Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study

Abstract

Introduction: Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number 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 comparing its costs to conventional rehabilitation pathways.

Methods: Since 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. The aim of the study is to compare the costs of a "mixed" rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings.

Results: We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one.

Conclusion: Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.

Keywords: cost analysis; cost minimization; probabilistic simulation; robotic rehabilitation; sensitivity analysis.

Figure 1

Box plot of Barthel index values at admission for the three different groups of patients D (inpatients), H (day cases) and P (outpatients). The lower and upper side of each box represent the first and third quartile, respectively. The horizontal bold line shows the median value. Whiskers extend to the most extreme data point which is no more (or less) than 1.5 times the interquartile range from the box, while circles represent outliers. Double asterisks indicate p-values <0.001.

Figure 2

Color-map representing how DCost varies in function of the parameters R and n. The DCost gradient is represented on the right side of the figure and moves from green (DCost<0) to red (DCost>0). The black line represents the situation where DCost = 0. The situation of the SMP hospital is represented by the blue cross (R = 17.88 and n = 297). The dashed area represents the situations that cannot be achieved due to the organizational and/or clinical constraints ( $R·n<=8250$ and R/S $=$ 0.35, where S = 67.94). The green star represents the maximum DCost achievable by increasing R while the red star represents the same DCost value achieved by increasing n.

Figure 3

From left to right, histograms of the parameters R, P, D and H with the PDFs fitting obtained by minimizing the AIC (red lines).

Figure 4

Histograms resulting from the simulations of cost differences (DCost). On the left side the simulation with R varying around the mean value of 23.78 and n fixed at 297, on the right side the simulation with R varying around the mean value of 23.78 and n varying around the mean value of 346.93.