Computational Interventions to Improve Access to Opportunity
Access to healthcare and health information is of major global concern. The stark inequality in the availability of health data by country, demographic groups, and socioeconomic status impedes the identification of major public health concerns and implementation of effective interventions. This data gap ranges from basic disease statistics, such as disease prevalence rates, to more nuanced information, such as public attitudes. A key challenge is understanding health information needs of under-served and marginalized communities. Without understanding people's everyday needs, concerns, and misconceptions, health organizations lack the ability to effectively target education and programming efforts. In this presentation, we focus on the lack of comprehensive, high-quality data about information needs of individuals in developing nations. We propose an approach that uses search data to uncover health information needs of individuals in all 54 nations in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis; these searches reveal diverse health information needs that vary by demographic groups and geographic regions. We also shed light on discrepancies in the quality of content returned by search engines. We conclude with a discussion of computationally-informed interventions both on- and off-line in health and related domains and the Mechanism Design for Social Good research initiative.