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Fairfax County Health Department Modernizes Infrastructure to Optimize Public Health Data Sharing Susan Fluerant, Reema Mistry, Christina Severin Learn how the Fairfax County Health Department in Virginia is working to modernize its data infrastructure & optimize public health data sharing. Using lessons learned from the COVID-19 pandemic, the Fairfax County Health Department (FCHD) is coordinating with county agencies, the Virginia Department of Health (VDH), and nonprofit public health organizations to modernize its data infrastructure and optimize public health data sharing functions. FCHD’s Informatics and IT team engaged in a strategic process to develop a multiyear IT roadmap, which outlines solutions and systems required to support this modernization, including clinical services, communicable disease surveillance, laboratory information systems, communication systems, and solutions for community engagement and administrative needs. As a result, FCHD is well-positioned to leverage its robust IT and data infrastructure to align with VDH for better data sharing. Advancing IT and Informatics Infrastructure Challenges associated with manual data-sharing processes (e.g., the use of CSV files to ingest data and produce analyses) during the COVID-19 pandemic prompted FCHD to reassess its informatics capacity. It conducted department-wide needs assessments and identified the need for improved technology, increased workforce capacity, and better data governance policies. It then ascertained IT functions that could be outsourced (e.g., cloud-hosted solutions and managed services for FHIR HL7 implementation) versus core public health functions (e.g., manipulating and managing data for epidemiologic use) that needed to remain internal, allowing FCHD to preserve internal staff capacity for key public health activities. Public health staff found working with CSV files to be inefficient for large or frequently updated datasets. To address this challenge, FCHD worked closely with VDH and the Fairfax County Informatics and IT team to adopt automated processes and develop a data warehouse solution—allowing FCHD to manage and transform data from multiple sources as well as control the frequency and timing of data retrieval, enhancing their ability to respond promptly to public health needs while keeping data secure on cloud-host servers. VDH hosted an API for FCHD to retrieve death data, significantly increasing flexibility and reducing the need for manual intervention. As FCHD increases the use of new data sources and technology, it continues to develop data governance policies, roles/responsibilities for data users, and data safeguarding guidance. Noel Clarin - Brief - Fairfax VA DMI to Optimize Public Health Data Sharing Engaging State and Local Partners FCHD recognizes that data sharing agreements are vital to support data exchange and, until recently, relied on informal data sharing agreements among other jurisdictions in Virginia. It partnered with attorneys at the state and local levels to work toward a universal data sharing agreement, which can expedite the data sharing process when public health programs have new use cases for local data. As a result, FCHD executed a Memorandum of Understanding with VDH in less than six months—a notable improvement from past data sharing agreements, which historically could take over a year to execute. They worked collaboratively through complex technical and bureaucratic challenges, highlighting the iterative nature of establishing effective data sharing protocols. FCHD also supported relationship-building between programs and divisions within the Fairfax County government, allowing for greater strategic alignment around emerging technologies. Interdepartmental relationship-building between public health program staff and the Fairfax County IT team has been equally important to foster effective collaboration and identify data modernization champions within FCHD. Alida Laney - Brief - Fairfax VA DMI to Optimize Public Health Data Sharing Planning for Sustainable and Diverse Funding Sources FCHD received an initial grant from the Public Health FHIR Implementation Collaborative through NACCHO, which helped launch the IT infrastructure improvement work; however, one-time funding is not a long-term solution. Now, FCHD is developing a long-term funding strategy to support the costs of IT systems licensing, operations and maintenance, and research and development. FCHD recognizes as public health changes, so must the technology that supports it, and modernization will require sustainable funding from government, associations, and other sources. Centering Health Equity in Informatics Solutions FCHD is developing processes to collect and standardize data disaggregated by sexual orientation and gender identity, race and ethnicity, language, and the social determinants of health to better identify and address health disparities. By incorporating additional data into its systems, program staff can now make informed decisions to strengthen health literacy by disseminating information in preferred languages and easy-to-read formats, and ensuring materials are accessible to persons with colorblindness. Ben Klekamp - Brief - Fairfax VA DMI to Optimize Public Health Data Sharing(2) Implementation Considerations Foster relationships within and across local and state government to get buy-in for the modernization of public health informatics infrastructure and the improvement of data-sharing practices. Define clear goals for implementing new technology, and build a strategy for infrastructure improvement, partner engagement, and long-term sustainability that’s grounded in a shared understanding of the goals. Align workforce responsibilities, process improvement efforts, and technological advancements. Develop universal data sharing agreement templates between state and local health departments to expedite future data-sharing efforts. OT18-1802 website yes

Learn how state public health programs like Title V can help sustain community engagement infrastructure.

