Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems

Garg, Lalit; McClean, Sally; Barton, Maria; Meenan, Brian J.; Fullerton, Ken

doi:10.1109/tsmca.2012.2210211

Cited by 50 publications

(51 citation statements)

References 63 publications

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“…Moments of costs of patient care in future time periods can thus be estimated and used for patient prognostication and health service planning. The current paper also extends our previous result for the mean numbers of patients in future states [5]. In this paper we provide the Moment Generating Function (MGFs) of total cost in time (0, t] for an individual and a cohort of patients moving through a Mixed Coxian PHD.…”

Section: Introductionsupporting

confidence: 65%

Costing Mixed Coxian Phase-type Systems in a given time interval

McClean

Garg

Gillespie

et al. 2013

Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems

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Previously we have introduced a modelling framework to classify individuals in Mixed Coxian Phase-type Systems. We here add costs and obtain results for moments of total costs in (0, t], for an individual, and a cohort arriving at time zero. Based on data from the Belfast City Hospital Stroke Unit we use the overall modelling framework to obtain results for total cost in a given time interval to facilitate planners who have limited time horizons for budget planning.

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Section: Introductionsupporting

confidence: 65%

Costing Mixed Coxian Phase-type Systems in a given time interval

McClean

Garg

Gillespie

et al. 2013

Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems

Self Cite

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“…In doing so, operand throughput and system efficiency in terms of cost and time can be quantitatively assessed and maximized. Consequently, a large body of healthcare literature summarizes safety [2], [3], resource management [4], capacity planning [5], [6], and scheduling of various types including for outpatients [7]- [10], clinicians [11]- [13], operating room [14]- [16]), work flow processes [17]- [20] and patient-flows [21].…”

Section: A Dynamic Modeling Of Healthcare Delivery Systemsmentioning

confidence: 99%

Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

Khayal

Farid

2018

Complexity

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The current healthcare system is facing an unprecedented chronic disease burden. This paper develops a healthcare dynamic model for personalized healthcare delivery and managed individual health outcomes. It utilizes a hetero-functional graph theory rooted in Axiomatic Design for Large Flexible Engineering Systems and Petri nets. The dynamics of the model builds upon a recently developed systems architecture for healthcare delivery which bears several analogies to the architecture of masscustomized production systems. At its essence, the model consists of two synchronized Petri nets; one for the healthcare delivery system and another for individuals' health state evolution. The model is demonstrated on two clinical case studies; one acute and another chronic. Together, the case studies show that the model applies equally to the care of both acute and chronic conditions, transparently describes health outcomes and links them to the evolution of the healthcare delivery system and its associated costs. Index Terms-systems framework; dynamic model healthcare delivery system; personalized care; individual health outcomes; arXiv:1910.09104v1 [eess.SY]

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“…The essential to minimize the rising cost of care through ensuring convenience of rare health resources and optimum utilization is effective resource necessity forecasting [35]. Covariates are used for the better in hospital capability planning and it will help the patient's predictable destination after release.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Patient Inflow Prediction Model for Hospital Resource Management

Srikanth

Arivazhagan

2017

IJSC

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There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

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Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems

Cited by 50 publications

References 63 publications

Costing Mixed Coxian Phase-type Systems in a given time interval

Costing Mixed Coxian Phase-type Systems in a given time interval

Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

An Efficient Patient Inflow Prediction Model for Hospital Resource Management

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