Using AI and Big Data in the HealthCare Sector to help build a Smarter and more Intelligent HealthCare System

The purpose of this paper is to demonstrate the use of AI and Big Data in HealthCare Sector to help build a smarter and more intelligent HealthCare System. Researchers in HealthCare Sectors are relying heavily on big data and compute power to build correlations by using statistical methods and artificial intelligence (AI) models. These models enable Healthcare Sector participants to manage HealthCare for a core set of the population. They also help providers to analyze the impact of decisions on their most vulnerable patients. There are many factors that are considered in performing big data analysis, some of them are: the patient’s medical history, genetic information, eating habits and fitness regimen. The data that is analyzed includes several key decision-making processes. Some of the challenges with the data used include data quality, data validation, data knowledge, domain expertise, and data integration challenges with various end points. While performing data analysis, the HealthCare Sectors must take security and data governance (HIPPA regulations etc.) into consideration. Big data analysis follows the (4P) approach [1], preference, prediction, personalization, and promotion. The question that arises most often is the type of data that is the most reliable for analysis in the HealthCare Sector. Most HealthCare organizations use demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, and patient insurance for their analysis. Since the data comes from multiple sources [2], there is a big challenge to perform data integration, extraction, and transformation as it consumes large amounts of resources and compute power, coupled with the additional challenges of data aggregation, data enrichment and format inconsistencies. To address this challenge and to analyze the process completely requires data scientists who have domain knowledge and expertise to extract, enrich and transform data. and group them into a meaningful format for proper data analysis and research. The HealthCare Sector faces this as a major challenge. Another key component in big data analysis is the lack of data visualization tools that can take structured and unstructured data and build customized dashboards for data correlation. There are also challenges with big data management in the HealthCare Sector. This data needs to be highly secured, with proper guardrails with tightened security measures and controls, and with proper data governance in place. There is also a need for a robust infrastructure that can handle large amounts of medical data that can allow researchers to analyze, build data correlation models and generate meaningful insights by using AI models through prompt engineering techniques [3] to build data correlation. In our paper, our focus is to understand, through several use cases the key challenges in gathering and grouping HealthCare data (both structured and unstructured data) from various sources. We also focus on understanding the impact of technical advancements in emerging AI technologies and how it plays a vital role in defining and deriving meaningful data insights for research and for learning HealthCare data patterns with a primary focus on data authenticity, ethics, privacy, governance, integrity, and security.