A multi-phase DES modelling framework for patient-centred care

Gillespie, Jennifer Z.; McClean, Sally; Garg, Lalit; Barton, Maria; Scotney, Bryan; Fullerton, Ken

doi:10.1057/jors.2015.114

Cited by 28 publications

(19 citation statements)

References 42 publications

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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%

“…JCR Category MI [3, 11, 13, 14, 16, 17, 19, 32, 37, 42, 45-47, 52, 54, 56, 57, 66, 72, 73, 1-93, 95-97, 99-101, 108, 122, 124, 131, 140, 144, 149, 150, 157, 160, 164, 170, 178, 187, 196, 197, 206, 211] OR/MS [7,8,12,18,22,23,27,30,31,43,50,51,64,75,79,90,113,114,134,151,158,165,169,190,203,212] HPS [9, 10, 21, 33, 39, 40, 80, 102-104, 110, 116, 141, 143, 153, 163, 166, 209] IE [34,120] AN [53,133] No ISSN [15,24,36,60,70,71,82,127,138,139,142,161,183,[198][199][200] Table A4. Way in which information about the pathway was obtained.…”

Section: Fundingmentioning

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%

“…Outcome Pathway Mapping [1, 4, 5, 13, 20, 33, 37, 42, 47, 54-56, 58, 62, 70, 71, 81-83, 86-99, 107, 112, 114, 115, 121, 126-128, 136, 138-140, 149, 154, 157, 162, 164, 171, 172, 177-179, 183, 184, 187, 188, 192, 195, 197-202, 206, 210, 211] Time [3, 8, 10, 12, 15, 16, 18, 23, 29-32, 34-36, 41, 46, 50, 52, 57, 62, 64, 67, 68, 72, 74, 75, 80, 85, 94, 100, 108, 110, 118, 129, 133, 138, 141-143, 145-147, 151, 160, 161, 163, 165-167, 169, 182, 185, 190, 204, 205, 212] Resource [3, 7, 10, 14, 15, 17, 25, 27, 29, 31, 32, 35, 36, 46, 52, 64, 75, 76, 100, 113, 118-120, 142, 145, 148, 151-153, 160, 161, 163, 170, 182, 191, 204] Cost [9,11,14,15,19,21,22,40,43,45,53,60,73,79,101,111,117,118,125,133,134,138,145,158,166,167,209] Patient Progression [24, 29, 39, 40, 45, 48, 49, 51, 60, 79, 86, 101-104, 107, 116, 122, 124, 131, 139, 144, 145, 150, 186, 196] Legal [66,203] Table A14. Planning decision level taxonomy.…”

Section: Fundingmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Obtaining the Pathwaymentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

Section: Fundingmentioning

confidence: 99%

See 2 more Smart Citations

Clinical pathway modelling: a literature review

2019

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“…Demir, Lebcir, and Adeyemi (2014) modelled length of stay using continuous time Markov chain in the context of neonatal care. Gillespie et al (2016) used a phase type distribution (Markov chain) to model the entire patient journey for a specific treatment.…”

Section: Flat Rate Pricing Modeltheory Developmentmentioning

confidence: 99%

Optimal package pricing in healthcare services

Tanwar

Kumar

Mustafee

2019

Journal of the Operational Research Society

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Fixed pricing for healthcare services is emerging as an attractive business model for private healthcare service providers. Under fixed pricing (or flat rate) contract, the patient is charged a fixed price for the healthcare services irrespective of the actual cost incurred by the hospital. Such contracts increase the risks for the healthcare service provider, thus making pricing decision crucial. In this paper, we study uncertainty and analyse the flat rate pricing contract for a profit maximising hospital to find the optimal price of treatment and examined value-at-risk (VaR) associated with such contracts for a risk minimising hospital. Bounds on price were derived to support healthcare providers with price negotiations. We extended the basic models by adding constraints to obtain risk-adjusted optimal price. We proved analytically that the optimal price lies between profit maximisation value and risk minimisation value of price, which we refer to as the efficient pricing interval. Our models and insights provide practical support to private healthcare service providers for optimal pricing and keep them informed about their risk position. ARTICLE HISTORY

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An alternative formulation of Coxian phase‐type distributions with covariates: Application to emergency department length of stay

Rizk

Walsh

Burke

2021

Statistics in Medicine

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In this article, we present a new methodology to model patient transitions and length of stay in the emergency department using a series of conditional Coxian phase‐type distributions, with covariates. We reformulate the Coxian models (standard Coxian, Coxian with multiple absorbing states, joint Coxian, and conditional Coxian) to take into account heterogeneity in patient characteristics such as arrival mode, time of admission, and age. The approach differs from previous research in that it reduces the computational time, and it allows the inclusion of patient covariate information directly into the model. The model is applied to emergency department data from University Hospital Limerick in Ireland, where we find broad agreement with a number of commonly used survival models (parametric Weibull and log‐normal regression models and the semiparametric Cox proportional hazards model).

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A multi-phase DES modelling framework for patient-centred care

Cited by 28 publications

References 42 publications

Clinical pathway modelling: a literature review

Clinical pathway modelling: a literature review

Optimal package pricing in healthcare services

An alternative formulation of Coxian phase‐type distributions with covariates: Application to emergency department length of stay

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