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Healthcare

Leveraging Cloud Solutions and Machine Learning for Data Processing in Healthcare

Dashboard showing real-time metrics and predictive analytics powered by machine learning in a healthcare cloud platform.

Focus Areas

Cloud Architecture & Migration

Machine Learning Applications

Healthcare Data Processing

Encrypted healthcare data storage and real-time compliance monitoring.

Business Problem

A national healthcare analytics provider struggled to process large volumes of patient and claims data in a timely and cost-efficient manner. Their on-premise infrastructure lacked scalability, leading to slow performance during peak loads and delays in data availability for analytics teams. The manual processes involved in cleaning and structuring unstructured health records also introduced inconsistencies and compliance risks. They needed a modern, automated data processing pipeline capable of scaling with demand and delivering accurate, actionable insights rapidly.

Key challenges:

  • Scalability Limits: Existing infrastructure couldn’t efficiently handle growing data volumes from EHRs, labs, and claims systems.

  • Manual Data Cleansing: Ingesting and standardizing unstructured or incomplete health data was time-consuming and error-prone.

  • Slow Time-to-Insight: Analytical reports were delayed by bottlenecks in ETL and model training cycles.

  • Compliance Requirements: The portal did not meet modern accessibility standards (WCAG 2.1) or HIPAA privacy expectations for digital tools.

The Approach

Curate Consultant’s partnered with the client to build a secure, cloud-native data processing pipeline powered by machine learning. The solution enabled real-time ingestion, automated cleansing, and scalable analytics across millions of health records—delivering faster insights and reducing manual overhead

Key components of the solution:

  • Discovery and Requirements Gathering:

    • Stakeholder Workshops: Conducted sessions with data engineers, compliance officers, and analytics leads to define system needs and risk parameters.

    • Current State Assessment: Reviewed existing ETL flows, infrastructure architecture, and processing bottlenecks.

    • Requirements: Scalable compute, real-time ingestion, ML-based data parsing, and strong security protocols.

  • Cloud Architecture & ML Pipeline Design:

    • Cloud Migration: Moved core data infrastructure to AWS using S3 for data lakes, Redshift for analytics, and Lambda for serverless compute.

    • Data Ingestion: Designed streaming ingestion pipelines using AWS Kinesis and Glue for real-time loading from EHRs, claims systems, and partner APIs.

    • Automated Cleansing: Applied NLP-based machine learning models to clean and structure diagnosis descriptions, lab notes, and provider comments.

    • Predictive Analytics Models: Developed ML algorithms (XGBoost, logistic regression) to detect data anomalies and predict outcomes like readmissions or claim rejections.

    • Orchestration & Monitoring: Implemented Apache Airflow to schedule, track, and audit jobs, ensuring reliability and compliance visibility.

  • Process Optimization and Enablement:

    • Parallel Processing: Leveraged Spark clusters in EMR to process massive datasets concurrently.

    • API Access for Teams: Exposed clean, structured datasets through internal APIs and dashboards for clinical and actuarial teams.

    • Self-Service Analytics: Enabled real-time data visualization in Power BI and Tableau, connected directly to the cloud data warehouse.

    • Security & Compliance: Applied encryption at rest/transit, IAM policies, audit logging, and automated redaction of PHI.

  • Stakeholder Engagement & Change Management:

    • Cross-Functional Coordination: Data scientists, compliance leads, and product teams jointly defined and validated ML logic and business rules.

    • Training Sessions: Hosted onboarding and technical workshops to help internal teams adopt the new cloud and analytics tools.

    • Documentation & SOPs: Delivered end-user guides and process flows to support ongoing maintenance and governance.

Business Outcomes

Faster and More Accurate Data Processing


ML-driven automation reduced processing time from hours to minutes, with improved data accuracy and consistency across pipelines.

Improved Time-to-Insight for Analytics


Data was accessible to business users and analysts in near real-time, enabling faster decision-making and patient outcome tracking.

Scalable Infrastructure at Lower Cost


Pay-as-you-go cloud resources allowed the system to scale effortlessly while cutting costs associated with overprovisioned hardware.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Average data processing time 6.5 hrs 28 minutes 93% faster
Accuracy of data cleansing 82% 97% 18% increase
Monthly compute cost (est.) $75,000 $46,000 39% reduction
Time-to-insight (dashboard refresh) 1 day 1 hour 24X increase
ML anomaly detection precision N/A 92% Introduced new capability
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Accelerated Insights


Analysts and care managers accessed critical reports in hours instead of days.

Security & Compliance


Adhered to HIPAA, ensuring safe handling of sensitive patient data.

Sample Skills of Resources

  • Cloud Engineers: Designed and implemented AWS architecture for scale and security.

  • Data Scientists: Trained ML models for parsing unstructured data and detecting anomalies.

  • Data Engineers: Built ETL pipelines and data lake solutions using Glue, Spark, and Airflow.

  • Security & Compliance Experts: Ensured architecture met stringent regulatory requirements.

  • DevOps Specialists: Automated deployments, monitoring, and rollback strategies.

Tools & Technologies

  • Cloud & Storage: AWS (S3, Redshift, Lambda, EMR, Kinesis)

  • Data Processing: Apache Spark, Glue, Python

  • Machine Learning: scikit-learn, TensorFlow, AWS SageMaker

  • Data Visualization: Power BI, Tableau

  • Orchestration: Apache Airflow

  • Security: IAM, KMS, CloudTrail

  • Monitoring: CloudWatch

Healthcare data being processed and visualized on a scalable cloud platform with machine learning integration.

Conclusion

By integrating cloud-native architecture with machine learning, Curate enabled the healthcare provider to transform their data processing capabilities. The new system provided faster, more accurate insights, scaled with growing demand, and adhered to the strictest compliance standards—paving the way for improved clinical outcomes, smarter decision-making, and greater operational agility.

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