Estimating patient demand progression in home care: a Bayesian modeling approach

Home Care (HC) service consists of providing care to patients at their own home, without the necessity of bringing them to hospitals or nursing homes. This service allows a high quality of life for the assisted patients and, at the same time, a cost reduction for the health care system. Planning human resources is a difficult task and, for a good quality of planning, a knowledge of future demands for visits from patients is required. In the literature, several studies deal with stochastic models for representing patient conditions in health care systems but, to the best of our knowledge, few works deal with HC service and Bayesian approaches have not been considered in the HC context, yet. The aim of this paper is to propose a methodology for estimating and predicting the demand for care by HC patients in terms of number of visits (N) required in a defined time slot. Patients are characterized by a Care Profile (CP) which varies along with the time secondary to a periodic revision or sudden variations in health state. Our approach considers the joint distribution of N and CP over time as a conditional distribution of N given CP, times the marginal of the CP; in addition, the transition between CPs is regulated by a non homogeneous multistate Markov Chain. The proposed model is developed and validated considering the data of one of the largest HC providers in Italy. We obtain the posterior densities of model parameters through MCMC simulation and predict the number of visits from patients in future time slots. Results show the applicability of the approach in the practice and a good quality of the predicted number of visits.