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Kentucky COT AI policy (Oct. 2025): acceptable generative AI use for state employees, data protection, and mandatory agency training.

California HHS IT & Data Strategic Plan: AI integration, data governance, and digital transformation goals through 2028.

Learn about how five jurisdictions approached self-measured blood pressure monitoring programs to reduce hypertension and uncover systemic barriers to care.

Overdose Data to Action (OD2A) Case Study: New York State’s Power of Voice Program

Olmsted County Pilots a Regional Population Health Data Hub to Improve Data Accessibility Gelila Tamrat, Sara Black, Reema Mistry, Christina Severin Olmsted County, Minnesota, pilots a regional population health data hub to improve data accessibility, which supports improved decision-making and interventions. Historically, Olmsted County and other local counties in southeast Minnesota have faced barriers to accessing timely and actionable public health data, including limited data analytics workforce capacity, lack of data-sharing agreements (DSAs), and misaligned data suppression standards. To address these challenges, Olmsted County Public Health Services (OCPHS) piloted a regional population data hub, in partnership with the Minnesota Department of Health (MDH) and 10 local health departments (LHDs). OCPHS procured resources to develop a regional data-sharing platform, expanded their epidemiology team, and pursued DSAs. As a result, they gained access to critical data that supports informed decision-making and tailored interventions at the local level. Tina Jordahl - Brief - Olmsted County MN DMI Hub Developing a Regional Population Health Data Hub With financial support from the Minnesota legislature in 2021, OCPHS collaborated with MDH and its regional counterparts to develop a regional population health data hub for smaller LHDs to access community-level public health data. OCPHS maintains the hub by managing data from the state, regional partners, and 10 LHDs, and creating data dashboards to support southeast Minnesota counties’ population health data needs. This effort involved building and expanding relationships with MDH unit-specific epidemiologists, working closely with public health system consultants at MDH, and raising awareness of the need for sustained data analytics workforce support. Following the initiative’s success, OCPHS plans to engage with state and local leaders to identify funding sources that can sustain the hub beyond the pilot funding cycle. Promoting Data Accessibility through Strategic Partnerships and Agreements MDH’s Center for Public Health Practice supports public health system consultants, who offer technical assistance and consultation services to strengthen public health infrastructure across Minnesota. The consultant for the southeast region of the state was crucial in linking state and local staff to advance the development of the regional population health data hub. They helped triage and expedite requests from OCPHS by identifying the right points of contact for datasets and legal counsel within MDH. The collaboration of MDH, OCPHS, and participating LHDs facilitated the development of DSAs, which allowed for proper data flow and enabled OCPHS to request data from MDH on behalf of participating counties, reducing the need for each county to request data. It also helped OCPHS to become the first county in the state to adopt CDC’s ESSENCE tool to monitor hospital visits for syndromic surveillance across Minnesota and neighboring states, better enabling LHDs to address the needs of communities residing along state borders. Hiring Strategies for the Data Analytics Workforce OCPHS focused on hiring staff to support the regional population health data hub with data expertise, strong communication skills, and a particular interest in population health and social determinants of health. OCPHS created two permanent epidemiologist positions to promote sustainability for that position in the future. To expand their hiring pool, OCPHS relied on Olmsted County’s updated remote work policies following the COVID-19 pandemic when many shifted to remote or hybrid work. They also invited leaders from partner counties to help vet candidates who could support other LHDs’ needs. Meaghan Sherden - Brief - Olmsted County MN DMI Hub Advancing Equity Through Data Accessibility Due to data suppression rules, counties in southeast Minnesota had limited access to county-level data for certain statewide datasets. OCPHS worked with MDH to identify appropriate data suppression standards that supported access to community-level public health data and preserved privacy and security, and collaborated with the county IT department to develop the regional data hub with public-facing and internal dashboards, aligned with the required privacy and security standards. The public-facing dashboards show aggregate data with appropriate suppression standards at county, regional, and state levels. The internal dashboards provide complete data summaries and are protected with appropriate permissions and multi-factor authentication for LHD staff to perform population-level analysis. Providing timely, granular data to participating counties allows LHD staff to develop tailored strategies to address emerging health issues promptly, bridging health equity gaps. OCPHS also integrates standard demographic data on race, sex, gender, and age into its dashboards, enabling regional LHDs to gain deeper insights into their communities and fine-tune equity-centered public health initiatives and interventions. Jenny Passer - Brief - Olmsted County MN DMI Hub Implementation Considerations Foster collaborative relationships across state and local health departments to identify opportunities to share resources when advancing data-sharing efforts. Models in which larger LHDs support key data infrastructure needs on behalf of smaller LHDs may bolster data analytics/epidemiology capacity across multiple LHDs and streamline coordination with key partners at the state health department. Consider how state health department consultant or liaison roles charged with providing technical assistance to state or local partners may help facilitate key connections between state and local health department staff pursuing cross-jurisdictional data-sharing efforts. Invest in data analytics/epidemiology workforce strategies that help address specific needs related to population health and relationship building, along with technical skills. Cross-jurisdictional data-sharing efforts require staff with strong data analytics and communication skills, as they work with multidisciplinary leaders and across jurisdictions to inform community-based interventions. Collaborate proactively with legal and IT departments to identify data governance solutions and technical approaches to adhere to required privacy and security standards. Establishing DSAs is important, as it allows sharing of data within required legal guardrails. Similarly, IT leaders can identify technological solutions that support effective access to data. OT18-1802 website yes

This brief dives into the impact of the COVID-19 pandemic on the ability of people with disabilities to access vital health care services during the public health emergency.