Communicating About Disease Forecasting Effectively Communicating About Disease Forecasting Clearly communicating about disease forecasting helps policymakers, the media, and the public make informed decisions in public health emergencies. Communicating effectively about disease forecasting data is essential for the public to understand the associated risks, implications, and recommended actions. It’s also critical for policymakers who can use the data to determine effective, equitable strategies for outbreak response. In turn, the public can make informed decisions to keep safe. Each audience, including the media, will likely need tailored messages about the indications and limitations of a given forecast. Approaches for communicating about disease forecasts/models with policymakers and the public align with many of the key tenets of public health communications more broadly. Public health practitioners should consider integrating Crisis and Emergency Risk Communication (CERC) principles, which suggest that communications be first, right, credible, empathetic, actionable, and respectful to help the public make informed decisions during challenging circumstances. Communicating with Policymakers Policymakers—public health or elected—have important decision-making and rule-making authority; additional considerations may be needed when communicating with them about disease forecasting. Interactions frequently occur through staffers, who may have limited knowledge and/or time. In addition to CERC principles, communications should also be: Relevant. Tailor communications to policymakers’ priorities to emphasize potential impacts on resource allocation, public health outcomes, local or regional policies, and public sentiment. Collaborative. Foster relationships with policymakers and offer subject matter expertise on forecasting to inform policy decisions. Timely. Provide frequent updates, allowing for proactive collaboration, decision-making, and communication with the public. Concise. Develop briefs that summarize forecasts and their implications for constituents. Consistent. Build trust with policymakers and their staff between emergencies to highlight disease forecasting as a useful tool whose methodologies, data sources, and limitations are clear. Communicating with the Public In addition to CERC principles and in collaboration with media partners, communication about disease forecasts/models with the public should be: Accessible. Provide easily accessed information about methodology, data sources, and limitations in the forecasts. Clear. Use jargon-free, non-technical language to convey critical messages. Contextualized. Frame forecasts within the broader public health context, emphasizing how individual and community actions can mitigate risk, and reinforce the effects of collective actions as part of an outbreak response. Empowering. Give the public reasonable, actionable steps to protect themselves and their communities. Local. Use social media, news releases, listening sessions, and other relevant means to reach as broad an audience as possible. Collaborate with community leaders and health care providers to amplify messaging and reduce misinformation. Frequent. Keep the public informed with regular updates on forecast products including changes in local risk levels, new trends, guidance, and recommendations. Utah’s Best Practices for Communicating Forecasts During the COVID-19 response, the Utah Department of Health and Human Services (DHHS) modeled potential disease transmission and shared how to effectively communicate with the media and public. DO Create diverse forecasting and communications teams that include public health, universities, and ‘bonus’ team members (e.g., political representatives, laboratories, and health economists). Clarify the purpose for which a given model was developed. Discuss the limitations of the disease model. Communicate about future outcomes (i.e., what would happen without further intervention). Provide language on how to alter the current path (e.g., ‘With this intervention, we could potentially avoid…’) Remind audiences that models can affect the future, not only predict it. Share information and data that are digestible for the public. DON’T Sound definitive when outcomes aren’t clear or make predictions using absolute terms. Omit a variety of scenarios or tools when presenting a forecast. Disregard inherent uncertainties in forecasts. Show models with conflicting results. Utah DHHS also recognized that members of the media are key partners in communicating public health messages; closely coordinating and contextualizing forecasts helps communicate accurate conclusions and/or recommendations. Communicators and staff should be prepared and have information ready to share for more complex questions as needed. Storytelling with forecasts and models helps the reader internalize them and make informed decisions. CDC-RFA-OT18-1802 2018-2024 article yes

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Systematic review of AI in early warning systems for infectious disease surveillance. 67 studies identified. Frontiers in Public Health, 2025.

Nature Water perspective on ML-augmented wastewater-based epidemiology for pathogen detection and global disease surveillance.

Idaho ITS AI governance policy (Aug. 2025): ethical use, prohibited cases, data classification, and human oversight for state agencies.

CDC's vision for AI in public health: accelerating detection and response, reducing operational burden, and empowering an AI-ready workforce.

BCG analysis of 165M U.S. jobs: 50–55% reshaped (not replaced) by AI in 2–3 years; only 10–15% at risk of elimination. Six-category framework.

TEPHI/UT Austin self-paced online course on AI applications in health communication. 1 CEU credit, 60-day access.

Pennsylvania Office of Administration policy on acceptable and responsible AI use across executive branch state agencies.

Georgia state standard for responsible AI use: ethical principles, data governance, and compliance with state IT standards.

