Hospital operations optimization


Health analytics SaaS product using advanced data science improves
operational performance.


Year2009 – Ongoing

RoleDevOps, UX, QA

TechnologyAWS, HP Vertica, Java, Spring/Play framework, Chronos, Mesos, Docker, Zoo Keepr, MySQL, Python, R, Angular 2, jQuery, PhantomJS, Tableau, Nginx, Lua

Achieve low latency across workloads

A health analytics SaaS product uses advanced data science to significantly improve the operational performance of hospitals and clinics. Used in conjunction with existing EHR’s, the platform creates optimized schedules that reduce patient wait time and overall operational cost. The requirement was to achieve low latency across different workloads and usage patterns.

Docker improves resource utilization

We leveraged Docker (an opensource containerization solution) along with other OSS solutions for deployment to improve resource utilization through a near real-time capacity planning. As workloads are not known upfront, we used dynamic partitioning using Docker cGroup memory to shrink the quotas and pass it on to other client deployments which may need more resources.

Using opensource solutions like Nagios, Codahale Metrics and Cloud Metrics, we created a dashboard that provides weekly performance report (workloads, response times, operational expense) that compares current performance with previous week’s performance.


Long-term infrastructure health assured

Continuous monitoring of live data helped us better predict trends to improve the long-term health of the client’s business infrastructure.