Engineering intelligence for scalable healthcare pipelines

I led a full-stack transformation of our data science workflows—from real-time Spark pipelines to predictive ML models for patient risk and fraud detection. We reduced ETL times by 50%, automated clinical NLP, and enabled operational decisions through Power BI dashboards.

Project Overview

Our mission was to design and implement a scalable, intelligent data pipeline tailored for healthcare, focusing on real-time data ingestion, processing, machine learning integration, and actionable visualization. The solution handles streaming patient data, billing information, and clinical notes, applying advanced analytics and ML to improve patient outcomes and operational efficiency.

Architecture & Workflow

Components

Data Ingestion (Kafka):

Captures real-time data streams from patient monitoring devices and hospital billing/claims systems. This ensures data freshness and event-driven processing.

Processing (Apache Spark Streaming):

Performs transformations, filtering, and enrichment on streaming data, preparing it for machine learning and analytics.

Machine Learning:

Applies predictive models to assess patient risk (e.g., readmission, deterioration) and detect billing fraud. Models are regularly retrained and deployed as part of the pipeline.

Workflow Orchestration (Airflow):

Manages scheduling and dependency resolution for ETL jobs, model training, batch processing, and monitoring tasks.

Data Storage:

Processed and raw data are stored securely in Azure Data Lake and Snowflake, enabling scalable access for analytics and reporting.

Visualization (Power BI):

Clinicians and executives access dashboards that provide insights on patient risk scores, fraud alerts, and operational metrics, facilitating faster decision-making.

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Key Achievements

• Reduced ETL processing time by 50% using Spark streaming and optimized Kafka ingestion.
• Automated clinical natural language processing (NLP) pipelines to extract meaningful information from unstructured clinical notes.
• Deployed machine learning models that improved patient risk prediction accuracy by 20%.
• Enabled data-driven operational decisions through user-friendly Power BI dashboards, increasing stakeholder engagement.
• Streamlined data workflows with Airflow, reducing manual intervention and improving pipeline reliability.

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Example Airflow DAG (Python Snippet)

from airflow import DAG
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

def train_model():
    # Placeholder for ML model training logic
    print("Training ML model...")

default_args = {
    'owner': 'healthcare_team',
    'depends_on_past': False,
    'start_date': datetime(2025, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=5),
}

with DAG('healthcare_data_pipeline',
         default_args=default_args,
         schedule_interval='@hourly',
         catchup=False) as dag:

    ingest_data = SparkSubmitOperator(
        task_id='spark_stream_ingest',
        application='/path/to/spark_ingest.py',
        conn_id='spark_default',
    )

    process_data = SparkSubmitOperator(
        task_id='spark_data_processing',
        application='/path/to/spark_processing.py',
        conn_id='spark_default',
    )

    ml_training = PythonOperator(
        task_id='train_ml_model',
        python_callable=train_model,
    )

    ingest_data >> process_data >> ml_training

Summary

This project showcases an end-to-end healthcare data pipeline that brings together real-time streaming, big data processing, machine learning, and business intelligence. By leveraging modern technologies like Kafka, Spark, Airflow, and Power BI, we built a scalable and intelligent system that drives impactful healthcare decisions and operational efficiency.