An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
This is an incremental overview for healthcare researchers and practitioners, focusing on applying existing methods to pandemic-related data.
The paper addresses the challenges of analyzing big data in healthcare, particularly during the COVID-19 pandemic, by advocating for innovative analytical methods and multi-disciplinary approaches to improve diagnosis, treatment, and epidemiological tools.
In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.