Abstract
The article assesses data science software to evaluate the usefulness of data science technology in addressing concerns such as health disparities. Data science software was analyzed using KDnuggets data related to analytics, data science, and machine learning software. Data science functionalities include computational processes and frameworks that are relevant for healthcare. This study demonstrates the importance of leading applications for conducting data science operations that can improve care in healthcare networks by addressing such factors as health disparities.
Highlights
The application of data science in health care was studied by many professionals in the health care space to forecast its value and particular uses
Health care organizations can benefit through the impact data science software can have on their organizations and the multiple ways data science can lead to important findings in health care
The healthcare sector has not seen the full benefits of data science
Summary
The application of data science in health care was studied by many professionals in the health care space to forecast its value and particular uses. These challenges include data accuracy, missing data, and standardizing of data (Delaney & Westra, 2016) These are very important challenges to address, an important axiom to keep in mind is that the underlying information complexity to be achieved would have a major effect on the information system structure most appropriate for achieving the desired information outcome (Murphy, Murphy, Buettner, & Gill, 2015). Data science in healthcare may in some ways be limited, but it is useful to help solve significant and common healthcare problems One such problem is that of health disparities found across health care organizations. The Covid-19 pandemic media coverage has reported mortalities among blacks in the United States at a higher rate compared to Caucasians (Shelby Lin Erdman, 2020) Is this due to disparities in socioeconomic issues and healthcare access that
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