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Topic Inspiration

The below links are a collection of health equity resources that will help you better understand the breadth and depth of health equity issues, and should provide ideas of potential topics.

The Department of Health and Human Services’ (HHS) Strategic Plan for FY22-26 includes Strategic Goal 1: Protect and Strengthen Equitable Access to High Quality and Affordable Healthcare. There are five objectives in this goal that outline areas to address health equity.

  • Strategic Objective 1.1: Increase choice, affordability, and enrollment in high-quality healthcare coverage
  • Strategic Objective 1.2: Reduce costs, improve quality of healthcare services, and ensure access to safe medical devices and drugs
  • Strategic Objective 1.3: Expand equitable access to comprehensive, community-based, innovative, and culturally-competent healthcare services while addressing social determinants of health
  • Strategic Objective 1.4: Drive the integration of behavioral health into the healthcare system to strengthen and expand access to mental health and substance use disorder treatment and recovery services for individuals and families
  • Strategic Objective 1.5: Bolster the health workforce to ensure delivery of quality services and care



Addressing Health Equity at the VA

Dr. Amanda Lienau, Director of Data and Analytics Innovation with the VA Office of Healthcare Innovation and Learning (OHIL) and Dr. Ernest Moy, MD, MPH, Executive Director of the Office of Health Equity, Veterans Health Affairs (VHA)

Watch this to learn more about:

  • The importance of understanding individual patient needs (physical and socioeconomic) to match them to both the right provider of healthcare and the right information for their situation and population group.
  • The difference between equality and equity and finding the “unknown unknowns.”
  • Using AI to reduce administrative processes, so that human interactions receive the most attention.
  • Examples of disparities of care including controlling diabetes and hypertension, cancer screening, cancer care, mental health services and social services.

Data for Health Equity, UVA Datapalooza 2022

Data for Health Equity, UVA Datapalooza 2022, with a special presentation by Dr. Rupa Valdez, Associate Professor of Public Health Sciences and Engineering Systems and Environment, UVA

Watch this to learn about:

  • Examining health disparities and vulnerable populations using public datasets of COVID cases.
  • [beginning at time 14:09] Dr. Valdez presents the disability community as a health disparity population and asks “how can we use Natural Language Processing to extract disability information from electronic health records (EHR)?”
  • [beginning at time 27:14] Dr. Paul Perrin, Professor of Data Science and Psychology, UVA, presents a study on the prevalence and structure of neurobehavioral symptoms in people with long COVID.

What Makes a Good Pitch?

Robert Liander, Chief Technology Officer, 22C Capital LLC


Watch this video to learn:

  • "Traditional" pitch advice and how yours can be better
  • Elements of a compelling story
  • The importance of having a demo, no matter what form!
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    Executive Summary Template

    To register for the challenge, you will need to complete a 1-2 page Executive Summary outlining the topic you chose, your proposed technical solution, and your plan for adoption.

Potential Solutions

There are many ways you can influence health equity using analytics and technology. Analytics can be applied: at the point of healthcare delivery, while training future medical professionals, in policy decisions, or during healthcare research and development.

You might: build training application for future doctors to better treat disadvantaged communities, develop an interactive map that displays the nearest healthcare provider to uninsured individuals, re-train the AI in a medical device, build a chatbot that answers questions about health insurance, make a mobile health screening app to serve people who don’t have access to transportation.

In any of these domains, you may consider several approaches to applying AI, such as:

  • Remove bias in an existing model
  • Build or use unbiased training set (e.g., with new data, employing new processing techniques)
  • Develop methodology for identifying bias in training datasets
  • Apply an explainable model to a problem that currently uses unexplainable AI
  • Build an explainable and interactive visual interface on top of model outputs
  • Address a new problem with an explainable model
  • Build a toolkit/common functions library with unbiased methods
  • Develop or apply a method to monitor and detect bias in existing models or assess how bias may change over time in deployment
  • Develop new model evaluation metrics for assessing fairness, model explainability, etc. 

This is not an exhaustive list, but should give you an idea of the breadth of solutions that are relevant to this challenge.


Additional Background Reading