Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this recordA symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.Erasmus

Real-Time Simulation As A Way To Improve Daily Operations In An Emergency Room

Emergency Rooms (ER) are resource constrained systems that demand efficient management in order to fulfill their care and service objectives. This article explores the use of real-time simulation to improve daily operations at an ER where unplanned events may occur. Such a simulation model must adequately portray the current state of the system; however ER workflow management systems often provide only limited information for this purpose. This paper studies the impact of the model\u27s ability to predict ER performance with a limited amount of input information, such that what-if questions may be asked to guide decision making. Towards this end, two input data scenarios are compared to a case of perfect information. One scenario considers only patient arrival times and the other assumes additional knowledge of patient care pathways. Although both generate similar performance measures, the case with least information yields a slightly worse estimate of patient care pathway composition

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Online simulation for the operational management of inpatient beds

In many modern hospitals, resources such as beds, theatre time, medical equipment and staff are shared between patients who require immediate care and must be dealt with as they arrive (emergency patients), and those whose care requirements are known to the hospital some time in advance (elective patients). Caring for these two types of patients poses a logistical challenge, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. Failing to strike this balance can result in negative outcomes, such as patient-stays on non-ideal wards, or increased waiting time for elective procedures (in the case of public health services). The potential benefits of using discrete event simulation (DES) models in healthcare are well established, and they are often preferred to other modelling approaches because of their ability to emulate the randomness seen in real systems, at a level of detail which is necessary for models to be convincing. However, their use is often limited to strategic or tactical decision making, and few have attempted to produce models which can help hospitals with short-term (operational) decision making. This is where Online Discrete Event Simulation (ODES) can help. An ODES (also known as symbiotic simulation) takes all the components of a DES model, and adds the ability to load the state of the real system at run-time to make predictions about how the real system might evolve in the short-term. This thesis reports the development of a whole-hospital, proof-of-concept ODES to assess the impact of elective admissions decisions, on wards which are shared with emergency patients. The model is parameterised by analysing 18 months of patient administrative data from an Australian General Hospital. Since ODES is a relatively new method, this research focuses on formalising the model development process, resulting in a new “black-box” validation method for handling conditionally distributed simulation outputs. Additionally, a new probabilistic routing method is developed to better represent inter-ward dependencies during peaks in bed demand. A statistical analysis of the relationship between ward transfers and ward occupancy is conducted on real hospital data to parameterise so-called “Dynamic Transition Matrices” for this purpose. Finally, the ODES is used to demonstrate how additional patient-level information (which might only become available after admission) can affect the predicted bed census. Clinicians’ discharge date estimates fit this criterion, and the case is made for more scientific use of this type of information, as part of an operational ODES model