AI Research on Data Privacy in Healthcare for the UC San Diego Federal Government Relations Team

Summary

Role: Lead Researcher and Designer
Team: Faculty Advisors (2), Government Relations Team Leads (2), Researcher (Me)
Timeline: 4 weeks (2025)
Tools: Figma, Google Workspace
Audiences: UCDC Student Advocacy and Research Forum (Winter 2025), UC San Diego Mentored Undergraduate Research and Applied Learning Symposium (MURALS) (2025)

AI on the Prize was a four-week independent research and design project conducted through the University of California, Washington Program (UCDC). The project examined the intersection of emerging artificial intelligence systems with patient privacy, healthcare legislation, and ethical data governance in the United States.

As the project’s lead researcher and designer, I conducted a literature review, consolidated data ethics research, and translated findings into a policy-oriented informational poster for presentation at the UCDC Project Showcase. The objective was to communicate the growing implications of AI in healthcare data systems and advocate for stronger transparency and accountability measures in the governance of medical AI tools.

The Problem

Artificial intelligence has become increasingly integrated into healthcare, from diagnostic imaging to insurance claims processing, yet national regulatory frameworks surrounding patient data protection remain fragmented and outdated.

While the Health Insurance Portability and Accountability Act (HIPAA) provides a foundation for privacy protection, it does not adequately address AI-specific risks such as algorithmic bias, opaque model decision-making, and data repurposing without consent. This research addresses the following questions related to these issues: "What policies currently exist to safeguard medical data, especially as it relates to its use with AI tools?" and “How might we design systems and policies that ensure ethical, transparent, and equitable use of AI in healthcare?”

Challenges Identified:

• Lack of unified AI governance within healthcare legislation

• Limited patient understanding of how AI uses their data

• Gaps in existing privacy laws (HIPAA, CCPA, CPRA) regarding usage accountability

• Complexity of translating legal and technical concepts into accessible public language

This image depicts the output of NaviHealth's AI algorithm, employed by UnitedHealth Group, which was used to deny continued coverage for patient care.

Research and Methodology

To address these questions, I synthesized a wide range of interdisciplinary sources, including government white papers, academic journals on data ethics, and investigative reports from Reuters, STAT News, and The Markup on the misuse of AI in healthcare.

The research process included:

• Policy Review: Examined national, California-based, and emerging AI-focused legislation.

• Case Studies: Analyzed incidents where AI-driven claim denial systems (e.g., UnitedHealthcare) adversely affected patients.

• Content Analysis: Compared data privacy frameworks around the world to identify transferable practices.

• Qualitative Coding: Extracted and categorized key concerns such as bias, transparency, consent, and enforcement.

My findings revealed a pressing need for standardized, human-centered regulation and improved public communication regarding the impact of AI decisions on healthcare outcomes.

Design Approach

The design phase aimed to translate dense policy findings into an accessible visual artifact that could educate both policymakers and the public. Using Figma for layout design and Google Workspace for content synthesis, I designed a poster that guided viewers through four key sections:

1. The expanding role of AI in healthcare

2. Ethical and legal challenges

3. Current and proposed legislative efforts

4. Policy recommendations for ethical AI integration

Color-coding the categories of questions and answers improved visual navigation and comprehension. Typography and layout followed accessibility guidelines to ensure clarity across diverse audiences.

Findings

• U.S. data privacy laws inadequately address AI-specific healthcare risks.

• Patients and practitioners both desire more transparency in AI-driven decisions.

• Visual communication aids comprehension of complex policy topics.

• There is growing potential for design to bridge the gap between technical governance and public understanding.

The Final Design

The final poster, AI on the Prize: Protecting Medical Data in the Digital Age, presents a cohesive synthesis of legal research, ethical framing, and UX communication. It employs a structured hierarchy of text and visuals to demystify the interplay between AI innovation and patient privacy. Viewers at the UCDC Student Advocacy and Research Forum and UC San Diego Symposium, where I presented, could quickly trace how policy gaps translate into real-world risks and identify recommended reforms.

Impact and Reflection

Reflecting on this project, I learned how UX research can amplify policy communication and promote ethical design beyond digital products. By translating abstract regulations into understandable visual language, the project demonstrated how design can empower informed dialogue around emerging technologies.

Outcomes:

• Established a research-based communication framework for ethical AI in healthcare.

• Highlighted legislative gaps, supporting advocacy for standardized AI governance.

• Validated the role of visual design in policy education and interdisciplinary research.

Takeaways:

• Visual communication can make policy research accessible and actionable.

• Data ethics must be integrated into every stage of the design process.

• Collaboration between designers and policymakers enhances ethical innovation.

• Accessibility and clarity should anchor all information design decisions.

• Research-driven design can inspire meaningful dialogue on complex issues.

“Design can make an impact beyond aesthetics—it’s about making systems of power understandable.”