St. James Ballroom
AI-Enhanced Innovations in Large National Health Care Survey Data Analytics (303900)
*Steven B. Cohen, RTI International
Keywords: Artificial intelligence, imputation, modeling
A high degree of rigor is essential in the statistical integrity of “end-product” analytic resources that are used to inform health and healthcare policy and action. In this vein, statistical and analytic staff devote substantial time and effort to implement estimation, imputation and analytic tasks, which are essential components of the “end-product” analytic databases derived from national or sub-national health care surveys and related data collections. This presentation focuses on the development and implementation of AI and machine learning enhanced applications to imputation for specific national health and health care survey efforts that achieve efficiencies in terms of cost and time while satisfying well defined levels of accuracy that ensure data integrity. Attention is given to enhanced processes that serve as an alternative solution to manual, repetitive or time-intensive tasks; operationalize decisions based upon predefined outcome preferences and upon access to input data that sufficiently informs the decisions, and permit real-time interpretation for accessing and acting upon the AI-derived decisions to permit the user to focus energy on higher-order problem solutions