How to Modernize Data Infrastructure: A Toolkit for Public Health Leaders

Introduction

Public health data modernization strategies aim to transform outdated, siloed systems into a unified, connected, and resilient data ecosystem. A modernized data infrastructure is essential for responding effectively to public health emergencies, enabling real-time data sharing and data-driven decision-making, and improving public health outcomes. However, there is not a one-size-fits-all approach, and its scope can vary significantly across S/THAs. Its landscape continues to evolve, and agencies will view it through varying lenses based on their specific context, authority, and organizational structure (e.g., an IT director might have a different perspective than a principal investigator or chief information officer given the projects they have worked on, their mandates, and their tenure).

This toolkit recognizes this diversity and is flexible for agencies and their staff at different stages of their data modernization journey. Regardless of their starting point, agencies can adapt the key concepts and recommended actions to fit their specific jurisdictional needs. Together, with systems improvement and change management principles, S/THAs can engage partners and stakeholders to modernize systems that serve public health outcomes equitably and efficiently. Data modernization is dependent on action at the executive leadership and tactical levels and is more complex than straightforward software or hardware solutions. Building on ASTHO’s “Data Modernization Primer” and tactical guides, this toolkit guides agencies on how to modernize data, providing actions for advising leadership, developing strategies and activities, executing projects, and prioritizing and investing for long-term sustainability.

How to Use This Toolkit

S/THAs should review this toolkit with a critical eye and convene with colleagues to discuss what meaningful steps they can take. Then, move forward with a thorough understanding of how to modernize data in their jurisdiction and with a tailored approach to building a modernized, resilient public health data infrastructure. 

1. Mobilizing a Data Modernization Leadership Team

This section highlights key actions for appointing leaders, involving agency-wide stakeholders, and creating a clear vision to move modernization efforts forward.

Action 2: Understand the Data Modernization Landscape

State and territorial health officials (S/THOs) and senior leaders should understand the broader landscape of data modernization efforts at the national, state, and local levels. Becoming familiar with key players, initiatives, and existing frameworks can help drive data modernization in their jurisdiction.

• Understand the role of key national organizations (e.g., CDC, the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology, the Council of State and Territorial Epidemiologists (CSTE), and ASTHO). Familiarize the team with technical assistance and collaboration opportunities to stay informed/aligned with ongoing modernization
• Identify and apply for federal and state funding mechanisms that align with jurisdictional DM efforts, such as CDC’s Epidemiology and Laboratory Capacity Program and the Public Health Infrastructure Grant. Ensure the agency capitalizes on these opportunities to fund data modernization initiatives and adhere to provided guidelines.
• Review the value of key standards, such as Fast Healthcare Interoperability Resources and the United States Core Data for Interoperability, and policies like the Trusted Exchange Framework and Common Agreement. Encourage the DM leadership team to align with these frameworks to foster secure, interoperable health data exchange and meet national data modernization objectives.

Action 3: Create a Strategic Vision

An effective vision statement clearly articulates the agency’s long-term goals for data modernization and desired future state of data systems (including milestones for success), and aligns with CDC DM priorities. This will help guide the direction of an agency’s data modernization journey. The DM leadership team should collaborate with the S/THO and key staff.

2. Planning Data Modernization Activities

This section highlights an approach to support DM directors and leaders in assessing an agency’s current data modernization landscape, identify priority strategies, and sequence activities effectively. Establishing a clear DM plan promotes accountability and enhances communication with all involved.

Action 1: Assess the Current State

A current state assessment helps identify gaps, strengths, and opportunities in a S/THA’s data systems.

• Complete a data modernization assessment: Use tools like Public Health Informatics Institute’s (PHII’s) Self-Assessment Tools or CDC’s Public Health Data Modernization Assessment. If the agency or specific programs have completed an assessment within the last two years, review and update the findings as needed.
• Analyze results: Work with DM leadership to discuss assessment findings and understand possible solutions. Consider how to prioritize projects, and secure additional resources and funding.

Tailor Key Plan Components to Meet the Agency’s Needs

• Collaborate with DM leadership and the S/THO to articulate long-term goals.
• Set strategic goals with short-, intermediate-, and long-term targets, and use clear success measures to track progress. These should be explicitly connected to the DM strategies the agency and advisory committee prioritize. For more information on how to prioritize, see the Identifying and Implementing Data Modernization Projects section in this toolkit.
• Identify potential risks and develop mitigation strategies and clear decision-making processes. 
• Define key messages, delivery methods, and feedback processes to inform and engage staff.
• Review all funding sources, estimate project costs, and compare them with available funds. Strategize to address any funding gaps and regularly monitor financials. 
• Establish a review process to track progress and adjust the plan as needed.

Action 3: Sustain Leadership Support and Expand a Guiding Coalition

Engaging the agency’s executive leadership, fostering the DM leadership team, and continuing to build a modernization coalition is critical to securing the necessary support and resources.

