مقاله انگلیسی رایگان در مورد تجزیه و تحلیل مه داده (الزویر)

Information technology (IT)-related challenges such as inadequate integration of healthcare systems and poor healthcare information management are seriously hampering efforts to transform IT value to business value in the U.S. healthcare sector (Bodenheimer, 2005; Grantmakers In Health, 2012; Herrick et al., 2010; The Kaiser Family Foundation, 2012). The high volume digital flood of information that is being generated at ever-higher velocities and varieties in healthcare adds complexity to the equation. The consequences are unnecessary increases in medical costs and time for both patients and healthcare service providers. Thus, healthcare organizations are seeking effective IT artifacts that will enable them to consolidate organizational resources to deliver a high quality patient experience, improve organizational performance, and maybe even create new, more effective data-driven business models (Agarwal et al., 2010; Goh et al., 2011; Ker et al., 2014). One promising breakthrough is the application of big data analytics. Big data analytics that is evolved from business intelligence and decision support systems enable healthcare organizations to analyze an immense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision making and action taking (Watson, 2014; Raghupathi and Raghupathi, 2014). Big data analytics encompasses the various analytical techniques such as descriptive analytics and mining/predictive analytics that are ideal for analyzing a large proportion of text-based health documents and other unstructured clinical data (e.g., physician’s written notes and prescriptions and medical imaging) (Groves et al., 2013). New database management systems such as MongoDB, MarkLogic and Apache Cassandra for data integration and retrieval, allow data being transferred between traditional and new operating systems. To store the huge volume and various formats of data, there are Apache HBase and NoSQL systems. These big data analytics tools with sophisticated functionalities facilitate clinical information integration and provide fresh business insights to help healthcare organizations meet patients’ needs and future market trends, and thus improve quality of care and fi- nancial performance (Jiang et al., 2014; Murdoch and Detsky, 2013; Wang et al., 2015). A technological understanding of big data analytics has been studied well by computer scientists (see a systemic review of big data research from Wamba et al., 2015). Yet, healthcare organizations continue to struggle to gain the benefits from their investments on big data analytics and some of them are skeptical about its power, although they invest in big data analytics in hope for healthcare transformation (Murdoch and Detsky, 2013; Shah and Pathak, 2014). Evidence shows that only 42% of healthcare organizations surveyed are adopting rigorous analytics approaches to support their decision-making process; only 16% of them have substantial experience using analytics across a broad range of functions (Cortada et al., 2012).