Healthcare organizations face a transformative moment where data modernization redefines patient care delivery. The healthcare industry’s adoption of AI marks a fundamental change in diagnosis, treatment planning, and patient outcomes. This evolution creates a future of precision medicine and personalized care accessible to everyone.
According to Deloitte’s “2025 Global Health Care Executive Outlook”, 92 percent of healthcare executives are experimenting with or investing in generative AI, demonstrating the industry’s strong commitment to AI-driven transformation. This widespread adoption signals a pivotal moment in healthcare’s evolution, where organizations must either modernize their data infrastructure or risk falling behind in their ability to deliver optimal patient care.
Generative AI’s healthcare transformation potential
Generative AI has the potential to revolutionize healthcare in numerous ways:
Enhanced diagnostics: AI models can analyze medical imaging, in connection with other patient data, to assist in early disease detection—providing more accurate diagnoses in a timely fashion.
Personalized treatment plans: By processing vast amounts of medical literature and patient data, AI can help create tailored treatment strategies for individual patients.
Drug discovery: Generative AI can accelerate the drug discovery process by predicting molecular structures and drug interactions.
Clinical decision support: AI-powered systems can provide real-time insights to healthcare providers, improving decision-making at the point of care.
Administrative efficiency: AI can automate routine tasks, streamline documentation, and optimize resource allocation, allowing healthcare professionals to focus more on patient care.
Patient engagement: Chatbots and virtual assistants, powered by generative AI, can provide around-the-clock support, answer patient queries, and offer personalized health advice.
Predictive analytics: AI models can help forecast patient outcomes and identify high-risk individuals—assisting to prevent hospital readmissions.
Prerequisites for healthcare data modernization
Before embarking on your modernization journey, healthcare organizations must address several critical prerequisites:
1. Develop a comprehensive data strategy aligned with organizational objectives by clearly defining your mission:
Identify specific data challenges across their enterprise customers
Prioritize which data sources to integrate
Define clear metrics for success, including cost reduction and performance improvement targets
2. Invest in skilled personnel and partnerships:
Establish collaboration sessions with all important parties
Organize training sessions for employees on pertinent technologies
Maintain regular technical deep-dives to explore new service capabilities
3. Establish robust data governance and quality management processes:
Standardized processes for handling varied data formats
Quality controls for managing healthcare data at scale
Have clear protocols for maintaining HIPAA compliance
4. Conduct a thorough infrastructure assessment:
Review database operations
Inefficiencies in existing cluster management
Opportunities for cost optimization
By following these prerequisites, your organization can build a solid foundation for modernization. To fully leverage these transformative capabilities, healthcare organizations need robust, scalable, and secure data platforms. To assist facilities and organizations, at all levels, Amazon Web Services (AWS) has many different Healthcare and Life Sciences specific services.
AWS Healthcare specific solutions
AWS empowers healthcare organizations to modernize their data infrastructure by supporting AI-enabled innovation with services like:
Amazon OpenSearch Service: provides AI-powered search, observability, and vector database operations with a secure, cost-effective, managed service.
AWS HealthLake: A HIPAA-eligible service offering healthcare companies a complete view of individual and patient population health data using FHIR (Fast Healthcare Interoperable Resources) API-based transactions to securely store and transform their data into a queryable format at petabyte scale, and further analyze this data using machine learning (ML) models.
Amazon SageMaker AI: Brings together a broad set of tools to enable high-performance, low-cost machine learning for any use case. With SageMaker AI, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more—all in one integrated development environment (IDE).
Amazon Bedrock: Offers a choice of high-performing foundation models (FMs) from leading AI companies through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
These AWS solutions can accelerate your journey to improve operational efficiency and enhance patient care through data modernization and AI integration. To help illustrate let’s walk through a specific use care.
Case study: The transformation of Innovaccer through AWS
Innovaccer, a global healthcare AI and analytics company serving 96,000 providers across 1,600 locations, demonstrates how strategic modernization can overcome common scaling challenges while enabling AI innovation. Their journey began with clear objectives: to enhance data accessibility and improve operational efficiency in healthcare data management. Their AI-powered platform now runs complex analytics on hundreds of terabytes of data each day, serving enterprise-level customers including Kaiser Permanente.
Initially, Innovaccer faced several significant challenges that resonated with many healthcare organizations:
Managing increasing volumes of varied data formats efficiently
Controlling costs while scaling operations
Maintaining performance with growing enterprise-level customers
Processing data from 40-50 different sources in both structured and unstructured formats
Freeing up technical teams to focus on innovation rather than infrastructure management
To address these challenges, Innovaccer implemented a comprehensive modernization strategy using AWS services across several key areas.
