Optimisation-Based on Simulation: A Diagnostic Imaging Department Case-Study

Granja, Conceição; Mendes, João; Janela, Filipe; Soares, João; Mendes, Adélio

doi:10.1109/eknow.2010.26

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…After targeting any article that combined the terms ''patient flow'' with ''diagnostic imaging'', 14 useful articles were retrieved. 1,2,[4][5][6][7][8][9][10][11][12][13][14][15] Reviewing the literature provided a wealth of knowledge and a multitude of frameworks for analysing and improving patient flow. Additionally, several gaps in the literature were identified, including the possible benefit(s) of utilizing personnel such as Technologist Assistants (TA) or DIdedicated porters.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving patient flow in diagnostic imaging: a case report

Jessome

2020

Journal of Medical Imaging and Radiation Sciences

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Introduction: This case study focuses on Erie Shores Healthcare, a small Canadian hospital with a busy emergency department (ED) who acts as the sole provider of outpatient diagnostic imaging (DI) services to the community. The hospital is experiencing bottlenecks when balancing outpatient diagnostic procedures with inpatient and urgent ED requests in the X-Ray department, creating the need for increased overtime and missed breaks, as well as frustrations amongst patients, staff and physicians. Case and Outcomes: To alleviate these issues and improve patient flow, this case study aims to identify options for increasing efficiency, improving adaptive workflow and decreasing wait times during peak hours in X-Ray. Discussion: After a literature review, key components were narrowed down to include the following Lean Methods: floor plan evaluation with spaghetti diagrams, collection of benchmarking data from similar Canadian sites, and a real-time Client Flow Analysis. The potential benefits of Technologist Assistants (TA) and DI-dedicated porters are also explored.

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Section: Literature Reviewmentioning

confidence: 99%

Improving patient flow in diagnostic imaging: a case report

Jessome

2020

Journal of Medical Imaging and Radiation Sciences

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“…In general, extended waiting time is a common problem in health environments with highly demanded shared facilities (5,6). Simulation techniques such as discrete event simulation (DES) help to identify bottlenecks causing waiting times and understand and improve health protocols (7).…”

Section: Introductionmentioning

confidence: 99%

“…Johnston et al (9) and Werker et al (11) for example classified different patients types but did not applied optimisation analysis to compare workflow scenarios as Granja et al (5) or Nickel and Schmidt (8) did. However, in these studies, decision points and tasks were modelled at a departamental level.…”

Section: Introductionmentioning

confidence: 99%

Workflow optimisation for multimodal imaging procedures: a case of combined X-ray and MRI-guided TACE

Fernández‐Gutiérrez

Wolska-Krawczyk

Buecker

et al. 2016

Minimally Invasive Therapy & Allied Technologies

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This study presents a framework for workflow optimisation of multimodal image-guided procedures (MIGP) based on discrete event simulation (DES). A case of a combined X-Ray and magnetic resonance image-guided transarterial chemoembolisation (TACE) is presented to illustrate the application of this method. We used a ranking and selection optimisation algorithm to measure the performance of a number of proposed alternatives to improve a current scenario. A DES model was implemented with detail data collected from 59 TACE procedures and durations of magnetic resonance imaging (MRI) diagnostic procedures usually performed in a common MRI suite. Fourteen alternatives were proposed and assessed to minimise the waiting times and improve workflow. Data analysis observed an average of 20.68 (7.68) min of waiting between angiography and MRI for TACE patients in 71.19% of the cases. Following the optimisation analysis, an alternative was identified to reduce waiting times in angiography suite up to 48.74%. The model helped to understand and detect 'bottlenecks' during multimodal TACE procedures, identifying a better alternative to the current workflow and reducing waiting times. Simulation-based workflow analysis provides a cost-effective way to face some of the challenges of introducing MIGP in clinical radiology, highligthed in this study.

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“…In this work, a hospital scenario was studied and patient pathway was modelled to consider different phases of care, such as acute, treatment and rehabilitation. In the flow modelled by the Granja et al [7], three stages were considered in the patient pathway in radiology admission, examination and billing. The patience path way is mapped with queuing system is depicted in Fig.…”

Section: Mathematical Representation and Problem Descriptionmentioning

confidence: 99%