OBJECTIVES: Health economic models are essential tools for evaluating the cost-effectiveness and budget impact of healthcare interventions. The ability to develop and deploy these models in an interactive, user-friendly manner significantly enhances their accessibility and utility for stakeholders. This study explores the use of Python Dash and R Shiny, two leading platforms for building interactive web applications, in the development of advanced health economic models (including simulation and state transition models).

METHODS: Python Dash and R Shiny frameworks were used to develop interactive web-based economic models. Different libraries in Python Dash were used to handle various tasks related to the web-based economic models, such as data manipulation, model development, and creation of visualizations. Functions were developed to handle a wide range of computational operations for Excel models, including the calculation of different economic model parameters. Similarly, R Shiny used different packages to handle data manipulation and interface development to replicate the functionality of Excel based economic models.

RESULTS: Both platforms successfully overcome significant constraints of Excel-based models. User feedback indicated higher satisfaction with Dash’s visualizations, while Shiny was preferred for its user-friendly interface. Both platforms demonstrated excellent scalability, effectively handling large datasets without significant performance issues. Overall, the choice between Dash and Shiny depends on the user’s familiarity with Python or R and the specific requirements of the project.

CONCLUSIONS: Leveraging Python Dash and R Shiny for health economic modeling provides robust solutions for developing interactive and accessible economic models. Integrating these platforms into health economic research significantly enhances model transparency, stakeholder engagement, usability, complex calculations, and data-driven decision-making in healthcare.