Database Optimization became their first priority, implementing:
Amazon Aurora as a PostgreSQL-compatible database for managing online transaction processing and complex analytical workloads
Amazon Aurora I/O optimized for IOPS-intensive applications, resulting in 45 percent savings on monthly Amazon Relational Database Service (Amazon RDS) costs
AWS HealthLake for high-performance, petabyte-scale data warehousing
To enhance their data processing capabilities, they developed:
Custom data pipelines using SQL scripts for extract, transform, and load (ETL) processes
Advanced ingestion systems capable of processing data from 40-50 sources in multiple formats
Amazon EMR with spot instances for complex analytical models and predictive analytics
For AI/ML integration, they deployed:
Amazon SageMaker for hosting vision models using NVIDIA Triton Inference Server
Amazon Bedrock for implementing generative AI workflows and integrating AI into products
To reduce operational overhead, they utilized:
Supporting these technical implementations, Innovaccer invested in their team through:
AWS-organized training sessions on containerization technologies
Comprehensive training programs benefiting over 300 employees across site reliability engineering and product teams
This strategic modernization yielded impressive results:
33 percent reduction in overall cloud computing costs
45 percent savings on monthly database costs through optimization
65 percent reduction in management overhead
30 percent better data processing performance
Ability to process hundreds of gigabytes of data in batch jobs in less than an hour
Successfully processing data from 40-50 different sources in both structured and unstructured formats
“With production automation and improved visibility, our teams are able to concentrate on important projects that previously might not have been feasible due to time constraints,” explains Bill Burton, vice president of cloud engineering at Innovaccer.
This comprehensive transformation enabled Innovaccer to build a foundation for AI innovation, while delivering immediate operational benefits. Their experience provides a blueprint for healthcare organizations seeking to modernize their data platforms for the AI era.
Generative AI for healthcare organizations
Beyond Innovaccer’s specific case, AWS offers a comprehensive set of tools and services that enable healthcare organizations to build a robust data foundation for generative AI. Let’s examine how key AWS services can serve as a blueprint for other healthcare organizations:
1. Data storage and management: AWS data management solutions including cloud databases can transform healthcare operations by efficiently handling diverse data sources, including electronic medical records and medical device data.
2. Data interoperability: AWS HealthLake and AWS Glue facilitate connecting and organizing data from various sources, potentially reducing integration time by half compared to industry standards.
3. Analytics and machine learning: Amazon SageMaker AI and Amazon Bedrock streamline AI model deployment and auto-scaling. The No Code/Low Code approach of Amazon Bedrock enables quick implementation of advanced features like RAG-based systems for multimodal content searching.
4. Security and compliance: AWS integrated security services help maintain HIPAA compliance while processing sensitive patient data at scale, addressing the stringent regulatory requirements in healthcare.
These core services demonstrate how organizations can build a secure, scalable foundation for generative AI initiatives. Your organization’s specific requirements will determine which combination of AWS services will best support your modernization journey.
AWS tailored programs
To help organizations execute their healthcare data modernization, AWS offers tailored programs that address key prerequisites.
AWS Migration Acceleration Program (MAP): Designed for medium to large-scale transformations, this program follows three strategic phases: Assess, mobilize, and migrate and modernize. During the Assess phase (3-6 weeks), AWS experts and certified partners work with you to evaluate your existing systems and develop comprehensive migration strategies. The Mobilize phase extends over 2-4 months, where AWS experts or partners help establish your cloud operations model, build migration foundations, and initiate data consolidation to optimize storage costs. In the final Migrate and Modernize phase, AWS experts or partners guide your database transformation to Amazon RDS, Amazon Aurora, and Amazon DynamoDB.
AWS Healthcare quick start solutions: Ideal for smaller organizations or focused deployments, these pre-built reference architectures allow rapid implementation of common healthcare workloads. Solutions include ready-to-deploy templates for HIPAA-compliant environments, healthcare data lakes, and clinical analytics platforms.
AWS Healthcare Competency Partners: For organizations seeking specialized expertise, AWS Healthcare Competency Partners offer validated technical capabilities and proven customer success in healthcare workload migrations. These partners provide cost-effective implementation services scaled to your needs.
The modernization timeline varies based on scope and chosen approach, typically spanning 3-36 months. Most organizations begin seeing substantial results within 12-18 months, including optimized IT costs, enhanced data processing speeds, and improved database performance. This structured approach helps build a robust, scalable infrastructure ready for advanced analytics and generative AI applications, positioning your healthcare organization at the forefront of innovation.
Conclusion
Investing in data platform modernization today positions your organization to adapt, leverage emerging tech, and deliver superior patient care in an increasingly data-driven, AI-enabled environment.
As demonstrated by the transformation of Innovaccer, modernizing healthcare data platforms creates the essential foundation for generative AI implementation. Their achievements—33 percent reduction in cloud computing costs, 30 percent improvement in data processing performance, and 65 percent reduction in management overhead—freed their teams to focus on AI innovation.
Using this modernized data foundation, Innovaccer successfully implemented generative AI workflows through Amazon Bedrock and deployed advanced vision models through Amazon SageMaker. They demonstrate how a robust data infrastructure can enable AI-driven healthcare transformation.
Modernizing legacy data platforms is crucial for healthcare organizations to effectively leverage generative AI. By partnering with AWS, you can build a scalable data foundation that unlocks the full potential of AI.
We encourage you to take the next steps in your organizations journey to modernization for generative AI. Visit today AWS for Healthcare & Life Sciences to explore AWS healthcare-specific solutions. Or schedule a complimentary consultation with our healthcare specialists and request a personalized assessment of your organization’s data modernization needs.
