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Data Analytics

Predictive Analytics in Healthcare | A Quick Guide

Jagreet Kaur | 22 March 2023

What is Predictive Analytics in Healthcare?

Predictive Analytics solutions in healthcare are a part of advanced data analytics used to predict future events. Predictive analytics practices in many ways, from data mining, statistics, modeling, machine learning, and artificial intelligence to examine current data to making forecasts about the future.
  • Good healthcare boosts the economy of the nation. Precision medicine, along with Big Data, is leveraged to build better patient profiles and predictive models to diagnose and treat diseases.
  • TeleMedicine and AI in healthcare is a miracle in remotely performing treatment of patients using Pattern Recognition, optimizing duty allocation, and monitoring live data.
  • Real-Time Big Data for Infection Control to predict and prevent infections through networks creating safer environments.
  • Patient Data Analytics for patient dealing and preventing readmissions and better pharmaceutical supply chain management and delivery.

Challenges for Building a Predictive Analytics Platform

  • Interface for the patient to search nearby doctors 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 suggestions to the patient.
  • Interface for the patient to contact nearby labs to collect a sample and upload test reports on the server, followed by the push notification when the report is ready.
  • Share the report with a doctor, followed by a prescription to the patient.
  • Search for nearby medical stores and place an order for the prescription got from the doctor.

Solution Offerings for Predictive Analytics in Healthcare

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

Enable real-time monitoring of user events for better decision making

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 users’ clicks, cookies, and other data to know users better.

Microservices using Spring Cloud, NetFlix OSS, Consul, Docker, and Kubernetes. Develop REST APIs using Microservices architecture with Spring Cloud and Spring Boot Framework using Java language. Moreover, use the Async support of Spring framework to create Async controllers that make REST API easily scalable. Spring to deploy REST and use Kubernetes for secure containers and their management. For API gateway, use NetFlix Eureka Server, which acts as a proxy for REST API and a lot of Microservices, Consul as DNS enables auto-discovery of Microservices.

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