Real-Time Patient Vitals with Kinesis and Lambda

Process real-time patient vitals events using Kinesis and Lambda, store valid records in S3, and create alerts when readings cross thresholds.
AWS • Streaming • Advanced • Healthcare

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What this project solves

Healthcare systems may receive patient vitals continuously from bedside monitors or clinical applications.

Heart rate, oxygen level, temperature, respiratory rate, and blood pressure cannot always wait for a daily batch pipeline. Some events need to be captured quickly, validated, stored safely, and checked for possible alerts.

In this pipeline, you will build a real-time patient vitals pipeline on AWS. You will send vitals events into Kinesis, process them using Lambda, store valid events in Amazon S3, separate invalid records into a DLQ area, query the results using Athena, and trigger SNS alerts when readings cross defined thresholds.

What you’ll build

  • Generate sample patient vitals events
  • Send real-time vitals events into Kinesis
  • Process incoming events using AWS Lambda
  • Validate vitals readings before storing them
  • Store valid events in Amazon S3
  • Route invalid or incomplete records to a DLQ area
  • Create simple threshold-based alerts using SNS

How you’ll build it

  • Create a Kinesis stream for patient vitals events
  • Publish sample NDJSON vitals events into the stream
  • Use Lambda to validate and process incoming records
  • Write valid records and DLQ records into Amazon S3
  • Query stored events with Athena and send high-severity alerts using SNS

Tools you’ll use

Amazon Kinesis • AWS Lambda • Amazon S3 • Amazon Athena • Amazon SNS • AWS IAM • Python • NDJSON

What you’ll walk away with

After completing this pipeline, you will be able to:

  1. Explain how real-time event processing works on AWS
  2. Process streaming healthcare events using Kinesis and Lambda
  3. Validate JSON events and separate bad records
  4. Store and query streaming outputs using S3 and Athena
  5. Create simple rule-based alerts from real-time event data

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