Is simulation in health different?

Tako, Antuela A.; Robinson, Stewart

doi:10.1057/jors.2014.25

Cited by 39 publications

(20 citation statements)

References 27 publications

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“…Cultural, practical and political aspects have been identified to explain this situation (Jahangirian et al 2015;Tako and Robinson 2014). Overall, modellers agree that "simulation in healthcare is different" (Tako and Robinson 2014). The same discussions have occurred regarding lean management (Young and McClean 2008;Radnor, Holweg, and Waring 2012).…”

Section: Transferring Industrial Approaches To Healthcare Organisationsmentioning

confidence: 91%

“…Jahanagirian et al (2012) found that only 8% of simulation papers in healthcare involved "a significant level of user/stakeholder engagement in the simulation part", as opposed to "hypothetical Problem-Solving papers" or "methodological papers", whereas "real problem solving papers" accounted for 49% of the simulation papers in commerce and 39% in defense. Cultural, practical and political aspects have been identified to explain this situation (Jahangirian et al 2015;Tako and Robinson 2014). Overall, modellers agree that "simulation in healthcare is different" (Tako and Robinson 2014).…”

Section: Transferring Industrial Approaches To Healthcare Organisationsmentioning

confidence: 99%

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Position Paper: On Design Research Engaging With Healthcare Systems

Lamé¹

2018

Proceedings of the DESIGN 2018 15th International Design Conference

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This position paper intends to trigger a debate on the engagement of the design research community with healthcare systems. We analyse the specificities of healthcare systems design and of healthcare organisations. We discuss the focus on evaluation and the evidence-based paradigm in healthcare, and argue that design research currently is ill-prepared to meet such expectations. Dedicated research attention in healthcare seems necessary to bridge this gap. It could enrich design research through multidisciplinary collaborations, and enable design to make a precious contribution to healthcare.

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Section: Transferring Industrial Approaches To Healthcare Organisationsmentioning

confidence: 91%

Section: Transferring Industrial Approaches To Healthcare Organisationsmentioning

confidence: 99%

Position Paper: On Design Research Engaging With Healthcare Systems

Lamé¹

2018

Proceedings of the DESIGN 2018 15th International Design Conference

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“…This includes the amount and type of information that the software can process, and the judgements involved in deciding what information to include. This is aggravated by the complex structure of hospital operations [28]. For instance, dividing patients into different types comes as a result of the difficulty in taking into consideration all patient illnesses both in terms of software capability and the ability to form a valid or accurate model.…”

Section: Model Limitationsmentioning

confidence: 99%

Modeling the critical care pathway for cardiothoracic surgery

Bahou

Fenwick

Anderson

et al. 2017

Health Care Manag Sci

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The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type's pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique with minor capacity reallocations resulted in more than 60% drop in cancellations.

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“…In a survey among simulation practitioners Tako and Robinson (2015) discovered also a number of other differences between simulation in health care and other sectors. Among these differences were more complex systems with less evident structure and messier problems, difficulties in collecting and accessing data, difficulties in developing generic models and ensuring implementation.…”

Section: Simulation In Health Carementioning

confidence: 99%

Towards Increased Use of Discrete-Event Simulation for Hospital Resource Planning

Steins¹

2017

Linköping Studies in Science and Technology. Dissertations

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Health care systems in many countries are experiencing a growing demand while their resources remain limited. The discrepancy between demand and capacity creates many problems -long waiting times for treatment, overcrowding in hospital wards, high workload, etc. More efficient delivery of health care services can be achieved by better planning of its resources so that the mismatch between demand and capacity is minimized. Planning health care resources, including hospital resources, is difficult due to system complexity and variability in both resource availability and demand. Discrete-event simulation and other operational research methods can be used for solving planning problems in health care, and have been gaining increased attention from researchers during recent decades. Despite the growing number of academic publications, simulation appears to be less used in health care than in other application areas and only a small proportion of simulation studies is actually implemented.The aim of this thesis is to contribute to increased use of discrete-event simulation in hospital resource planning. The separate studies regarding intensive care unit capacity planning, operating room allocation strategies and the management of emergency patient flow in a radiology department highlight both the possibilities and the requirements for practical application of discrete-event simulation in hospital resource planning. The studies are described in five papers.In the first paper, the relationship between intensive care unit (ICU) occupancy and patient outcomes was investigated and the results showed that risk adjusted mortality was higher in the group of patients who were treated during high levels of occupancy. This indicates that appropriate planning of ICU resources is necessary to avoid adverse effects on patient outcomes.In the second paper, analysis of a relatively simple care chain consisting of two hospital departments -emergency and radiology -revealed a process that was not very well defined and measured. Investigation into data availability uncovered disparate information systems storing incompatible and fragmented data. It suggests that the current degree of process orientation and the current IT infrastructure does not enable efficient use of quantitative process analysis and management tools such as simulation.In the third paper, the value and possibilities of using simulation modelling in hospital resource planning were examined through the development and use of a simulation model for improved operating room time allocation and patient flow in a hospital operating department. The model was initially used for studying overcrowding in a post-anaesthesia care unit. Advanced planning logic implemented in the model enabled evaluation of several different scenarios aiming to improve the utilization of operating room resources. The results showed that it is possible to achieve slightly better and more even resource utilization, as well as provide greater flexibility in scheduling operations.In the fourth p...

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Is simulation in health different?

Cited by 39 publications

References 27 publications

Position Paper: On Design Research Engaging With Healthcare Systems

Position Paper: On Design Research Engaging With Healthcare Systems

Modeling the critical care pathway for cardiothoracic surgery

Towards Increased Use of Discrete-Event Simulation for Hospital Resource Planning

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