• Leverage leadership influence and authority: Encourage leadership/senior managers to advocate for the plan, secure funding, foster external collaboration, and resolve internal challenges.
• Develop a compelling business case: Prepare a formal document with:
  • Executive summary: Outline the project, benefits, and alignment with organizational goals.
  • Initiative description: Provide details about the business need, goals, scope, and risks.
  • Business impact: Describe how the project will positively and negatively affect business processes.
  • Financial considerations: Summarize funding sources and anticipated costs including capital as well as operating and training requirements.
• Empower DM leadership team members as ambassadors of the business case and encourage them to engage a wider range of stakeholders both horizontally and vertically for each DM priority (known by John Kotter as a “Guiding Coalition”).
• Consider developing a DM-specific operating model and change management strategies: The former will serve as a framework for day-to-day operations, including governance, processes, workforce roles, and necessary technology. The latter will guide the organization through DM-related changes.
  • Communicate often, focusing less on the technical elements and more on the change vision.
  • Use multiple methods/formats to communicate the vision and goals internally and externally.
  • Demonstrate commitment to the prioritized strategies and acknowledge short-term wins.

3. Identifying and Implementing Data Modernization Projects

In this section, S/THAs will learn how to evaluate various activities based on complexity and effort, ensuring that they use resources efficiently to drive significant data modernization outcomes.

Action 1: Identify and Prioritize Data Modernization Activities

As part of the DM planning process, the DM leadership team and stakeholders should identify key data modernization activities. It may be helpful to organize these into three main themes:

• Data governance: Focus on data management, privacy, security, and analytics.
• IT governance: Address the IT strategy, management, and cybersecurity.
• Technology and systems: Develop systems for data storage, exchange, and analysis.

Within each, there are foundational, intermediary, and advanced activities. Consider the level of complexity and the agency’s capacity to implement key data systems strategies. View a strategies table for all themes in ASTHO’s tactical guide “Identifying and Implementing Data Modernization Projects.”

Use an impact-effort matrix to:

• Forecast impact: Determine how much each activity will contribute to the agency’s goals.
• Estimate effort: Estimate the time, money, and resources required for each activity.
• Categorize activities: Map activities into quadrants based on their impact and effort (e.g.: Quick wins: high impact, low efforts. Incremental: Low impact, low efforts. Rethink: Low impact, high efforts. Major projects: high impact, high effort).
• Prioritize activities: Consider focusing on quick wins first, then pursue other activities based on available resources and alignment with agency goals.

Action 2: Address Implementation Challenges and Project Management

Identifying and mitigating potential challenges early is critical to the success of data modernization.

Implementing Project Management Strategies

• Use agile and performance improvement methodologies to develop project plans, monitor performance, and adapt to changes.
• Work with procurement teams to define technical requirements, assess costs, and select vendors using a checklist for procurement efforts.

Action 3: Leverage Technical Assistance and Onboarding Resources

As the DM leadership team formalizes and implements data modernization projects, it is important for agencies to leverage technical assistance and onboarding resources.

• Use external resources for expertise and support in areas like data governance, IT governance, and technology development. Refer to the resource list in ASTHO’s tactical guide “Identifying and Implementing Data Modernization Projects” for technical assistance options.
• Ensure staff have access to necessary onboarding tools/training throughout implementation.

4. Building, Equipping, and Sustaining a Data Modernization Workforce

Data modernization efforts rely on a skilled workforce to design and manage essential systems. This section focuses on assessing workforce needs, upskilling support for staff, and recruiting talent to ensure long-term workforce development aligned with modernization goals.

Action 1: Identify Key Areas of a Data Modernization Workforce

Data modernization requires a knowledgeable workforce. Agencies must assess their workforce structure and identify crucial roles, responsibilities, and departments to lead the efforts.

• Assess workforce needs: Conduct an internal assessment to determine which areas are already covered and where gaps exist. Key areas include executive leadership, data governance, IT, informatics, knowledge management, data analytics/reporting, data users, and partnerships.
• Determine new or expanded roles: Some areas, like data governance or informatics, may require new positions or expanded roles to manage tasks such as data privacy or system maintenance (e.g., if the agency lacks formal data governance, consider naming a data governance officer).
• Engage leadership: Collaborate with department heads to decide which responsibilities will be managed at the program level and which require an agency-wide approach. Secure executive support for new hires or budget adjustments to support these roles.

Action 2: Build Workforce Competency

To keep pace with new technologies, public health staff need ongoing training in data science, informatics, and data management.

• Develop a comprehensive training plan: Again, align the DM training plan with the agency workforce development plan to gauge feasibility and scope in coordination with other agency-wide training efforts. Address data modernization gaps through targeted training programs in areas like statistical analysis, data governance, and informatics. Use platforms such as CDC TRAIN or CSTE’s Data Science Team Training for courses in public health informatics and related subject areas.
• Create time for learning: Allocate specific time periods for training, with leadership support for staff development. Regular training schedules will help sustain skills over time. Additionally, highlight the long-term benefits of investing in staff development, such as reducing inefficiencies, improving public health responses, staff satisfaction, and retention.

Action 3: Develop Candidate Profiles for Specific Roles

When agencies cannot address staffing gaps internally, developing candidate profiles to recruit new talent can support grant submissions and funding requests.

