Introduction
In healthcare industry the emerging technologies are not properly being grasped to increase the potential benefit. “Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations” authored by YichuanWang, LeeAnn Kung and Terry Anthony Byrd in an article published in 2015 that examined the historical development, architectural design and functionalities of the components in Big Data Analytics. The study also recommended five strategies for the healthcare industry to adopt Big Data Analytics to increase their capabilities in data-driven operations (Wang, Kung and Byrd 2015).
The focus of this Essay is to understand the dynamic potentialities of using Big Data Analytics in healthcare industry highlighted by the chosen literature. This essay will also present the implications highlighted by other researcher to critically explore the potential benefits of using Big Data in Healthcare service. However, as per the chosen article and other literatures Big Data Analytics can allow the healthcare service provides huge number of opportunities to take more data-driven and evidence based decision while executing a more controlled and efficient social service.
Big Data Analytics
The term Big Data refers to the huge volumes of data that cannot be stored, processed and fetch properly in conventional way of database application usage. In order to manage this huge amount of data a complex data analysis algorithm is used and this process is known as Big Data Analytics (Wang, Kung and Byrd 2015). The Big Data technology has passed its evolutionary stage between 2001 and 2008. Several other software application developments such as Extendable Markup Language (XML), Database Management System (DBMS), Web development ad Hadoop enhanced the complexities of the core big data analytics model with their diverse data set (Wang, Kung and Byrd 2015). The purpose of these advancements is to increase the opportunity of the end users to store, fetch and process an indescribably huge amount of data in a real-time platform. The process of Big Data Analytics is framed by the Big Data Architecture that holds five major layers of data handling namely data, data aggregation, analytics, information exploration, and data governance. As supported by Wang, Kung and Byrd (2015), the cloud based big data operation also enhanced the opportunity engage third party based distributive operational handling through cost effective storage system, which is known as “big data in the cloud”. In a nutshell the Big Data Analytics system is able to deliver the user a appropriate set of data through analysing a huge volume of different data types, that can enable the user to take more effective decision and perform more data-driven operations (Wang, Kung and Byrd 2015).
Utilization of Big Data in healthcare organisations
Healthcare industry needs tons of dataset comes from different data source such as patient details, medical resource details, treatment plans, human resource details, regulatory compliance and many others. According to Wang, Kung and Byrd (2015), in 2011 healthcare industry around the world stored healthcare data of 150exabytes that is 15×109GB, through electronic health record system. It clearly indicates that utilising this huge number of data for data driven statistical analysis to predict a specific outcome of treatment plan or health condition will be highly beneficial. As stated by Wang, Kung and Byrd (2015), the huge amount of organisational data stored from the healthcare industry worldwide is highly utilisable for economical analysis of healthcare interventions. Big Data Analytics can be also utilised as a major attribute of Evidence Based Practice in healthcare environment.
As defined by the chosen article, the Big Data architecture of a healthcare system should include four major components namely Data governance, Data management and Data lyfe-cycle and Data privacy-security (Wang, Kung and Byrd 2015). Data governance will be used to operating different data layers in healthcare environment that includes collecting data from healthcare service users, global network and organisation’s data storage. These data then will be stored in a systematic format that will be highly efficient for data fetching operation. After that the analysis has been done through AI aided data pattern findings and adoptive learning. The purpose of database management will be to increase data accuracy while reducing database anomaly and data-loss through rechecking the database from patent details, treatment plans and other resources (Wang, Kung and Byrd 2015). Data cycle management will control the data flow and the disposal of insignificant garbage data-set. Through Data security management the healthcare organisations can control the accessibility and can maintain the privacy of the data complying with the data privacy and protection act and health data accessibility related regulation (Wang, Kung and Byrd 2015).
Strategies to adapt Big Data Analytics
Therefore, from the chosen research article and other supportive literatures it can said that Big Data Analytics is one on that technological advancement from which the healthcare industry can improve their operations while enhancing their benefits. However, healthcare industry has to identify some strategic and business value of Big Data Analytics. In the chosen article the major strategies to adapt Big Data driven healthcare service are Implementing effective data governance to implement a enterprise wide error free data handling framework, Developing information sharing culture to ensure the accessibility to the dynamic data flow, Training key personnel to use this technology for enhancing the strength of human resource for advancement, incorporating cloud computing in organisation’s infrastructure to reduce the data handing burdens and developing business ideas from Big Data analysis to ensure continuous growth of service quality with profitability (Wang, Kung and Byrd 2015).
Conclusion
From the above discussion it can be clearly concluded that Big Data Analytics can improve the healthcare service operations with more accurate, dynamic and robust data driven approach while equally enhancing their benefits. Along with the chosen article there are several other articles in this topic that highlighted the same issues regarding the potential benefit of utilizing big data analytics in healthcare operations such as economic assessment, evidence based approach, treatment planning, intervention effectiveness analysis and many others. Moreover, from secure data governing to training the employees the healthcare organisations has also major roles to play for adapting this new technology in their infrastructure.
References:
Wang, Y., Kung, L. and Byrd, T.A., 2015. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3-13.
