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Ramanakar Reddy Danda

Abstract

Rising healthcare needs, burgeoning medical costs, and several other interconnected elements of healthcare contribute to the difficulty of having drug plans that are tailored to one's unique healthcare needs. Digitization of healthcare data has led to our ability to process and understand patterns within healthcare and pharmacy data. Furthermore, due to advancements in data analytics arising from IT, it is much more feasible to identify the unique needs of seniors accurately and efficiently. An aging population needs more comprehensive care and access to lower-cost drugs; yet, targeting those beneficiaries for reductions in drug plan costs with established drug plans has led to further health disparities in Medicare's health program for seniors. In this paper, we build a case for using AI-powered methods for analyzing the unstructured qualitative interviews with senior beneficiaries and pharmacy staff to optimize prescription drug plans during their initial year with the program.
The development of AI methods can be used to find repetitive text-based patterns in senior prescription plan use and guidelines for pharmacy staff in-person interviews to help ensure carriers of drug plans meet the unique needs of the rapidly growing senior population. Unstructured qualitative data has a wealth of patterns that could be mined for insight into seniors' needs despite the challenges of textual data analysis. It is hypothesized that AI-powered methodologies provide a much more cost-effective analysis of the operational data relating to the way in which seniors leverage their prescription drug benefits in a dynamic setting. Likewise, AI methodologies can also help build a more comprehensive account of typical senior user behavior, which would require a more quantitative-based analysis. Such patterns can be extracted using AI data mining techniques, in turn informing personalized coaching instructors about how to address the needs of seniors who do have the authority to make drug choice preferences, as well as pharmacists and providers.

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