A patient stochastic model to support human resource planning in home care

Lanzarone, Ettore; Matta, Andrea; Scaccabarozzi, Gianlorenzo

doi:10.1080/09537280903232362

Cited by 60 publications

(61 citation statements)

References 36 publications

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“…For instance, the presence or absence of familiars in the home affects the delivery of home cleaning services and the administration of certain specific therapies. Lanzarone et al (2010) proposed a procedure to build Markovian models for the estimation of the patientrelated workload and care pathway to be used in human resource planning. This topic has also been recently investigated by Garg et al (2010b).…”

Section: An Idef0 Model For Describing Home Care Processes 241 Manamentioning

confidence: 99%

Modelling home care organisations from an operations management perspective

et al. 2012

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Home Care (HC) service consists of providing care to patients in their homes. During the last decade, the HC service industry experienced significant growth in many European countries. This growth stems from several factors, such as governmental pressure to reduce healthcare costs, demographic changes related to population ageing, social changes, an increase in the number of patients that suffer from chronic illnesses, and the development of new home-based services and technologies. This study proposes a framework that will enable HC service providers to better understand HC operations and their management. The study identifies the main processes and decisions that relate to the field of HC operations management. Hence, an IDEF0 (Integrated Definition for Function Modelling) activity-based model describes the most relevant clinical, logistical and organisational processes associated with HC operations. A hierarchical framework for operations management decisions is also proposed. This analysis is derived from data that was collected by nine HC service providers, which are located in France and Italy, and focuses on the manner in which operations are run, as well as associated constraints, inputs and outputs. The most challenging research areas in the field of HC operations management are also discussed.

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Section: An Idef0 Model For Describing Home Care Processes 241 Manamentioning

confidence: 99%

Modelling home care organisations from an operations management perspective

et al. 2012

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“…We consider data from one of the largest Italian public Home Care providers; data from this provider have already been analyzed according to frequentist [5] and Bayesian approaches [6].…”

Section: Exploratory Analysis Of the Hc Datasetmentioning

confidence: 99%

“…We started the analysis considering the same dataset as in [5]. The time horizon is pretty wide, i.e., 252 weeks long, from January 2004 to March 2008.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…In addition, many random events may affect the service delivery and mine the feasibility of plans; see [1,2,3,4]. As mentioned above, the most relevant randomness sources are patients' health conditions, which may determine a different demand for visits than the planned one, as reported in [5]. Hence, reliable estimation tools of future demands for visits required by the patients in the charge represent useful instruments to increase the quality of HC planning and service.…”

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“…For instance, Blanco [15] proposes a multi-state Markov model to estimate the cost of care provisioning to elderly people in order to help governments in efficiently and effectively allocating resources, whereas Gardiner et al [16] adopt a Markov model to estimate the transition probabilities between health statuses to asses total treatment costs for cancer patients. On the other hand, for a HC dataset similar to the one we analyze here, Lanzarone et al [5] proposes a frequentist approach based on Markov chains associated with a cost probability density function for the number of required visits in each state.…”

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

et al. 2014

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This is the author's final version of the contribution published as:Argiento, R.; Guglielmi, A.; Lanzarone, E.; Nawajah, I. planning decisions related to service delivery in the territory must be taken. With the goal of helping home care management to take robust decisions, in this paper we propose a Bayesian model for estimating and predicting both the demand for care and the history of health conditions for patients in the charge of a home care service. In particular, we jointly model the temporal evolution of patients' care profile and the weekly number of visits required to nurses, and use a Markov chain Monte Carlo algorithm to compute posterior inference and prediction. The model is applied to data of one of the largest Italian home care providers, obtaining small prediction errors.

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