Social Responsibilities Round Table Talk
Talk/Presentation · April 2026
From Data to Action: How Open-Source Health Data Platforms Democratize Research and Empower Community Advocacy
Presentation Overview
The Health Equity Tracker (healthequitytracker.org) demonstrates how publicly accessible, disaggregated health data can bridge the gap between academic research and community action. This presentation explores how we've transformed complex epidemiological data—including 25 years of maternal mortality statistics, COVID-19 disparities, and chronic disease outcomes—into accessible visualizations that community organizers, journalists, educators, and advocates use to demand policy change. We'll share how our open-source approach and SEED (Software Engineering and Education Development) program simultaneously addresses data justice and technology workforce barriers, creating a replicable model for libraries and community organizations seeking to leverage health data for equity advocacy.
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Recording
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Where to start
Presentation Slides
Screen Reader Slides
Outcomes
- By the end of this presentation, attendees will be able to:
- Understand the landscape of health data accessibility barriers and how they perpetuate inequities by keeping evidence out of community hands
- Learn practical strategies for using existing open-source health data platforms (like HET) in library programming, community workshops, and patron education
- Gain concrete examples of data-driven advocacy in action—how community organizations have used disaggregated health data to influence policy, secure funding, and shift public discourse
- Explore partnership opportunities between libraries, public health institutions, and technology teams to expand health data literacy in their communities
- Discover replicable frameworks for building capacity in underrepresented communities—both in terms of health literacy and technology workforce development
- Leave with actionable ideas for integrating health equity data tools into existing digital literacy programs
- Recognize the interconnection between data justice, workforce equity, and health equity—understanding that sustainable solutions must address multiple dimensions of systemic inequity simultaneously
Related work
Building a Zero-Cost Grant Monitoring Microservice
With our three team roles confirmed funded through FY27, I began transitioning into a grants-facing capacity alongside my UX engineering responsibilities, taking on the early infrastructure work that a dedicated grants professional would otherwise own.
Rather than monitoring dozens of foundation websites manually or investing in a grant database subscription that tracked historical awards rather than live open calls, I scoped and built Grant Watchdog: a lightweight automation tool that watches the live web pages of curated funder targets for changes, flags potential new opportunities, and generates a plain-language report the whole team can read.
Claim What Is Yours: A Trauma-Informed Content System for a High-Stakes Writing Task
Claim What Is Yours was a personal initiative I built to address a gap I'd lived through directly: veterans navigating VA disability claims — particularly for conditions tied to military sexual trauma — face a writing task that is procedurally demanding and, for many, close to impossible to approach directly. I designed a four-part content system (a free introductory guide plus a core guide with two companion modules) to walk survivors from "I don't know where to start" to a completed claim package, using AI tools as a structured writing partner along the way.
Description The core design problem wasn't information delivery — the VA's process is documented; it's the access gap between knowing the steps and being able to sit down and do them. I addressed that in a few specific ways: Staged disclosure architecture. Rather than asking a reader to write their most difficult account first, I sequenced the material so the hardest writing task (the formal MST stressor statement) comes after three lower-stakes "shadow work" exercises — third-person narration, a before/after contrast, and a behavioral-markers inventory — each one building material the reader can carry forward into the harder document, so no one starts at the hardest point. Structured AI-prompting as a writing scaffold. I wrote reusable, fill-in-the-blank prompt templates for every major document in the process (personal statements, nexus letter requests, buddy letter asks), each engineered to produce output in the reader's own voice rather than a generic or clinical one, and each paired with explicit guidance on what to check in the output before using it. Explicit safety and data-literacy guidance. Because the audience would be using AI tools for the first time, often around highly sensitive material, I included direct instruction on what not to disclose to an AI platform, how to evaluate a tool's data practices, and clear crisis-line signposting throughout — treating tool literacy as part of the content's job, not an afterthought. A tiered information structure. Free → core guide → bonus modules, so a reader in crisis could access something immediately useful at zero cost, with a deeper system available for those ready to go further.
Designing CHARLIE — Translating Federal Health Data into Community-Ready Insight
CHARLIE (Community Health Analytics & Responsive Learning Intelligence Engine) is a mobile-first, AI-powered platform I independently designed and built in 2025, over a fixed pre-accelerator program period, to translate federal public health data into plain-language insight for non-technical users — building on a concept a colleague originally proposed in a different technical form.
I took an idea a colleague originally developed — proposed to an outside AI grant program in a resource-intensive, fine-tuned-model form that ultimately wasn't funded — and independently designed and built an entirely different implementation using Bubble's no-code platform and Anthropic's Claude API directly. Completing Bubble's Immerse pre-accelerator program gave the project structured mentorship as I refined it; once the program period ended, the ongoing cost of running an unfunded, solo-maintained no-code app outpaced what made sense for me to sustain, and the engineers who maintain HET itself have since carried the API-integration approach forward into HET's own infrastructure. I chose Anthropic's Claude API specifically because, in a health equity context, algorithmic bias can determine who gets diagnosed, treated, or overlooked — making AI-provider selection itself a trust and equity decision for the users I designed for.
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