# Enhancing Gen3 for clinical trial time series analytics and data discovery: a data commons framework for NIH clinical trials

## TL;DR

This paper introduces a framework to improve Gen3, a data platform, for analyzing time-based clinical trial data, making it easier to study chronic pain and substance use disorders.

## Contribution

The paper introduces a cloud-native framework with temporal visualization tools for clinical trial data in Gen3.

## Key findings

- The framework reliably handles varied time-based data structures with temporal precision.
- Implementation in NIH HEAL Initiative networks shows utility for real-time monitoring of longitudinal outcomes.
- The enhancement supports FAIR principles for clinical trial data sharing and reuse.

## Abstract

This work presents a framework for enhancing Gen3, an open-source data commons platform, with temporal visualization capabilities for clinical trial research. We describe the technical implementation of cloud-native architecture and integrated visualization tools that enable standardized analytics for longitudinal clinical trial data while adhering to FAIR principles. The enhancement includes Kubernetes-based container orchestration, Kibana-based temporal analytics, and automated ETL pipelines for data harmonization. Technical validation demonstrates reliable handling of varied time-based data structures, while maintaining temporal precision and measurement context. The framework's implementation in NIH HEAL Initiative networks studying chronic pain and substance use disorders showcases its utility for real-time monitoring of longitudinal outcomes across multiple trials. This adaptation provides a model for research networks seeking to enhance their data commons capabilities while ensuring findable, accessible, interoperable, and reusable clinical trial data.