The development and practical application of a simulation model to inform musculoskeletal service delivery in an Australian public health service

Comans, Tracy; Chang, Angela; Standfield, Lachlan; Knowles, Dylan; O’Leary, Shaun; Raymer, Maree

doi:10.1016/j.orhc.2017.07.002

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…We adapted each model to reflect the local variations of each service. Comprehensive details of the modelling software and pathways used and refined in two previous projects have been published [5, 16]. We populated each model with extensive local service-specific data.…”

Section: Methodsmentioning

confidence: 99%

“…reduce queues or waiting times) in complex healthcare systems [6, 11, 13, 14]. Discrete event simulation models are built to support operational decision-making, resource allocation and optimisation and planning decisions [15] that has application in service planning for challenging health problems such as musculoskeletal conditions [5, 16]. Discrete event simulation has been used successfully to identify the most efficient and cost-effective scale and professional mix of services required to achieve waiting time targets in orthopaedic and neurosurgical outpatient services [5, 16].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

et al. 2019

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Background Advanced physiotherapist-led services have been embedded in specialist orthopaedic and neurosurgical outpatient departments across Queensland, Australia, to ameliorate capacity constraints. Simulation modelling has been used to inform the optimal scale and professional mix of services required to match patient demand. The context and the value of simulation modelling in service planning remain unclear. We aimed to examine the adoption, context and costs of using simulation modelling recommendations to inform service planning. Methods Using an implementation science approach, we undertook a prospective, qualitative evaluation to assess the use of discrete event simulation modelling recommendations for service re-design and to explore stakeholder perspectives about the role of simulation modelling in service planning. Five orthopaedic and neurosurgical services in Queensland, Australia, were selected to maximise variation in implementation effectiveness. We used the consolidated framework for implementation research (CFIR) to guide the facilitation and analysis of the stakeholder focus group discussions. We conducted a prospective costing analysis in each service to estimate the costs associated with using simulation modelling to inform service planning. Results Four of the five services demonstrated adoption by inclusion of modelling recommendations into proposals for service re-design. Four CFIR constructs distinguished and two CFIR constructs did not distinguish between high versus mixed implementation effectiveness. We identified additional constructs that did not map onto CFIR. The mean cost of implementation was AU$34,553 per site (standard deviation = AU$737). Conclusions To our knowledge, this is the first time the context of implementing simulation modelling recommendations in a health care setting, using a validated framework, has been examined. Our findings may provide valuable insights to increase the uptake of healthcare modelling recommendations in service planning. Electronic supplementary material The online version of this article (10.1186/s13012-019-0923-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

et al. 2019

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“…Eleven papers specifically stated other methods of collecting the information on the pathway, which are as follows - [19,22,41,82,116,169] stated that the information was provided to them in some way, [79] through previous work (also consulted with experts and stakeholders), [128] online user input, and [21,24,60] used national guidelines.…”

Section: Obtaining the Pathwaymentioning

confidence: 99%

“…Obtained Data Driven [5, 7-9, 11, 13, 32, 33, 37, 47-49, 54, 58, 62, 66, 70-72, 74-76, 80, 81, 83, 85-99, 104, 107, 108, 112, 120, 121, 124, 126, 127, 131, 133, 134, 138-140, 143, 144, 147-150, 153, 157, 160, 162, 167, 170, 172, 177-179, 183-185, 191, 192, 195, 197-206, 209-211] Collaboration [3,10,14,17,27,42,51,73,100,114,142,152,163] Both [12,20,35,40,56,101,103,129,136,146,154,166,171,186] Other [19,21,22,24,41,60,79,82,116,128,169] Table A6. Care level considered.…”

Section: Fundingmentioning

confidence: 99%

“…Investigating Type Mapping (Ma) [1, 4, 5, 7, 22, 29, 33, 37, 42, 47, 48, 54, 56, 57, 70, 71, 81, 83, 87, 88, 90, 91, 94-99, 104, 112, 121, 126, 127, 131, 136, 138, 139, 144, 149, 154, 157, 161, 162, 164, 170, 172, 177-179, 183-185, 192, 195, 197-203, 206, 209-211] Modelling (Mo) [15, 19-21, 23, 24, 30, 31, 36, 39, 43, 50, 55, 58, 64, 67, 76, 79, 82, 86, 89, 102, 103, 115, 117-119, 122, 129, 145-147, 150, 151, 160, 169, 171, 187, 188, 190, 191, 196, 212] Improving (I) [13,46] Ma & Mo [3, 9, 11, 17, 18, 40, 60, 62, 66, 72-74, 80, 85, 90, 93, 101, 107, 108, 114, 120, 124, 125, 128, 133, 134, 140, 143, 158, 205] Mo & I [8,16,25,32,34,41,45,52,53,68,75,110,111,113,116,141,148,165,182] All Types [10,12,14,27,35,49,51,100,142,152,153,163,166,167,186,204] Table A12. Outcome focus of the pathway.…”

Section: Fundingmentioning

confidence: 99%

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Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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Reducing Waiting Time for Orthopaedic Consultation Through a Continuous Improvement Approach

Dinis‐Carvalho

Vieira

Lima

et al. 2021

Innovations in Industrial Engineering

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The development and practical application of a simulation model to inform musculoskeletal service delivery in an Australian public health service

Cited by 14 publications

References 13 publications

Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

Clinical pathway modelling: a literature review

Reducing Waiting Time for Orthopaedic Consultation Through a Continuous Improvement Approach

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