Transforming population health requires collaboration across disciplines, sectors, and borders. We follow CDC's proven model of multi-sector engagement to accelerate innovation while maintaining mission alignment.
Following CDC's leadership in collaborative AI development, we engage diverse stakeholders to ensure solutions are effective, ethical, and sustainable.
State, Local, Tribal, & Territorial (STLT) Health Departments: Partnership model designed for frontline public health agencies to co-develop AI solutions addressing real operational needs. Proposed collaborations include pilot programs, technical assistance, and workforce training.
Federal & International Organizations: Framework designed to align with CDC, WHO, and other leading health agencies, ensuring work follows evidence-based best practices and contributes to global public health infrastructure.
Schools of Public Health: Collaborative research projects with leading academic institutions advance the science of AI for population health, from methodological innovation to implementation science.
Research Partnerships: Joint publications, data sharing agreements, and validation studies ensure our work meets rigorous scientific standards and contributes to the evidence base.
AI/ML Platform Providers: Strategic partnerships with technology companies enable us to leverage cutting-edge tools while maintaining data sovereignty and mission focus for public health agencies.
Health IT Vendors: Interoperability with electronic health records, laboratory information systems, and public health surveillance platforms ensures seamless integration into existing workflows.
Community-Based Organizations: Grassroots health advocates and trusted community partners ensure AI systems serve diverse populations and address health equity from design through deployment.
Patient & Community Engagement: Stakeholder input guides ethical AI development, identifies unintended consequences, and ensures solutions are culturally appropriate and accessible.
Multiple pathways for collaboration depending on your organization's goals, capabilities, and resources.
Partnership framework enables health departments to pilot AI solutions in real-world settings. Proposed collaboration includes technical implementation, evaluation frameworks, and capacity building—partners provide operational context and data.
Scope: 6-12 month pilots with pre/post evaluation
Cost: Grant-funded opportunities available
Academic researchers can collaborate on methodological innovations, validation studies, implementation science, and ethical frameworks. Joint publications and grant proposals welcomed.
Focus Areas: AI fairness, causal inference, privacy-preserving ML, forecasting methods, implementation science
Propose ResearchMulti-party data collaboratives enable federated learning across jurisdictions while preserving data sovereignty. We facilitate governance, technical infrastructure, and privacy protections.
Approach: Federated learning, differential privacy, secure multi-party computation
Explore Data PartnershipsHealth IT vendors and AI platform providers can integrate our open-source tools, contribute to shared infrastructure, or collaborate on interoperability standards.
Standards: HL7 FHIR, SNOMED CT, ICD-10, LOINC
Technical PartnershipTraining programs designed for health departments cover AI fundamentals, responsible deployment, and practical implementation. Workshops can be tailored to organizational readiness and goals.
Format: In-person, virtual, hybrid
Duration: Half-day to multi-week programs
Support for developing organizational AI policies, governance frameworks, and compliance strategies aligned with NIST, OMB, and CDC guidance.
Services: Policy drafting, risk assessment, ethics review, regulatory navigation
Request AdvisoryOur collaborations are guided by core values ensuring mutual benefit, ethical practice, and public health mission alignment.
All partnerships must advance the public health mission: protecting and promoting population health, advancing health equity, and serving the public good. Commercial interests are subordinate to public benefit.
Partner organizations retain ownership and control of their data. Privacy-preserving techniques enable collaboration without compromising security or violating regulations (HIPAA, GDPR).
Benefits from AI innovations should be distributed fairly. Smaller health departments and under-resourced communities receive priority access to ensure technology doesn't widen health disparities.
Partnership terms, data use agreements, and evaluation metrics are documented clearly. Conflicts of interest are disclosed. Model performance is reported honestly, including limitations and failures.
Where feasible, we publish methodologies, share code, and contribute to open-source tools. Reproducible science accelerates field-wide progress and enables independent validation.
Partnerships should build long-term capabilities, not create dependencies. We prioritize knowledge transfer, workforce training, and sustainable infrastructure over turnkey services.
We are actively establishing partnerships with public health agencies, academic institutions, and technology organizations. Our collaborative model follows CDC's proven approach: multi-sector engagement, shared governance, and commitment to responsible innovation.
Interested in partnering? Contact us to explore opportunities aligned with your organization's mission and capabilities.
Whether you're a health department seeking implementation support, a researcher proposing a study, or a community organization advocating for health equity—let's explore how we can work together.