Indianapolis Emergency Medical Services (IEMS) is the largest provider of emergency prehospital medical care in Indiana, responding to nearly 100,000 911 calls each year. The organization not only serves the nearly 1 million residents of Marion County, Indiana through emergency care, but also through outreach and educational programs. In order to deliver the best level of care, as well as optimize operations as part of its duty to the city's taxpayers, IEMS turned to data to help guide decisions around how they operate.
This required pulling together millions of data points across various government agencies, from clinical and patient data from local hospitals, to response and logistical data from 911 calls and ambulance dispatches, and census data like demographics and crime statistics. IEMS tried using business intelligence and performance management software, Cognos, but found it too cumbersome due to its batch-oriented processing. Between data cleansing and manual correlation in extensive pivotal tables, getting insights out of their data would take weeks, making it nearly impossible to make decisions that could have an immediate impact on protocol and resource allocation.
To help them gather and analyze data more efficiently, IEMS turned to Perceivant, which makes a HIPAA-compliant, cloud-based data analytics platform powered by Elasticsearch called Data Dojo.
In working with Perceivant, IEMS exported data from various medical systems into CSV, XML, HL7, and JSON files. These files were then securely imported to Data Dojo, where the data was indexed in Elasticsearch. From there, the data is directly output to an OLAP multi-tenant reporting service where users can run real-time queries and build interactive dashboards, eliminating a lot of the manual data processing and correlation that IEMS used to perform. The NoSQL nature of Elasticsearch and Perceivant's flexible analytics structure allowed the addition of new structured columns on-demand to further analyze the data, eliminating the need to make any coding changes to the medical systems the data was being pulled from. And, thanks to the full-text search and analytics capabilities of Elasticsearch, Data Dojo also let IEMS incorporate unstructured data from doctor's notes, helping create new dashboards that let them respond to unforeseen challenges and extend their use case in ways they couldn't before imagine.
One such example is the 2014 Ebola outbreak epidemic. IEMS knew they needed to get real-time insights from their data to get ahead of the virus, so they engaged Perceivant to help them build dashboards in Data Dojo to identify changes in normal influenza patterns. Elasticsearch was essential to searches of the unstructured doctors notes to identify symptoms like ‘fever', ‘fatigue', or ‘headache'. By comparing historical influenza data to real-time reported symptoms, IEMS could easily spot a spike or trend that they could immediately further investigate.