Make Artificial Intelligence a Reality
HL7 Data Streaming
with AI Model
#1 Cancer Research Hospital uses HiPaaS to enable AI driven care with real time monitoring.
Enable AI for your Hospital with real time streaming
AI Modeling Container
Python Container to enable and encapsulate AI Models. Integrate and use existing Models or build new models. Provide large amount of data with AI model. Provide selective real time data to execute the predictions.
EHR Integration
Easy integration with Epic, eCW, Athena or any EHR. Bi-direction interface with Epic or any EHR. Interface various HL7 messages. Dashboard capability to view messages. FHIR Interfaces. Custom API interfaces
Real-time HL7 and Data Streaming
Collect and stream a large amount of data using Kafka Stream. Segment data into multiple topics. Selective Filtering and data co-relation. Real-time data result retrieval. Update historical model with new results.
HiPaaS Platform Features
Store, locate, and reconcile healthcare data as it arrives using Graph DB
With HiPaaS Kafka stream large volumes of data in real time.
Support for all Healthcare data formats – EDI, HL7, FHIR, NCDP and more.
Autoscale with Kubernetes Cluster management and Docker containers.
Case Studies
Hospital Innovation
Population Health Management via HiPaaS
HiPaaS used for Population Health Management by leading Medical Group. HiPaaS collects following events from various sources – 1. CCDA information about Patient History 2.
Paper to Digital OCR integration for EHR systems
Paper to DigitalOCR Integrationfor EHR Systems Designing a highly-scalable patient management and OCR document service. CASE STUDY CLINICAL INNOVATIONS Client required a scalable and highly
Genetic predisposition to Cancer
HiPaaS is used by leading Cancer Research firm to collect genomics data which is then used for Prediction scoring. Patients can get online prescription from
Diabetes and Kidney Health Management via HiPaaS
HiPaaS used for Diabetes and Kidney Health Management by Medical Group. HiPaaS collects following events from various sources – 1. CCDA information about Patient History
Clinical Data Extracts for Analytics Engine
Client need was to develop and maintain automated EHR clinical data extracts to be sent over for ingestion to a third-party Analytics engine. HiPaaS team