Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study - Gower, Valerio; Aprile, Irene; Falchini, Francesca; Fasano, Alessio; Germanotta, Marco; Randazzo, Mattia; Spinelli, Federico; Trieste, Leopoldo; Gramatica, Furio; Turchetti, Giuseppe (Frontiers in Public Health, 2024)
Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number of those patients regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by analyzing and comparing its costs to conventional rehabilitation pathways. Given that, technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. In particular, the aim of the research presented here is to compare the costs of an organizational model in which upper limb robotic rehabilitation is performed with a set of four devices, with conventional treatment. This has been done by developing a cost model and retrospectively analyzing the costs sustained by one of the hospitals of Fondazione Don Gnocchi which has adopted such an organizational model. The results show that robotic rehabilitation allowed the hospital to achieve savings compared to conventional treatments. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to identify the conditions to optimize the efficiency of robotic rehabilitation. Finally, a probabilistic simulation approach has been applied to take into account the variability of model parameters and evaluate the probability of achieving a given level of savings.