A Digital Software Support Platform for Hyperthyroidism Management in South Korea: Markov Simulation Model-Based Cost-Effectiveness Analysis

Abstract

Background: The integration of wearable technology for heart rate monitoring offers potential advancements in managing hyperthyroidism by providing a feasible way to track thyroid function. Although digital health solutions are gaining traction in various chronic conditions, their cost-effectiveness in hyperthyroidism management requires deeper investigation.

Objective: This study aimed to evaluate the cost-effectiveness of a wearable or mobile-based thyroid function digital monitoring solution for hyperthyroidism management and to make a comparison with the existing standard approach within the South Korean health care context.

Methods: We developed a decision-analytic Markov microsimulation model to simulate the cost and effectiveness of digital monitoring in a cohort of 10,000 hypothetical hyperthyroidism patients aged 40 years. The analysis was conducted from the perspective of the health care system, with a 4.5% annual discount rate applied to costs and effectiveness and an inflation adjustment to 2022 values. Model inputs were sourced from clinical studies, publicly available datasets, and expert input, with outcomes measured in quality-adjusted life years (QALYs). Cost-effectiveness was evaluated through incremental cost-effectiveness ratios (ICERs) and net monetary benefits (NMB), with additional deterministic and probabilistic sensitivity analyses performed to address input uncertainties.

Results: Integrating digital monitoring yielded an additional 0.32 QALYs per patient at an incremental cost of US $3143, resulting in an ICER of US $9804.30 per QALY, significantly below the South Korean willingness-to-pay threshold of US $32,255/QALY. The digitally supported group exhibited improved rates of long-term remission (22.68%, 2268/10,000) and reduced postremission relapse (17.87%, 1787/10,000) compared to standard care (17.48%, 1748/10,000 and 26.37%, 2637/10,000, respectively). Probabilistic sensitivity analysis showed that digital intervention was the preferred cost-effective strategy in 64.4% (6440/10,000) of iterations. Subscription costs of the digital platform and the utility weight for thyroid-associated orbitopathy emerged as key factors affecting the ICER in sensitivity analyses.

Conclusions: The findings suggest that digital monitoring provides a cost-effective strategy for enhancing hyperthyroidism management, supporting sustained remission, and reducing relapse rates. As such, digital solutions could serve as a valuable adjunct to traditional care, with the cost-effectiveness analysis providing an economic basis for determining pricing and value-based reimbursement in health care systems. The study underscores the importance of integrating digital solutions in chronic disease management and suggests that further research should include societal costs, such as productivity, to capture economic benefits fully.

Keywords: QALYs; SDG3:good health and well-being; applications; apps; cost-effectiveness analysis; digital health; digital interventions; digital monitoring solution; digital technology; hyperthyroidism; hyperthyroidism management; mobile-based monitoring; quality-adjusted life years; wearable devices.

Figure 1.. Description of a wearable or mobile-based thyroid function digital monitoring solution designed for the effective management of hyperthyroidism. (A) Heart rates collected from a wearable device on each date (the date of referred thyroid function test and target date), and (B) the risk level of hyperthyroidism calculated for the target date is determined by comparing the heart rate distribution over N days, including the target date, with the heart rate distribution over N days, including the day of the referred thyroid function test. In cases where the risk level surpasses the designated threshold, a notification is sent to the user, prompting them to seek medical attention. TFT: thyroid function test.

Figure 2.. A basic description of the structure in the decision analytic Markov model. ATD: antithyroid drug; RAl: radioactive iodine.

Figure 3.. Markov probability analysis of each state with and without digital software support. ADT: antithyroid drug therapy.

Figure 4.. Tornado diagram for incremental cost-effectiveness ratio. ADT: antithyroid drug therapy; ICER: incremental cost-effectiveness ratio; TAO: thyroid-associated orbitopathy; WTP: willingness to pay.

Figure 5.. Results of probabilistic sensitivity analysis: (A) cost-effectiveness acceptability curve and (B) incremental cost-effectiveness scatterplot. WTP: willingness to pay.