Health Data & AI

Overview

Federal healthcare organizations are rapidly modernizing how they manage data, analytics, and artificial intelligence to improve mission readiness and care delivery.

Across agencies like Defense Health Agency (DHA), Department of Veteran’s Affairs (VA), and Indian Health Service (IHS), success increasingly depends on the ability to transform fragmented data into secure, AI-ready systems that support better decisions, stronger operational performance, and improved patient outcomes.

Challenges

  • Fragmented systems and disconnected healthcare data environments
  • Legacy infrastructure limiting scalability and innovation
  • High administrative burden and manual workflows impacting clinicians
  • Increasing cybersecurity, privacy, and compliance requirements
  • Difficulty structuring and analyzing large volumes of unstructured clinical and other critical data
  • Need for real-time insight to support operational and clinical decision-making

Approach

ATA enables healthcare organizations to transition from fragmented, legacy environments to secure, interoperable, and AI-enabled ecosystems. By combining modern data architectures, AI/ML, and cloud-native engineering, ATA delivers scalable solutions that integrate data, automate workflows, and support real-time decision-making.

Key capabilities include:

  • Enterprise health data platforms and AI-ready analytics
  • Intelligent document processing using OCR and 
NLP to structure clinical data
  • Secure data sharing and interoperability aligned 
to Zero Trust principles
  • Cloud-native application development and DevSecOps modernization
  • AI-enabled operational analytics for forecasting, scheduling, and resource optimization
  • Secure research and analytics environments to accelerate innovation into operations
  • Research to Operations (R2O) and rapid prototyping

Impact

  • Improved patient outcomes and care coordination
  • Reduced administrative burden and increased workforce efficiency
  • Faster, data-driven clinical and operational 
decision-making
  • Enhanced security, compliance, and data governance
  • Greater scalability, interoperability, and mission readiness across healthcare systems

Use Case: Intelligent Medical
Document Processing

Challenge

High-volume medical documents (records, referrals, forms) require manual review, creating backlogs, inconsistent data quality, and slow processing times.

Solution

ATA deploys a secure, cloud-native Intelligent Document Processing (IDP) solution that uses OCR, NLP, and AI/ML to automatically extract, validate, and structure clinical data, with human review for exceptions.

Impact

Reduced manual workload, faster data availability, improved data accuracy, and scalable intake operations that support clinical and operational decision-making.