Serverless OpenHealth at data commons scale-traversing the 20 million patient records of New York's SPARCS dataset in real-time

PeerJ. 2019 Jan 15:7:e6230. doi: 10.7717/peerj.6230. eCollection 2019.

Abstract

In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York's 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.

Keywords: Epidemiology data commons; Openhealth; Public health; Serverless computing; Sparcs.

Grants and funding

The authors received support from Suffolk Care Collaborative Delivery System Reform Incentive Payment Program (https://suffolkcare.org/AboutDSRIP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.