A Conference (Missingness in Action) to Address Missingness in Data and AI in Health Care: Qualitative Thematic Analysis.

Background: Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings.

Objective: This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings.

Leveraging technology in public-private partnerships: a model to address public health inequities.

Long-standing inequities in healthcare access and outcomes exist for underserved populations. Public-private partnerships (PPPs) are where the government and a private entity jointly invest in the provision of public services. Using examples from the Health Equity Consortium (HEC), we describe how technology was used to facilitate collaborations between public and private entities to address health misinformation, reduce vaccine hesitancy, and increase access to primary care services across various underserved communities during the COVID-19 pandemic.