Case study

Scaling AI-Powered Clinical Predictions and Compliance Through Smarter MLOps

About Client

A leading US-based healthcare technology provider supporting over 200 hospitals and clinical labs.
With an annual turnover of over $600 million and operations across North America, they specialize in clinical decision support systems (CDSS), diagnostic AI tools, and patient risk prediction models

Problem STATEMENT

The client was encountering growing challenges in running AI models reliably within clinical environments. As usage expanded, gaps began to surface, impacting trust in the models and their real-world effectiveness.

Irregular model retraining: AI models used for patient risk scoring and disease prediction were not being retrained on a consistent schedule, increasing the risk of outdated insights and potential clinical inaccuracies.
Compliance and audit gaps: Limited documentation and traceability across the ML lifecycle made it difficult to meet strict regulatory requirements, including HIPAA and FDA guidelines.
Disconnected model deployments: Models were deployed in silos across different product teams, with no centralized system for deployment, versioning, or performance monitoring.
Lack of real-time model visibility: Clinicians had little to no real-time insight into how models were performing during patient interactions, reducing transparency and confidence in AI-assisted decisions.

Solution

To address these challenges, the team at DataToBiz implemented a scalable, secure, clinician-focused MLOps framework on AWS. The setup combined AI/ML engineering, data pipelines, DevOps practices, compliance controls, and visualization tools to operationalize AI end-to-end in healthcare.

Data engineering and integration: Secure ETL pipelines pulled data from EHRs, clinical labs, and third-party sources. The Medallion architecture (Bronze-Silver-Gold layers) ensured data quality, traceability, and validation at every stage.
Model development and automation: Amazon SageMaker handled model training, tuning, and deployment pipelines. MLflow tracked versions, and automated retraining workflows were triggered by data drift or clinician feedback to keep models accurate.
Monitoring and governance: CloudWatch and SageMaker Model Monitor enabled real-time drift detection and compliance alerts. Audit logs and model lineage supported HIPAA and FDA readiness, while role-based access control kept data secure.
Visualization and clinician feedback: Quicksight dashboards provided clinicians with real-time predictions and confidence scores. Feedback tools were integrated into workflows to iteratively refine models.

This approach ensured AI models were reliable, compliant, and easy for clinical teams to use in practice.

Technical Implementation

Cloud Infrastructure & Security

Deployed the entire solution on a HIPAA-compliant AWS environment, ensuring data privacy, regulatory compliance, and secure model operations.
Implemented AWS Key Management Service (KMS) and IAM policies for encryption, access control, and data protection.
Enabled CloudTrail logging and audit trails for traceability and FDA readiness.

Data Ingestion & Storage

Built robust ETL pipelines using AWS Glue to extract data from EHRs, lab systems, and third-party clinical sources.
Stored ingested data in Amazon S3, structured using a Medallion Architecture (Bronze, Silver, Gold) to ensure data quality and lineage.

Data Processing & Feature Engineering

Utilized PySpark and AWS Lambda for distributed data transformation, preprocessing, and feature engineering.
Applied custom rules for handling missing clinical values, standardizing lab formats, and encoding patient data securely.

Model Training, Versioning & Deployment

Used Amazon SageMaker for model development, hyperparameter tuning, and automated training.
Integrated MLflow for model versioning, reproducibility, and lifecycle tracking.
Containerized models using Docker and deployed them through CI/CD pipelines on SageMaker endpoints.

Monitoring & Drift Detection

Enabled live performance monitoring using Amazon CloudWatch and SageMaker Model Monitor to track prediction accuracy and detect data/model drift.
Set up automated retraining triggers based on drift thresholds, ensuring clinical models remain accurate and relevant.

Visualization & Feedback Integration

Developed clinician-facing dashboards using Amazon Quicksight, displaying prediction scores, confidence intervals, and patient-level insights.
Embedded feedback mechanisms within the EHR workflow, allowing medical staff to rate model relevance and suggest improvements.

Technical Architecture

Business Impact

Stronger model governance
Full model lineage, version control, and audit tracking ensured end-to-end traceability. Teams could now review model history and changes easily, achieving nearly 80% improvement in governance and audit readiness.

Faster and smoother deployments
Automated CI/CD pipelines reduced model deployment timelines from two weeks to about five days, enabling teams to roll out updates quickly and reliably.

Reliable AI services
Robust monitoring, automated alerts, and failover mechanisms kept AI services highly available, achieving 95% uptime and reducing disruptions during clinical operations.

Actionable insights for clinicians
Real-time dashboards and confidence scores integrated into EHR systems gave clinicians immediate, actionable insights, improving decision-making at the point of care.

Regulatory compliance ensured
With proper audit logs, model documentation, and governance in place, the solution met HIPAA and FDA SaMD requirements, allowing AI modules to reach production and patients faster.

Through this collaboration, the client was able to streamline their AI operations, improve model reliability, and make data-driven insights more accessible to clinicians. The partnership helped put AI into practice safely, efficiently, and in a way that supports better patient care.