Unlocking the Potential of Next-Generation Scientific Computing Across Federal Health Agencies

Unlocking the Potential of Next-Generation Scientific Computing Across Federal Health Agencies 

The Imperative for a New Paradigm 

America’s federal health agencies stand at a pivotal juncture. Scientific research, public health surveillance, biomedical innovation, and regulatory oversight are becoming exponentially more data-intensive, complex, and time-sensitive. The Centers for Disease Control and Prevention (CDC) must integrate real-time epidemiological data to identify and respond to emerging public health threats. The National Institutes of Health (NIH) is scaling high-throughput genomics, imaging, and AI-enabled discovery science. The Centers for Medicare and Medicaid Services (CMS) is modernizing claims data analytics for value-based care. The Food and Drug Administration (FDA) is navigating new regulatory frontiers in precision medicine and digital health products. Each of these missions demands dramatically more agile and intelligent computational infrastructure. 

Despite deep scientific and technical expertise, the computing environments across these agencies remain heavily reliant on legacy systems that are siloed, brittle, and resource constrained. These environments cannot keep pace with the accelerating demands of multimodal data integration, AI-driven analytics, and cross-agency collaboration. The status quo imposes opportunity costs on innovation, operational efficiency, and ultimately public health. 

Meeting today’s challenges requires more than incremental IT upgrades. It calls for a next-generation scientific computing paradigm that unites cloud-native infrastructure, adaptive computing, multimodal AI, and secure collaborative frameworks across agency boundaries. This paradigm will transform computation from a supporting function into a central enabler of evidence-based science, policy, and action. 

Complexity and Collaboration: Overcoming Siloes 

The computational challenge is not simply one of scale, but requires consideration of a wide range of user requirements. Each agency supports specialized communities—virologists at CDC, health economists at CMS, computational biologists at NIH, regulatory scientists at FDA—each generating and interpreting distinct forms of data. From omics and imaging to claims data and digital health signals, these heterogeneous data sources must be harmonized to unlock their full value. 

Yet today, researchers and analysts often spend more time navigating infrastructure than extracting insight. Scientific computing environments must evolve to support interoperability, reproducibility, and secure data sharing both within agencies and across agencies. Applications built using SMART on FHIR standards offer a compelling model for secure, modular, and interoperable access to health data, and can serve as a blueprint for cross-agency computational tool development. Architectures grounded in FAIR (Findable, Accessible, Interoperable, Reusable) data and software principles are critical to enabling modern data governance and accelerating multi-disciplinary collaboration. 

By enabling seamless data integration and collaboration, agencies can connect the dots across populations, disciplines, and domains. This could mean unifying genomic insights from NIH with real-time surveillance from CDC, linking CMS claims data with FDA safety monitoring, or combining real-world behavioral health data with clinical trial outcomes. The result is faster, more informed action in response to major challenges like emerging infectious disease threats, the chronic disease epidemic, or the costs of health care delivery.  

Next-Generation Computing: Core Capabilities 

To support these complex and converging missions, a future-ready scientific computing infrastructure must deliver the following core capabilities: 

• Elasticity and Scalability: Whether NIH is processing petabytes of multi-omics data or CDC is tracking real-time outbreak patterns, computing resources must scale dynamically and efficiently. 

• Composable Architectures: Modular, container-based platforms allow diverse teams with varying degrees of computational expertise to utilize advanced analytics, AI models, and simulation tools tailored to their domain. 

• Federated, Collaborative Environments: Secure cloud-enabled environments must support cross-agency and external partnerships. For instance, CDC collaborations with state health departments or NIH partnerships with academic consortia should be seamless, compliant, and scalable. 

• Self-Service and User-Centric Platforms: Empowering a diverse user base, including researchers, clinicians, patients, and advocacy groups, with intuitive tools for exploration and analysis reduces bottlenecks and accelerates insight generation. 

• Security and Compliance by Design: From HIPAA to FISMA, federal health agencies must operate in sensitive regulatory environments. Infrastructure should embed robust security, data protection, access controls, and auditability from the ground up. 

Integration of Advanced Technologies 

Next-generation scientific computing is inseparable from advanced technologies. Cloud platforms provide the elasticity and global access needed for high-demand workloads. Large language models, image recognition, natural language processing and other AI methods can automate routine reviews, flag anomalies, and extract meaning from unstructured data at scale. Agencies like NIH are already piloting AI in cancer research, while FDA is applying across a range of regulatory use cases. 

While still in its early stages, quantum computing holds the potential to redefine the boundaries of what is computationally possible. It has demonstrated a unique ability to process complex, high-dimensional data that could unlock breakthroughs in molecular simulation for drug discovery, accelerate epidemiological modeling at unprecedented scale, and revolutionize cryptographic security for sensitive health data. For federal health agencies, now is the time to invest in foundational capabilities and pilot programs to ensure they are ready to harness these transformative technologies as they mature. Strategic foresight today could position agencies at the forefront of tomorrow’s most critical scientific advances.  

Realizing the Vision: Strategic Recommendations 

To enable this transformation, federal health agencies should collectively prioritize: 

1. Investment in Cloud-Native Infrastructure: Agencies must reduce reliance on on-premise infrastructure and adopt scalable, cloud-based computing environments aligned with mission needs. 
2. Enterprise-Wide Data Governance: Agencies should enforce FAIR-aligned governance models to harmonize data access, lineage, and interoperability, both within and across institutions. 
3. Collaborative Scientific Workspaces: Digital sandboxes or virtual research environments should be established to promote safe, auditable, and interdisciplinary exploration of sensitive datasets. 
4. Accessible AI/ML Toolchains: Provision of user-friendly, containerized AI tools will empower scientific staff to integrate predictive modeling and machine learning into existing workflows. 
5. Technology Foresight and Piloting: Agencies should invest in early exploration of nascent technologies such as quantum computing, neuromorphic systems, and edge AI to position themselves ahead of future analytic demands. 

A Broader Reflection on Scientific Computing 

Across the public health enterprise, scientific computing is a mission-critical function. Agencies that invest in advanced, interoperable, and scalable infrastructures will be best positioned to accelerate discovery, modernize policy, and respond rapidly to emerging health threats. 

Moreover, shared platforms and governance models can reduce duplication, promote reuse, and catalyze inter-agency synergies. In an era where collaboration is essential, shared computational infrastructure becomes a strategic asset to promote public health. 

Time to Act 

The missions of our federal health agencies demand a computational infrastructure that is as sophisticated and agile as the science and policy decisions it supports. Unlocking the full potential of next-generation computing is essential to the future of public health. 

With world-class expertise, data assets, and scientific missions, federal health agencies are uniquely positioned to lead this transformation. Now is the time to invest in the platforms, people, and partnerships that will define the next era of health science and public impact.