiency guided simulator practice in CA had no impact on the learning curve in CA but with an increased risk of complications. Proficiency based skills training in virtual reality CA was superior compared to conventional mentor-based training in real life CA both in quality and in safety thereby proving the concept of transfer validity

The Effects of Pattern Recognition Based Simulation Scenarios on Symptom Recognition of Myocardial Infarction, Critical Thinking, Clinical Decision-Making, and Clinical Judgment in Nursing Students

In the United States nearly 1 million annual new and recurrent myocardial infarctions (MI) occur with 10% of patients hospitalized with MI having unrecognized ischemic symptoms. Inexperienced nurses are expected to accurately interpret cardiac symptom cues, possibly without ever having experienced care of patients with MI, yet have been shown to be less able to classify symptom cues and reach accurate conclusions than experienced nurses. The purpose of this study was to test an educational intervention using theories of pattern recognition to develop CT in MI and improve nursing students’ clinical decision-making and clinical judgment using high fidelity patient simulation. This study used a quasi-experimental three group pre-/post-test design and qualitative data to triangulate information on critical thinking, clinical decision-making, and clinical judgment in MI. A sample of junior baccalaureate in nursing students (N = 54) from a large metropolitan university were divided in pairs and randomized to one of two control groups. Data were collected with instruments which measured pattern recognition in MI, critical thinking in MI, and self-perception of clinical decision-making. In addition, diagnostic efficiency and accuracy were measured. Triangulation on clinical decision making with semi-structured interviews using ‘thinking aloud’ technique was conducted. Data were analyzed as qualitative data and compared among groups. Students who were given prototypes for MI using simulation significantly improved critical thinking as measured by pattern recognition in MI (t(3.153(2), p = .038) compared with the non-simulation control group. Qualitative findings showed that students receiving the experimental simulation with a non-MI scenario and feedback-based debriefing had greatest gains in clinical reasoning which included development of clinical decision-making using analytic hypothetico-deductive and Bayesian reasoning processes and learned avoidance of heuristics. Students receiving the experimental simulation learned to identify salient symptom cues, analyzed data more complexly, and reflected on their simulation experience in a way which students reported improved learning. Students who were given MI only simulation scenarios developed deleterious heuristics and showed fewer gains in clinical reasoning, though both simulation groups demonstrated greater critical thinking ability than the non-simulation control group. Findings support the use of simulation to improve clinical reasoning including pattern recognition and clinical decision-making, and emphasize the significance of simulation scenario construction and debriefing to achieving learning outcomes. The findings could be used to guide further research to improve critical thinking, clinical decision-making, and clinical judgment in nursing students using simulation. Funding for this study was provided by the American Association of Critical Care Nurses and Philips Medical Systems and a testing grant from Elsevier, Assessment

Pediatric Simulation and the Development of Entry Level Safe Practice in Nursing Students

Simulation offers an important alternative for clinical education, it provides a safe practice environment and allows for high instructor control of the environment. The purpose of the study was to investigate how simulation contributes to the development of entry-level safe practice in junior level baccalaureate nursing students. Entry-level clinical safe practice was measured using the Educational Resource Incorporated Nursing Care of Children exam. The study used an experimental approach with 26 students in a clinical experience and 25 students in a clinical/simulation mix experience. A mixed model ANOVA was used to compare the group means of the post test. There were no significant differences found on any measures of entry-level safe practice between students who received a 100% clinical rotation and students who received a 20/80 simulation/clinical mix. This finding is significant to nursing education, primarily because it demonstrates that clinical in the pediatric setting can be simulated at least inSchool of Teaching and Curriculum Leadershi

Improving surgeon utilization in an orthopedic department using simulation modeling

Purpose: Worldwide more than two billion people lack appropriate access to surgical services due to mismatch between existing human resource and patient demands. Improving utilization of existing workforce capacity can reduce the existing gap between surgical demand and available workforce capacity. In this paper, the authors use discrete event simulation to explore the care process at an orthopedic department. Our main focus is improving utilization of surgeons while minimizing patient wait time. Methods: The authors collaborated with orthopedic department personnel to map the current operations of orthopedic care process in order to identify factors that influence poor surgeons utilization and high patient waiting time. The authors used an observational approach to collect data. The developed model was validated by comparing the simulation output with the actual patient data that were collected from the studied orthopedic care process. The authors developed a proposal scenario to show how to improve surgeon utilization. Results: The simulation results showed that if ancillary services could be performed before the start of clinic examination services, the orthopedic care process could be highly improved. That is, improved surgeon utilization and reduced patient waiting time. Simulation results demonstrate that with improved surgeon utilizations, up to 55% increase of future demand can be accommodated without patients reaching current waiting time at this clinic, thus, improving patient access to health care services. Conclusion: This study shows how simulation modeling can be used to improve health care processes. This study was limited to a single care process; however the findings can be applied to improve other orthopedic care process with similar operational characteristics. Keywords: waiting time, patient, health care processpublishedVersio