Guide to Building Health Care Platform for Predictive Analytics

  • Good healthcare boosts the economy of the nation. Precision medicine along with Big Data is leveraging in building better patient profiles as well as predictive models to diagnose and treat diseases.
  • TeleMedicine and AI in healthcare is indeed a miracle remotely performing treatment of patients using Pattern Recognition, optimizing duty allocation, monitoring live data.
  • Real-Time Big Data for Infection Control to predict and prevent infections through networks creating safer environments.
  • Patient Data Analytics for a patient dealing and preventing readmissions and better pharmaceutical supply chain management and delivery.

Challenges for Building Predictive Analytics Platform

  • Interface for the patient to search nearby doctor by particular Healthcare categories.
  • Enable patient visibility to see doctor's availability online and communicate via text chat, audio or video call.
  • Visible allotment number to the patient in the waiting queue.
  • Communicate with the doctor as well as test or medicine suggestion to the patient.
  • Interface for the patient to contact with nearby labs to collect a sample and upload test reports on server followed by the push notification when the report is ready.
  • Share report with doctor followed by prescription to the patient.
  • Search for nearby medical stores and place an order for the prescription got from the doctor.

Solution Offerings for Real-Time Monitoring


Develop a Healthcare platform to fully automate using the latest technologies and distributed Agile development methods.


Real-Time Monitoring of User’s Events


Apache Kafka & Spark Streaming to achieve high concurrency, set up low latency messaging platform Apache Kafka to receive Real-Time user requests from REST APIs (acting as Kafka producer).


Apache Spark Streaming (processing and Computing engine) Spark-Cassandra connector, stored 1 million events per second in Cassandra. Built Analytics Data Pipeline using Kafka and Spark Streaming to capture user’s clicks, cookies, and other data to know users better.


Microservices using Spring Cloud, NetFlix OSS, Consul, Docker, and Kubernetes


Develop REST API’s using Microservices architecture with Spring Cloud and Spring Boot Framework using Java language. Moreover, use Async support of Spring framework to create Async controllers that make REST API easily scalable.


Spring to deploy REST and use Kubernetes for secure container and its management. For API gateway, use NetFlix Eureka Server which acts as a proxy for REST API and the lot of Microservices, Consul as DNS enables auto-discovery of Microservices.

Looking For More Details

Download Now

Data Driven Enterprises with DataOps

Talk to Experts for Continuous Delivery to Analytics, Machine Learning and Data Management Practices

Reach Us

Disrupting Industries with Enterprise AI

Accelerate AI Adoption by Harnessing AI Power, Implementing AI Solutions and Leveraging AI Marketplace

Contact Us

Decentralised Big Data management and Governance, AI Marketplace for Operationalising and Scaling AI

  • Advanced Monitoring
  • Infrastructure Automation
  • Optimizing GPU Usage
  • Deploying Deep Learning Models
Learn More