Healthcare Delivery: Big Data Analysis

As healthcare continues to evolve, the emphasis of incorporating big data analysis in improving healthcare delivery has been woven into its very foundation. At its core, big data analytics encompasses a large volume of complex data and usually requires advanced software and IT to process, analyze and distribute it (Raghupathi & Raghupathi, 2014). With the emergence electronic healthcare records and the expected growth in data volume, utilization of big data has come at a practical time. But in it its glory, big data also presents a huge risk. The most pressing issue that arises from big data analytics use is understanding the legality and risk management behind handling large volumes of data. Corporate companies have experienced unsurmountable

Since the inception of Health Insurance Portability and Affordability Act (HIPAA) in 1996, the means of sharing healthcare data for private and commercial use has changed. Companies, both within and outside of the healthcare industry rely on processing power to mine data and filter through records. This process has been significantly simplified through the use of modern queries. One of the greatest factors in initiating a successful healthcare data analytics resource is by using big query platforms such as Hadoop or Google, both of which have shown that successful parameters of these systems rely heavily on its compatibility with HIPAA (Shafqat et. al, 2018). The reason behind this are the constraints when using commercially relevant data, which often has higher regulatory and legal concerns. This legal risk mainly stems from litigations that may arise from liability issues or a loss of intellectual property. One of the more recent legal concerns is the emergence of cloud storage. Using public cloud storage, although less costly than private cloud storage, brings about major security and patient privacy concerns (Wang et. al, 2018). This not only intensifies previously mentioned legal issues, but also opens doors for future concerns regarding new data analytics

Risk exposure not only includes legality issues, but also incorporates quality and safety risks, reputation risks, health risks, and mostly importantly, financial risks. The reality is that the above stated risks are interdependent and can have drastic effects on the administration of an organization. The reason risk management is such a daunting task stems from its ambiguous nature (Burke, 2013, p169). Risks are difficult to detect because of the interacting pieces that generate the likelihood of a risky result. Measuring risks also proves to be a seemly convoluted process due to the subjectivity behind the matter. Risks can only be measured as far as the human brain can process the complex parts and contingencies involved. Finally, risk mitigation also poses a problem as finding a solution to an unknown problem is just as difficult as identifying the issue at