Key features for Intelligent Data Platform

Integration Engine

Built to support Interoperability, IDP can consume a diverse set of Healthcare data (including HL7, FHIR) to create customized outcome-based workflows.

Unified Data Layer

Combination of EMR/HMIS or clinical system data presented as a single source of prepared, cleansed, and structured unified data for improved efficiency and faster insights.

AI-as-a-Service

Allows providers to experiment with AI to detect and diagnose preventable harms without large initial investment and with low risk.

MLOps

A platform for collaboration and communication between data scientists and operations professionals to help manage production ML.

Transform Your Data Pipeline with our Intelligent Data Platform

Healthcare Integration Engine

HL7 interface development for inbound/outbound messages in an EMR
Development of interfaces empowering customer EHRs to communicate with external systems (clinical, financial and pharmacy)
Enable data to be routed to many sources with help of HealthCare Event builder leveraging all open source components (RedHat – Intelligent Data Platform)

Unified Data Layer

Access complete patient records and improve clinical decision-making.
Provide personalized access to the unified patient record at the point of care for both physicians and patients.
Provide Standardize data models like UDMH and custom build to align with business needs.
Generate insights as per business needs, with a self-service data catalog with enriched data set.
Provide the right data, at the right time, in the right modality with a unified data layer.

AI-As A Service

Assist data-scientists to build better machine learning models faster and take them from prototype to enterprise-scale deployment in a few clicks.
Accelerate Data scientists to start with already built-in data models providing a self-service experience.
Build advanced AI/ML pipelines and bring the power of DevOps with built-in MLOps workflows.
Develop specialized and bespoke analytics use cases for different stakeholders .i.e. CXOs, research teams, data scientists, analysts, clinicians, etc.