Accelerating Clinical Trials with {teal}: Data Exploratory Apps Built in Weeks, Not Months

Project overview

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{teal} is an open-source Shiny framework for interactive clinical data exploration, designed to empower analysts working with CDISC datasets. As a community-driven initiative, {teal} is codeveloped by contributors across the pharmaceutical and tech industries, including Appsilon and leading global pharmaceutical companies. 

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Two Appsilon senior R developers became core contributors, focusing on reducing technical debt, enhancing the codebase, and preparing the framework for its 1.0 release. Once released, study teams across the ecosystem will leverage {teal} to streamline analyses, reduce manual work in generating tables, listings, and graphs (TLGs), and enable faster creation of Medical Data Review (MDR) dashboards.

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Challenge

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Appsilon joined the core {teal} team to contribute to technology transformation in clinical trials. Our developers are responsible for building and maintaining core R packages related to a Shiny-based framework for exploratory data analysis (EDA) and generating tables, listings, and graphs (TLGs) from CDISC datasets. {teal}, while powerful, faced hurdles common to open-source projects:

• Technical expertise: Contributors with expertise in both R/Shiny and large-scale package development were scarce.
• Team continuity: Frequent turnover of contributors made it challenging to maintain project momentum and knowledge.
• Technical debt: The existing framework had accumulated significant technical debt, hindering further development and release on CRAN.
• Onboarding complexity: The large codebase and complex design made onboarding new developers time-consuming and difficult.

The community’s goal was to advance {teal} toward a CRAN release by the beginning of 2026 while ensuring it remained a shared resource with high-quality packages.

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Solution

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Appsilon strengthened the client’s core team with two senior developers who contributed to R package development bringing ten packages to production so far. These include: teal.logger, teal.code, teal.data, teal.widgets, teal.slice, teal.transform, teal.reporter, teal, teal.modules.general, teal.modules.clinical. Our involvement resolves around

• Developing new features for the teal framework.
• Fixing bugs and addressing technical debt.
• Refactoring the codebase to improve quality and prepare for a 1.0 release.
• Assisting users with the adoption of packages.

By integrating their expertise in R, Shiny, CI/CD, and DevOps, Appsilon helped drive 10+ packages to production and advanced the framework’s readiness for its 1.0 release.

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Impact

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{teal} drastically reduces development time compared to building Shiny apps from scratch, thanks to its modularized design, built-in data filtering panel, extensive analysis modules, and automated reporting capabilities. While the project is still ongoing and specific business objectives continue to evolve, the collaboration has already delivered significant results:

• Roughly 80% of the project is now complete, bringing the framework significantly closer to a 1.0 release,  thanks partly to key technical contributions from Appsilon.
• Over 30 studies are already using the framework for data analysis. Two clinical trials, in particular, successfully implemented new packages with our support, further accelerating their workflows and improving reproducibility.
• A tutorial video on the framework has already reached over 2,000 views, supporting internal teams and the wider open-source pharma community in building confidence and applying the framework more effectively.
• With {teal}, building new data exploration applications for clinical trials now takes weeks instead of months, significantly accelerating time to insight and enabling quicker, data-driven decisions.

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The framework’s modular design, automated reporting, and built-in data tools have significantly cut development time for Shiny apps. Post-release, {teal} will enable study teams to reduce redundant TLGs and expedite MDR dashboard creation—potentially saving hundreds of hours annually.

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We have been working on the project since 2023, and the key focus is on bringing the framework to a 1.0 release on CRAN, which is planned for the end of 2025 or the beginning of 2026.

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We rely on Appsilon expertise to support the build and maintenance of critical clinical reporting software based in R and Shiny. They have provided us with excellent technical skills and we have witnessed their commitment to open source, and making an impact for all patients across the pharmaceutical industry.

- Data Engineering Lead at a top 10 Pharma Company

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