Using Data Science Software to Address Health Disparities

Literature Review

Applications of data science are evident in numerous fields, ranging from research-based disciplines such as market, social, and census research to financial, technical, consulting, business, and media disciplines (Fayyad, 2012). The field of healthcare has begun to benefit from data science amid acquisition of new healthcare technologies. These new technologies also make available new opportunities for data science exploration, which can lead to intriguing discoveries from the data collected. For example, data science can be an important component of health informatics. Although viewed with some skepticism initially, health informatics has been embraced by the healthcare industry over time through vital investments in health information technology (HIT), increasing exploration of its utility (Detmer & Shortliffe, 2014). Data science may have similar adoption challenges, but as data begins to increase at a rapid rate, embracing data science as a discipline and new technology will soon begin to make sense. For example, data science software can be important to clinicians because it can reduce unnecessary expenses in patient care, improve care quality and patient safety, and streamline the patient care process. Additionally, data science can help to determine the level of care or the level of care transitions that must occur for the well-being of the patient. Such information can come from new insights surfaced in the application of data science to patients’ health improvement.