Collaborative Cloud Computing Framework for Health Data with Open Source Technologies

TL;DR

This paper presents a new open-source cloud computing framework tailored for health data that addresses performance, flexibility, scalability, and privacy compliance challenges in scientific research.

Contribution

It introduces a novel architecture leveraging open source tools like Hadoop, Kubernetes, and JupyterHub for health data analysis in a distributed environment.

Findings

System successfully processed 69 million patient records.

Framework improved data manipulation and query performance.

Ensured HIPAA compliance in a scalable cloud setup.

Abstract

The proliferation of sensor technologies and advancements in data collection methods have enabled the accumulation of very large amounts of data. Increasingly, these datasets are considered for scientific research. However, the design of the system architecture to achieve high performance in terms of parallelization, query processing time, aggregation of heterogeneous data types (e.g., time series, images, structured data, among others), and difficulty in reproducing scientific research remain a major challenge. This is specifically true for health sciences research, where the systems must be i) easy to use with the flexibility to manipulate data at the most granular level, ii) agnostic of programming language kernel, iii) scalable, and iv) compliant with the HIPAA privacy law. In this paper, we review the existing literature for such big data systems for scientific research in health sciences and identify the gaps of the current system landscape. We propose a novel architecture for software-hardware-data ecosystem using open source technologies such as Apache Hadoop, Kubernetes and JupyterHub in a distributed environment. We also evaluate the system using a large clinical data set of 69M patients.