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

Home care providers are complex structures which include medical, paramedical and social services delivered to patients at their domicile. High randomness affects the service delivery, mainly in terms of unplanned changes in patients’ conditions, which make the amount of required visits highly uncertain. Hence, each reliable and robust resource planning should include the estimation of the future demand for visits from the assisted patients. In this paper, we propose a Bayesian framework to represent the patients’ demand evolution along with the time and to predict it in future periods. Patients’ demand evolution is described by means of a generalized linear mixed model, whose posterior densities of parameters are obtained through Markov chain Monte Carlo simulation. Moreover, prediction of patients’ demands is given in terms of their posterior predictive probabilities. In the literature, the stochastic description of home care patients’ demand is only marginally addressed and no Bayesian approaches exist to the best of our knowledge. Results from the application to a relevant real case show the applicability of the proposed model in the practice and validate the approach, since parameter densities in accordance to clinical evidences and low prediction errors are found

Changes in aortic pulse wave velocity of four thoracic aortic stent grafts in an ex vivo porcine model

OBJECTIVES: Thoracic endovascular aortic repair (TEVAR) has been shown to lead to increased aortic stiffness. The aim of this study was to investigate the effect of stent graft type and stent graft length on aortic stiffness in a controlled, experimental setting. METHODS: Twenty porcine thoracic aortas were connected to a pulsatile mock loop system. Intraluminal pressure was recorded at two sites in order to measure pulse wave velocity (PWV) for each aorta: before stent graft deployment (t1); after deployment of a 100-mm long stent graft (t2); and after distal extension through deployment of a second 100-mm long stent graft (t3). Four different types of stent grafts (Conformable Gore\uae TAG\uae Device, Bolton Relay\uae Device, Cook Zenith Alpha\u2122, and Medtronic Valiant\uae) were evaluated. RESULTS: For the total cohort of 20 aortas, PWV increased by a mean 0.6 m/s or 8.9% of baseline PWV after deployment of a 100-mm proximal stent graft (P<0.001), and by a mean 1.4 m/s or 23.0% of baseline PWV after distal extension of the stent graft (P<0.001). Univariable regression analysis showed a significant correlation between aortic PWV and extent of stent graft coverage, (P<0.001), but no significant effect of baseline aortic length, baseline aortic PWV, or stent graft type on the percentual increase in PWV at t2 or at t3. CONCLUSIONS: In this experimental set-up, aortic stiffness increased significantly after stent graft deployment with each of the four types of stent graft, with the increase in aortic stiffness depending on the extent of stent graft coverage

Modelling home care organisations from an operations management perspective

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

hospital factory for manufacturing customised patient specific 3d anatomo functional models and prostheses

The fabrication of personalised prostheses tailored on each patient is one of the major needs and key issues for the future of several surgical specialties. Moreover, the production of patient-specific anatomo-functional models for preoperative planning is an important requirement in the presence of tailored prostheses, as also the surgical treatment must be optimised for each patient. The presence of a prototyping service inside the hospital would be a benefit for the clinical activity, as its location would allow a closer interaction with clinicians, leading to significant time and cost reductions. However, at present, these services are extremely rare worldwide. Based on these considerations, we investigate enhanced methods and technologies for implementing such a service. Moreover, we analyse the sustainability of the service and, thanks to the development of two prototypes, we show the feasibility of the production inside the hospital

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching

A new scheduling problem arising in the home care context is addressed, whose novelty with respect to the literature lies in the way overtime is paid. In this problem, some clients are willing to pay a higher fee to cover the additional overtime cost, if such overtime is incurred because a caregiver works extra time with the client to preserve continuity of care. These overtime hours charged to clients unburden the company, which no longer has to balance between cost and continuity of care in a traditional way. The problem is also studied in a context that includes preferences expressed by both clients and caregivers. Strict preferences must be respected with a high priority, while soft preferences increase the satisfaction and should be preferably respected. We formalize the problem as a Mixed Integer Linear Problem and also propose a cluster-based decomposition to solve real-life instances. The problem is inspired by the real case study of a provider operating in the USA. Numerical results validate the model and confirm the capability of the decomposition approach to deal with real-life instances

Analysis of a patient-nurse assignment policy in home care services

This paper deals with the problem of assigning a Home Care (HC) patient to an operator among a set of possible ones, under the constraint of continuity of care. Starting from a previously proposed assignment policy, we first compare the policy with that implemented in real organizations and then we derive some rules to choose the reference operator that will be in charge to deliver the care service to a new admitted patient. The proposed assignment rules take into account the variability of the patient needs, expressed in terms of the number of visits requested along the time. The comparison of the approach with the current practice of assigning the patient to the operator with the highest expected free capacity shows that cost savings and workload balancing can be reached by including patient and workload variabilities

A cost assignment policy for home care patients

The patient assignment problem in Home care (HC) consists of allocating each newly admitted patient to his/her reference operator, chosen among a set of possible operators. The continuity of care, where pursued, imposes that the assignment is not changed for a long period. The main goal of the assignment is to balance the workload among the operators. In the literature, the problem is usually solved with numerical approaches based on mathematical programming that do not consider the stochastic aspects of the problem. We derive a structural policy to assign a newly admitted patient while balancing the workload among the operators, by minimizing the expected value of a cost function that penalizes the overtime of operators. The workloads already loaded to the operators are assumed to be random variables as they are in the practice, while the demand related to the new patient is considered both deterministic and stochastic. Results show that the variability of the new patient’s demand is negligible with respect to the variability of the already assigned workloads and that similar assignments are obtained both in the presence or in the absence of this demand variability. A numerical comparison with the current practice of assigning the new patient to the operator with the highest expected available capacity shows that better balancings and cost savings can be reached by implementing the proposed policy