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.