Living HTA: Automating Health Economic Evaluation with R

Area of Science:

• Health Economics and Outcomes Research (HEOR)
• Health Economic Modelling
• Data Science in Healthcare

Background:

• Accessing sensitive data is a major challenge for developing health economic models.
• Existing methods often require extensive data transfer, posing security and privacy risks.
• There is a need for innovative approaches to conduct health economic evaluations with minimal data sharing.

Purpose of the Study:

• To demonstrate a method for conducting health economic evaluations with minimal data transfer.
• To develop and showcase an automated analysis and reporting pipeline for health economic modelling.
• To enable secure data analysis without direct access to sensitive information.

Main Methods:

• Developed an automated analysis and reporting pipeline for health economic modelling, with open-source code.
• Utilized a consultant-built economic model with pseudo-data.
• Implemented an API hosted on the data-owner's server to host sensitive data, preventing direct transfer.
• Created an automated workflow to call the API, retrieve results, and generate reports.

Main Results:

• Modern data science tools enabled analysis without direct sensitive data access.
• The entire workflow can be largely automated, with scheduled or event-triggered analyses.
• Automated report generation and export streamline the dissemination of results.
• Manual document revision is significantly reduced, improving efficiency.

Conclusions:

• It is feasible to separate health economic models from sensitive data within the HEOR setting.
• An automated pipeline can streamline the main steps of health economic analysis and reporting.
• This approach enhances data security and operational efficiency in health economic evaluations.