Reduce mortality rate due to Sepsis
after Bone Marrow Transplant
Clinical evidence indicates that patients with acute deterioration or sepsis manifest clinical signs or symptoms several hours before the condition worsens.
#1 Cancer Research Hospital uses Parex to monitor patient data in real time and feedback Sepsis probability to Epic to reduce mortality rate due to Sepsis
Realtime Patient Chat App
Integrated Mobile app for hospital to enable AI driven context based chat to connect to access to care and hospital staff. Parex app is integrated with MyChart
Clinical Trial AI analysis
Collect Clinical Trial data from Phase 1, 2 and 3 and configure various AI driven analysis to predict outcomes and performance reports. The Clinical trial data is combined with population management data for analysis
90-Day Mortality Model Monitoring for patients with high risk conditions
Research Hospital uses Parex to monitor patient data in real time to predict 90-day mortality probability using vitals, meds, and labs data
Population Health Management via Parex
Leading Medical group uses Parex to collect patient historical CCDA data, real time Clinical data and Claims data to develop comprehensive health check report
Genetic predisposition to Cancer
Parex is used by leading Cancer Research firm to collect genomics data which is then used for Prediction scoring. Patients can get online prescription from Clinic and testing kit. Patients do nasal swipe and mail the testing kit. Parex manages end to end workflow from prescription to testing kit to results.
Our Parex Platform
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.
Easy integration with Epic, Cerner, eClinical Works, eCW, Athena, Allscripts 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.
AI Algorithm Setup UI and Metrics
SimpleInterface to monitor clinical messages, configure AI parameters, load historical data and monitor AI outcomes and predictions. Real time changes to model and checking of results via DevOps modeling.
Next Generation Patient Experience
Patients deserve empathetic and personalized attention during their most vulnerable moments.