IJBTT

Real World Data-Driven Transformation in Healthcare & Life Science: Evidence-Based Analytics, Machine Learning and AI Applications
	© 2023 by IJCTT Journal
	Volume-71 Issue-9
	Year of Publication : 2023
	Authors : Mayur Katariya, Snehal Tiwari
	DOI :  10.14445/22312803/IJCTT-V71I9P106

How to Cite?

Mayur Katariya, Snehal Tiwari, "Real World Data-Driven Transformation in Healthcare & Life Science: Evidence-Based Analytics, Machine Learning and AI Applications," International Journal of Computer Trends and Technology, vol. 71, no. 9, pp. 41-58, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I9P106

Abstract
High demand for real-time and effective patient outcome-centered healthcare systems is increasing globally. Therefore, there is a pressing need to counter inefficiencies and advance care delivery to promote positive health outcomes. Real-World Data (RWD) and evidence (RWE) have a tremendous opportunity and potential to improve patient outcomes. Understanding how patients use prescribed medication accurately guides stakeholders across the healthcare and life science system in making lifesaving, real-time choices regarding patients' health. RWD identifies inefficiencies across the healthcare environment and fills gaps in information silos among the stakeholders throughout the healthcare & life sciences ecosystem. Also, RWD is being used by pharmaceutical and life sciences firms at all phases of the drug development lifecycle, from initial discovery to post-market. RWE can bring crucial empirical data to clinical investigations that a standard study cannot.
This paper extensively researches the Real-World Synthea dataset, delves into the intricate process of generating patient data across various healthcare interaction points, offering a comprehensive view from the patient's perspective and showcases the different use cases that can be derived across the healthcare and life science systems such as patient demographics, treatment rates, medication adherence, comorbidity analysis, patient risk prediction, and disease progression tracking to improve patient outcomes. These use cases illustrate how RWD, when integrated with advanced analytics and artificial intelligence (AI), can drive informed decision-making, personalized patient care, and drug research and development advancements. It also incorporates actionable solutions using Medallion Architecture from Databricks Lakehouse. The integration of additional data sources, such as demographic data, genomics, claims data, and social determinants of health, is presented to enhance insights and improve patient outcomes. It concludes by emphasizing the profound impact of real-world data and the application of data analytics, machine learning, and artificial intelligence (AI) on reshaping healthcare systems, enhancing research endeavors, and ultimately paving the way for a future characterized by more efficient, patient-centric, and data-driven healthcare to improve patient outcomes and drive faster innovation across the drug lifecycle, leading to more promising prospects for patients and stakeholders alike.

Keywords
Artificial Intelligence, Data Analytics, Real World Evidence, Real World Data, Databricks Lakehouse, Healthcare, Synthea Data, Cloud.

Reference

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