AI Adoption for Healthcare and Public Health

​Artificial intelligence is advancing rapidly, but many healthcare and public health organizations still struggle to move from experimentation to meaningful and sustainable adoption.

In this presentation and interactive Q&A, attendees will explore the difference between simply using an AI tool and successfully integrating AI into real organizational workflows. The session will discuss common healthcare data challenges, including incomplete EHR data, inconsistent file formats, missing information, privacy requirements, and the difficulty of earning user trust.

Attendees will learn about practical AI applications such as data cleaning and standardization, automated reporting, prescribing outlier detection, dashboards, decision support, and natural-language analytics. The session will also examine important risks, including privacy, bias, hallucinations, incorrect outputs, explainability, and the need for human review.

A real-world case study from the Collaboration to Harmonize Antimicrobial Registry Measures, or CHARM, will demonstrate how secure data pipelines, analytics, small language models, machine learning, and natural-language reporting can support antimicrobial stewardship.

The session is designed for healthcare professionals, public health leaders, technology professionals, students, researchers, business leaders, and organizations exploring practical AI implementation. Attendees will leave with a realistic roadmap for starting small, validating results, measuring value, building internal support, and scaling AI safely.