Assessing the Value of Digital Technologies: A Case Study on Humanoid Robot Assistance in Neurorehabilitation

OBJECTIVES: This case study employed a methodological approach to examine the value of a digital application, specifically focusing on a humanoid robot in neurorehabilitation for stroke survivors. The objective of the case study was to simulate efficiency of digital applications using a value assessement framework (VAF).

METHODS: Value was assessed through an efficiency frontier (EF) using probabilistic simulation. Various tools were employed to evaluate and visually depict parameter uncertainty. Uncertainties regarding reimbursement prices were assessed using price acceptance curves (PAC) and net monetary benefit (NMB). Risk acceptability curves (RAC) and net risk benefit (NRB) diagrams were employed to support informed decision-making based on risk tolerance.

RESULTS: A probabilistic model was used to compare the benefits, costs, and risks associated with the utilization of a humanoid robot in comparison to alternative treatment methods. In the simulation, all treatment methods were plotted and compared over 10,000 iterations. The application of PAC assisted in estimating the range of reimbursement prices, effectively addressing uncertainties. NMB analysis was conducted to assess whether costs were justified or inefficient based on a predetermined threshold. The RAC and NRB provided visual representations of the threshold at which potential risks outweigh the associated benefits and when the level of risk becomes unacceptable.

CONCLUSIONS: This case study provided a comprehensive framework for evaluating the impact of digital healthcare interventions. The study addressed methodological challenges, such as weighting clinical endpoints, aggregating relevant parameters into a utility function, and visually representing parameter uncertainties. The simulation of artificial data, assumptions of linearity, and reliance on normal distributions of effects limits applicability and affects the study's robustness. Nevertheless, the transparent and reproducible VAF enhances the understanding of decision-making processes and facilitates informed reimbursement decisions and negotiations in the healthcare sector.