A Bayesian framework for describing and predicting the stochastic demand of home care patients

Argiento, Raffaele; Guglielmi, Alessandra; Lanzarone, Ettore; Nawajah, Inad

doi:10.1007/s10696-014-9200-4

Cited by 17 publications

(20 citation statements)

References 42 publications

(27 reference statements)

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“…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%

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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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Section: Fundingmentioning

confidence: 99%

“…Multiple Methods Two [8,11,12,18,54,74,76,99,108,140,151,162,167,179,190,203] Three [7,211] Table A11. Investigating type.…”

Section: Fundingmentioning

confidence: 99%

Clinical pathway modelling: a literature review

2019

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“…Hu (2015) applied the MC model to estimate the elderly health state transition probability, and combined this with the result of population prediction to forecast the demand of elderly and nursing services in different years and different disability states [ 30 ]. Argiento et al (2016) attempted to improve the Bayesian framework by applying the MC model, and developed the patients’ demand evolution for the further prediction [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation

Duan

Jiao

Qishan

et al. 2017

IJERPH

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The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

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“…In order to prevent these drastic changes, some authors (Shishebori and Babadi, 2015) have studied the robust approach to protect the network against changes. In another research (Argiento et al, 2014), these elements are introduced in the design of the system. Staff dimensioning in traditional hospital is less critical than in HHC structures for two main reasons related to the close proximity of patients: i) activities can easily be shared or exchanged in case of employees shortage; ii) working time can be saved by small reductions in the time spent for each care.…”

Section: Introductionmentioning

confidence: 99%

Staff dimensioning in homecare services with uncertain demands

Rodríguez¹,

Garaix²,

Xie³

et al. 2015

International Journal of Production Research

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International audienceThe problem addressed in this paper is how to calculate the amount of personnel required to ensure the activity of a home health care (HHC) centre on a tactical horizon. Design of quantitative approaches for this question is challenging. The number of caregivers has to be determined for each profession in order to balance the coverage of patients in a region and the workforce cost over several months. Unknown demand in care and spatial dimensions, combination of skills to cover a care and individual trips visiting patients make the underlaying optimisation problem very hard. Few studies are dedicated to staff dimensioning for HHC compared to patient to nurses assignment/sequencing and centres location problems. We propose an original two-stage approach based on integer linear stochastic programming, which exploits historical medical data. The first stage calculates (near-)optimal levels of resources for possible demand scenarios, while the second stage computes the optimal number of caregiver for each profession to meet a target coverage indicator. For decision-makers, our algorithm gives the number of employees for each category required to satisfy the demand without any recourse (overtime, external resources) with fixed probability and confidence interval. The approach has been tested on various instances built from data of the French agency of hospitalisation data (ATIH)

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A Bayesian framework for describing and predicting the stochastic demand of home care patients

Cited by 17 publications

References 42 publications

Clinical pathway modelling: a literature review

Clinical pathway modelling: a literature review

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation

Staff dimensioning in homecare services with uncertain demands

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