Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

Abstract

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Keywords: healthcare economics; healthcare systems management; patient-centric care; personalized medicine; resource-constrained healthcare.

Figure 1

A schematic view of the proposed model's settings. The dots of the same color indicate different levels of personalization for the same treatment. The boxes sounding the dots present the different versatility of this treatment configuration in terms of both the CSR and OEB.

Figure 2

A function of the SP's optimal policy's personalization level (ρ) with respect to the overhead budget provided for the treatment personalization (f). The results are shown as the mean ± standard deviation of n = 1, 000 repetitions.

Figure 3

The normalized number of choosing each treatment protocol based on its delta OEB and CSR compared to the global treatment (e.g., the non-personalized treatment). The results are shown as the mean of n = 1, 000 repetitions.

Figure 4

A sensitivity analysis of the treatment policy personalization level (ρ) with respect to the model's parameters. The results are shown as an mean ± standard deviation of n = 1, 000 repetitions. (A) Number of patients. (B) Average delta OEB. (C) Average delta CSR.