4-pillar epidemic intelligence framework adding AI decision support to respiratory disease surveillance, risk evaluation, and early warning.

5 steps for public health leaders to use AI responsibly: build awareness, pilot uses, manage risk, invest in staff training.

Michigan DTMB guidelines for ethical AI adoption: data classification, human-in-the-loop oversight, and equitable access for state agencies.

Gallup/West Health poll (2025): 25% of Americans use AI for health info; 59% before doctor visits, 56% after—mainly as a care supplement.

Op-ed arguing public health's real risk is AI hesitation, not adoption—urging professionals to engage and shape AI use. Forbes, 2026.

Guam OTECH AI use policy (Jan. 2025): responsible AI guidelines for government agencies—data privacy, human oversight, and prohibited uses.

HSCC living glossary of healthcare AI terms—governance-ready definitions for clinical, operational, compliance, and technical stakeholders.

San José city AI use policy: transparency, privacy, bias prevention, and staff accountability. Aligned with GovAI Coalition principles.

ASTHO and partners explore public health AI: ethics, equity, privacy, and practical applications.

Washington State WaTech AI policy (Dec. 2025): agencies must apply state AI principles, assess high-risk systems, and protect non-public data.

Arizona Department of Health Services Pursues Policies to Advance Data Sharing with Tribal Nations Erik Skinner, Christina Severin, Reema Mistry The Arizona Department of Health Services is pursuing policies to advance data sharing with tribal nations, centered around partnerships, education, and more. With leadership support and funding to modernize its public health infrastructure, the Arizona Department of Health Services (ADHS) is pursuing policies to advance data sharing with tribal nations. This includes investing in partnerships with tribal leaders, educating the public health workforce about tribal governments and tribal health care, and working to improve data identification processes to support effective data sharing between the state and tribal nations. Data sovereignty is an important consideration for ADHS, as there are 22 federally recognized tribal nations in Arizona. ADHS recognizes the inherent right of tribal nations to access their citizens’ public health data and is developing a tribal data sovereignty policy that both acknowledges their unique data needs and aligns with state requirements around tribal engagement. Leadership Support and Effective Tribal Engagement ADHS leadership understands the importance of making strong connections with tribal nations and recognizing each nation’s public health priorities while meeting its statutory requirement to develop tribal consultation policies. To that end, ADHS developed the tribal liaison position to serve as a resource, advocate, and communication link between ADHS and Arizona’s Native American health care community partners, including tribal community leaders, health and epidemiology directors, Indian Health Service (IHS), and Tribal Epidemiology Centers (TECs). Understanding cultural norms is essential to building trust with tribal partners; the tribal liaison role has been vital to ADHS engagement with tribal nations on data sovereignty topics. People and processes are important to establishing data sharing policies, and a well-informed workforce is essential for effective collaboration with sovereign tribal nations. ADHS is working with the Native Nation Institute to provide training on tribal sovereignty and cultural humility for staff. It has also developed a tribal handbook for public health staff on sovereignty, cultural trauma, and the roles of IHS and TECs. Identifying Tribal Affiliation within Datasets and Tribal Public Health Priorities ADHS conducted a data assessment to identify instances in which data sharing was active and ongoing between ADHS and tribal nations, and instances in which it had expired. A notable technical challenge was identifying tribal members within existing datasets, as many public health datasets are incomplete (e.g., do not include tribal affiliation) or rely on IT systems that are unable to aggregate data appropriately—making it difficult to ensure tribal authorities receive relevant, comprehensive public health data for their communities. In addition, because each tribal nation’s public health priority areas and data needs could differ from the data that state health information systems collect, sharing relevant data with tribal nations can be challenging. ADHS is working with each nation to identify tribal public health priority areas, find solutions to identify tribal data within state collected datasets, and share it with the respective nations. Ken Komatsu - Brief - AZ DHS Pursues Policies to Advance Data Sharing with Tribal Nations Honoring Sovereignty in Data Sharing Relationships Data sharing agreements with public health agencies often establish that the state agency controls the disposition and use of the data, and that each party benefits. Acknowledging that tribal partners are entitled to their citizens’ data without conditions differs from how ADHS has historically approached data-sharing relationships with others. ADHS plans to formally establish a non-transactional data sharing policy with tribal public health partners, and establish data sharing agreements that align with this approach going forward. Implementation Considerations Considerations for state health agencies in fostering strong relationships and effective engagement with tribal partners around data-sharing efforts include: Center tribal sovereignty when framing data sharing agreements with tribal nations. Engage tribal liaisons in data-sharing efforts with tribal nations. They maintain close relationships with tribes and can help develop mutual cultural understanding, which is essential to engaging tribal partners. Assess datasets to determine data completeness with regards to tribal affiliation and identify opportunities to improve comprehensive data sharing with tribal authorities. Invest in state health agency staff training on tribal sovereignty and cultural humility, so staff can be well-prepared when engaging in data sharing conversations with tribal partners. Gerilene Haskon - Brief - AZ DHS Pursues Policies to Advance Data Sharing with Tribal Nations OT18-1802 website yes

Learn how retail program standards improve retail food safety in this infographic.

Explore key actions, indicators, and supports to enhance workforce recruitment and hiring in governmental public health.

Policy Approaches to Improve State and Local Data Sharing Health officials can pursue organizational policies across key priority areas to advance state and local data sharing. Learn about these policy approaches. Several factors impact effective data sharing between state and local health departments—which is vital to public health decision-making—such as legislation and regulations, limited funding, IT and workforce resources, departmental processes, leadership buy-in, and data governance. Health officials can pursue organizational policies across these key priority areas to advance data sharing practices. Get the Infographic (PDF) website yes