Modernizing Data Systems in Environmental Public Health

Modernizing Data Systems in Environmental Public Health: A Blueprint for Action

Table 12

Artificial Intelligence Artificial intelligence (AI) technologies are transforming how EPH departments manage data, making it faster, easier, and more effective to extract insights and act on information. Although still emerging in many areas, AI tools are increasingly used to automate routine tasks, uncover hidden patterns, and support more informed decision-making. AI is a complement, not a substitute, for expert judgment. It enhances decision-making by augmenting capacity, automating routine tasks, and un - covering trends that might otherwise go unnoticed. The modernization of EPH data systems relies on the strategic integration of tools across the whole data life - cycle. GIS helps visualize patterns and plan interventions. Visualization platforms translate data into insights. Pro- cessing tools support quality control and analysis. Mobile applications streamline fieldwork. AI adds new layers of insight and automation. Together, these technologies empower EPH professionals to work more effectively, re - spond more rapidly, and engage more meaningfully with the communities they serve. As agencies continue to adopt and expand their use of these tools, the focus remains on usability, scalability, sustainability, and broad accessibility. A modern data en- vironment is not only more efficient but also more capa- ble of meeting the evolving challenges of EPH. Table 12 highlights practical examples of how AI is applied in EPH to streamline operations, enhance analysis, and improve community responsiveness.

AI APPLICATION

USE CASE EXAMPLE

Custom language models trained in agency documents allows staff to ask natural language questions and instantly retrieve policy guidance. Automated report generation uses inspection data to generate summaries, violation lists, or follow-up notices. Anomaly detection flags outliers or unusual trends in inspection or complaint data. Predictive modeling forecasts seasonal complaints, identifies vector hotspots, and assesses staffing needs. Natural language processing (NLP) analyzes open-ended feedback and categorizes themes. Image classification analyzes aerial or drone imagery to detect environmental hazards. Automated translation tools translate forms or submissions to improve accessibility.

A health department trains an internal chatbot on policy manuals. Inspectors can ask, “What’s the protocol for foodborne illness response?” and receive immediate guidance.

After routine inspections, AI translates spoken inspection observations into rule citations, appropriate legal descriptions of the violation, and recommended corrective action for the inspector to validate and edit, thus reducing time on data entry and documentation, and increasing time for education. An AI tool scans daily complaints and flags an unexpected spike in mold reports from a specific apartment complex, prompting an early investigation. A department uses mosquito trap data and weather trends to predict areas of likely West Nile virus activity, guiding proactive larvicide deployment.

NLP analyzes a community heat survey and identifies patterns like “lack of shade,” “housing issues,” and “transportation barriers.”

AI scans drone footage of vacant lots and detects piles of tires or stagnant water, flagging potential mosquito breeding grounds and illegal dumping. A Spanish-speaking resident uses a complaint form in Survey123. It is auto translated into English for staff, ensuring timely response and follow-up.