The Potential For Data Science Analytics To Remediate Existing Health Disparities Through Improved Clinical And Medical Research Insights

Data science analytics can respond definitively to health disparities through an investment in the creation of databases that emphasize the unstructured, qualitative nature of the most relevant information needed to alleviate health disparities.This paper discusses the potentials for unstructured machine learning to harness qualitative, ethnographic information of relevance in reconstructing the origins and maintenance of current inequities in diverse populations, medical settings, and health conditions.We also identify the current databases that attempt to address health disparities, and we suggest explicit strategies that must be put in place to use data science analytics to identify the nuanced details of health inequities.The application of ethnographic-rich databases to clinical and medical research can increase their power and better identify the proximate and ultimate causes in understudied health disparities.