City of Seattle 2025–2026 AI plan: framework for responsible AI adoption across city departments with equity and transparency goals.

Nebraska NITC AI policy governing AI tool use by state agencies: acceptable use, data handling, and risk evaluation.

Creating an Informatics Job Classification Series for Health Departments Creating an Informatics Job Classification Series for Health Departments Ari Whiteman Learn why public health informatics jobs are critical for health departments in this brief. Why a Public Health Informatics Job Classification Matters The push to modernize public health data infrastructure has highlighted that traditional job classifications (e.g., Epidemiologist, IT Specialist, or Developer) do not fully capture the unique skill set and value of public health informaticians. Informatics professionals specialize in integrating, standardizing, and managing health data systems to ensure interoperability across platforms and agencies. Their expertise includes health data standards (e.g., HL7, FHIR, LOINC, USCDI), database management, extract-transform-load (ETL) processes, business intelligence tools, and cross-system data exchange, all of which are skills and proficiencies needed for data modernization transitions and processes. According to the 2022 ASTHO Profile of State and Territorial Public Health, 25 states and territories did not have a dedicated public health informatics job classification series, which would create several key advantages over forcing informaticians into traditional public health classifications that may not adequately reflect their job duties or the expertise needed to perform them: Recruitment: Public health informatics job descriptions are likely to attract more qualified and better-fit candidates for the responsibilities of the role, which differ from that of IT professionals or epidemiologists. Retention: Informaticians often leave for the private sector due to higher pay opportunities, as governmental roles often pay less than similar roles in the private sector. A well-defined classification series with competitive salary benchmarks helps retain talent. Role Clarity: Differentiating informaticians from epidemiologists or IT ensures staff are linked to projects that fit their skills and experience, which can improve performance and morale across disciplines. Future-Readiness: Quality health data infrastructure is a preparedness necessity. Informatics positions enable agencies to respond to emerging health crises by linking response and surveillance data with other state and national reporting systems. Common Challenges and Solutions Anticipating challenges can help maintain progress and understanding across all involved parties. Challenge: Resistance to creating new classifications. Solution: Propose consolidation or sunsetting of outdated roles and highlight national best practices. Challenge: Salary constraints compared to private sector. Solution: Emphasize total rewards (retirement, benefits, meaningful work) and seek flexibility for exceptions. Provide salary data from sources like Lightcast, Healthcare Information and Management Systems Society, American Health Information Management Association, or surveys from the American Medical Informatics Association. Challenge: Limited internal public health informatics expertise. Solution: Build capacity through “train-the-trainer” models, upskilling, and leveraging partnerships with universities. Challenge: Slow civil service processes. Solution: Start early, maintain persistence, and adapt business case arguments to different decision-makers (HR, unions, leadership). See ASTHO’s Data Modernization Primer and Tactical Guides for more information, in particular: See the Tactical Guide on Building, Equipping, and Sustaining a Data Modernization Workforce for strategies for upskilling, training, recruitment, and retention of a data modernization workforce. See the Tactical Guide on Planning Data Modernization Activities for strategies to mobilize a team, conduct current state assessments, develop plans, and gain support from leadership. Challenge: Overlap with other roles (e.g., epidemiologists). Solution: Clearly define distinctions between public health informatics and other roles, then communicate role clarity to benefit morale and efficiency. Conclusion Creating a public health informatics job classification series is both a strategic investment and a practical necessity. By distinguishing informatics as a professional path within public health, states and territories can strengthen their workforce, address long-standing recruitment and retention barriers, and prepare for the rapidly evolving demands of data modernization. While the process requires persistence, clear documentation, and negotiation across HR and leadership, the payoff is a resilient workforce equipped to manage the data infrastructure that underpins modern public health practice. Reference the How-To Guide for more details on creating public health informatics job classification series. Learn More - Brief - Creating an Informatics Job Classification Series OE22-2203 PHIG article yes

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Use Partner Mapping to Power Data Modernization Projects Allen Rakotoniaina, Heidi Westermann, Elyssa Stoops, Charlie Ishikawa Learn how to use partner mapping to clarify and understand your data modernization partners, and tailor engagement strategies for shared projects ownership. Partner mapping is a practical way for state, territorial, local, and tribal public health agencies to identify, organize, and engage the wide range of stakeholders involved in data modernization (DM). This resource helps agencies clarify who their DM partners are, understand their perspectives, and tailor engagement strategies to build shared ownership of DM projects. article yes

PAHO guide on designing AI prompts for public health: covers objectives, audience, tone, format, and treating prompts as living protocols.

UNEP overview of AI's environmental footprint—energy, water, e-waste, emissions—with policy recommendations for greening data centers.