How Public Health Professionals Use Data to Drive Change

1. Data Infrastructure Interoperability and Modernization

CDC’s “Public Health Data Strategy” and One CDC Data Platform (1CDP)

In January 2025, the CDC (Centers for Disease Control and Prevention) released a new Public Health Data Strategy. Its top objective is to facilitate less difficult and faster sharing of health facts amongst all US tiers of presidency (e.g., federal, kingdom, tribal, nearby, and territorial).

The central objectives of this strategy:

• Produce high-priority data like reports, laboratory tests, hospital emergency visits, vaccinations, and wastewater testing in a timely manner and higher quality.

Establish a safe virtual platform named One CDC Data Platform (1CDP) via which CDC and different fitness corporations can proportion statistics and work with similar equipment.

By 2025, objectives include:

• Automate electronic case reporting (eCR) entirely, particularly in rural and tribal settings.
• Roll out FHIR technology into additional states so death data can be shared quicker.
• Embrace a common standard of data by culling outdated and cumbersome systems (e.g., NETSS).
• See at least 35% of states report wastewater data timely—and greater ELR adoption (Electronic Lab Reporting).
• Build dashboards with merged data such as case and wastewater to inform decision-making by officials.
• CDC will now focus on including social and environmental factors, not just medical, in its reports.
• The power of AI is being further enhanced in 1CDP to help identify threats in advance.
• A shared workspace is being launched where partner agencies can access three key data sources and five tools.
• Common legal agreements are being created to facilitate data sharing, which will be adopted by 20–30% of states.

All these initiatives translate into the fact that how data is gathered is becoming automated, consolidated, and quicker, going away from antiquated, disparate, and manual practices.

2. AI, anomaly detection, and expert understanding

Surveillance through the assistance of AI

Artificial Intelligence (AI) has also entered the arena of public health now. With the use of AI, any abnormality or unusual trend in health data can be detected instantly.

The new AI-driven system can scan as many as 50 lakh data points* in a day and provide results 54 times quicker than traditional methods.

Role of human intelligence

Human thought continues to be essential despite AI.

• It was discovered that when health officials relied on their own experience to forecast which flu would spread the most, their forecasts were generally more accurate than computer models. For that reason, it is now being proposed that human expertise must also be documented as an independent “data source.”.

Toward data equity

Now it is being observed that merely gathering data is not sufficient—it must be ensured that the data truly reflects every community.

• In August 2025, a new framework was released that stated that there must be transparency and accountability and fairness in each phase of data (e.g., collection, analysis, decision-making).

3. Digital transformation within health systems

AI utilization in NHS (UK)

The United Kingdom’s Health Service (NHS) is also shifting towards digital technology:

• It will soon launch in November 2025 an AI system that will automatically review hospital statistics (e.g., stillbirth rates) and trigger an alarm so that appropriate action can be taken. The 10-year plan of the NHS also discusses developing a “patient passport”*—i.e., everything that is known about the patient medically should be brought together in one place. But there are also technical and privacy issues ahead of it.

Digital Health Skills in Europe—Susa Project

In 2025, the EU initiated the Susa Project, where health professionals are being educated in digital solutions, AI, and data literacy.

4. Global and community-based innovation

CDC’s “Global Public Health Data Innovation” (GPHDI)

CDC’s new global initiative is fortifying global health systems by:

• Stabilizing data reporting to act rapidly in response to emergencies.
• Developing united health systems within nations.
• Delivering training and workforce development in health data.

Community voice and data ownership

The CDC Foundation is stressing that data must not be imposed top-down, but that local communities must be engaged and that their data must be owned by them.

Innovation in low-resource countries

Kenya and Lesotho are among the countries that have used cloud-based tools* to automate infectious disease sample tracing.

• Integrating environmental information with hospitalizations of children in Louisiana (USA) showed that indicators like air pollution could also be the underlying cause of medical issues.

5. Data visualization and analytics

Public health officials can influence policy change by visualizing data in easy-to-use graphics and charts* The CDC is moving legacy systems to the cloud to speed up reporting and decision-making. MENDS tools allow for monitoring diseases like high blood pressure and diabetes at the zip code level.

6. There are still challenges.

Disrupted data and disrupted tools—forcing the health workers to go back to phone calls or paper. Most health systems are still run on paper or using old technologies, which make it hard to implement AI and integrate data. Skills imbalance—the health workforce does not have digital and data analytics capability. Inequities in data—there are communities that do not have their data properly documented, so policies do not work for them.

7. Conclusion—How data is bringing change

Speed and intelligent decisions

With the help of AI now, dashboards, and automation, the health officials can identify a disease or threat quickly and act right away.

Scalability and connectivity

With tools such as 1CDP and global tools such as DHIS2, health systems are becoming more interconnected, agile, and quick.

Skills and accountability

Sharing of data is occurring with tools such as Susa and legal contracts, but transparency and confidentiality are also being addressed.

Data equity

Public health policies now are not only concerned about statistics but also with the ground truth and voice of each community as well.

Human + machine partnership

When human understanding and experience are added to the potency of AI, decisions turn out to be more effective, fair, and accurate.

Final Thoughts

Nowadays, public health is not most effective the paintings of doctors and nurses but an included method of statistics, generation, AI, and community involvement. This trade not best fights disorder however additionally assures that every network is heard and nobody is omitted.

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