• Create candidate profiles: Identify key roles requiring external recruitment, focusing on specialized skills like data analytics or HL7 standards. Build profiles that define job duties, required skills, and qualifications, such as data analysts or informatics specialists (e.g., a profile for a data analyst might include experience in statistical analysis, proficiency in Python or R, and data dashboard software like Tableau or Power BI).
• Leverage public health networks: Engage with national public health associations (e.g., ASTHO, CSTE, NNPHI, PHAB, etc.) for sample job descriptions and best practices. Also, build peer connections to learn from other agencies' hiring experiences.
• Tailor profiles to needs: Clearly define the job duties, technical/non-technical skillsets, qualifications, and experience required for each position, ensuring alignment with the agency’s data modernization strategy. Use these profiles to support funding requests for staffing needs.

5. Strengthening Data Modernization Through Collaboration and Partnerships

This section provides actions for identifying key partnership areas, engaging with external organizations, and building collaborative networks to strengthen public health data systems.

Action 1: Identify Key Areas for Partnership

Strategic partnerships can help address gaps in technology, workforce, data exchange, or legal expertise.

Review Assessments and Gather Feedback

• Review current and previous data modernization assessments, internal reports, and grant priorities to identify key areas where support from external partners is needed.
• Leverage insights from assessments and other planning activities (see the Planning Data Modernization Activities section of this toolkit) to engage external partners in identifying specific gaps in skills, technology, and resources, and collaborate on strategies to address them.
• Focus on areas where external support can make the biggest impact (e.g., data exchange, data sharing, workforce training).

Align Partnerships with Agency Goals

• Break needs down into key areas, such as planning and implementation, technology and systems, data exchange, workforce, data visualization and analytics, and legal and policy.
• Prioritize partnerships that address health equity in the context of immediate needs and long-term goals, such as collaborating with healthcare providers to improve data exchange for underserved populations.

Action 2: Identify Potential Partners

Engage with specific partners who can support the agency’s data modernization efforts.

• Leverage public health networks: Engage with organizations like ASTHO, CSTE, and APHL for technical assistance, peer networks, and access to best practices. In addition, join collaborative membership networks (e.g., PHII’s DMI Learning Community and ASTHO’s Informatics and Data Modernization Network) to share solutions and strategies.
• Collaborate with academic institutions: Partner with universities for research, workforce development, and mutual learning opportunities. This helps agencies attract skilled workers and offer training programs for existing staff.
• Engage healthcare providers and health Information exchanges: Partner with local healthcare providers, labs, and health information exchanges to enhance data exchange and promote common standards like FHIR. National provider groups (e.g., American Academy of Pediatrics, American Academy of Family Physicians) may also offer collaboration opportunities.
• Collaborate with Tribal nations: Engage Tribal public health agencies and Tribal epidemiology centers to collaborate on initiatives impacting both Tribal and non-Tribal populations. Designate a liaison to manage these relationships.

Action 3: Intentionally Initiate and Maintain Partnerships

Forming successful partnerships requires ongoing communication, mutual understanding, and trust.

• Establish clear communication and roles: Promote open dialogue between partners to ensure mutual awareness of capabilities and goals. Assign points of contact and define roles for all.
• Identify mutually beneficial opportunities: Work together to uncover shared goals and shape a clear value proposition for the partnership, ensuring benefits for both parties.
• Develop a strategic partnering plan: Create a partnership plan that tracks key information (e.g., initial outreach, kickoff calls, meeting cadences, and action items) and assign a lead staff member to manage each partnership.

6. Ensuring Long-Term Sustainability for Data Modernization Efforts

This section focuses on aligning investments with agency needs, making efficient use of available funds, and exploring shared services to meet multiple program needs.

Action 1: Integrate Sustainability into Planning

• Include sustainability topics in DM leadership team discussions: Discuss long-term cost projections and strategies to recruit and retain key staff to sustain modernization efforts. Align sustainability strategies with the DM Strategic Plan, and consider outlining resource strategies and funding allocation directly in it as well as within implementation workplans.
• Consider funding and workforce resources: Assess all program areas to review funding needs and plan for out-year costs to maintain financial viability. Also, create simple metrics that outline the pros and cons of maintaining, improving, or purchasing systems to justify funding requests to state/territorial governments. Finally, develop strategies to recruit/retain qualified staff, utilize federal funds efficiently, and initiate funding requests early to avoid delays.

Action 2: Align Investments with Agency Needs

To ensure data modernization efforts remain sustainable beyond initial funding, it is essential to develop a comprehensive financial plan and make efficient use of resources.

• Prioritize technology purchases that the agency can sustain long term and that align with the agency's most pressing needs and priorities.
• When identifying technology solutions, consider tools that align with interoperability standards and policies (e.g., support applicable transport or messaging standards).
• Explore shared services models and enterprise-level solutions to lower costs, increase efficiency, and reduce duplication. This includes implementing shared applications (e.g., Power BI, REDCap), centralizing IT functions, data file routing, and licensing. Consider interagency collaboration for sharing staff, laboratory services, or data infrastructure.
• Collaborate with other health agencies and national partners to leverage communities of practice for shared problem-solving, resource pooling, knowledge exchange, and technical assistance.