With the significant information technology advancement, the capacity of generating and storing big data has grown exponentially. This rapid growth of data availability is coined with a simultaneous growth in the analytical skills that enable researchers analyse large scale data i.e. big data analytics and data mining techniques. In healthcare, the growing adoption rate of the electronic health records (HER) contributed to the availability of high volume, high velocity and high variety health data. Scholars argued that the utilization of big data analytics i.e. data mining helps health practitioners improve care quality, optimize outcomes, and reduce the cost of healthcare. Nonetheless, unlike other industries, the adoption of big data analytics and data mining techniques is still in its infancy and our conventional analytical capacity has diminished and become incapable of handling the data massiveness. The limited use of big data mining approaches is multifactorial. Majority of research about the use of big data in healthcare is theoretical and presents the authors opinion not necessarily reflective of an institutional position. Moreover, the decision to invest in artificial intelligence solutions is strategic and requires deep understanding of the full potentials of big data and the data mining techniques. But more importantly there is no or a very little research about how big data mining techniques can help provide clinical evidence and inform the clinical decision making in the era of evidence based practice. The positivistic evidence based school of thought and its hierarchy of evidence are exclusive and consider any methodology outside the hierarchy incapable of producing scientific knowledge that can shape reliable evidence which can guide the clinical practice. Therefore, in order to reap the full benefits of the big data and big data mining approaches, health policy makers, scholars and practitioners have to realize what big data mining technology can do economically and clinically and how it can help clinicians make decisions that improve the treatment outcomes and help prevent and cure diseases. This conceptual study provides a framework that tells how big data mining techniques can augment the evidence based practice and how big data mining can become recognized scientific knowledge producing methods that contribute to the generation of clinical evidence.
Audience Take Away:
- It will draw the attention of the audience who come from all over the world to one of the latest information technological advancement that has great potentials to reshape the way we administer the healthcare. There is a great opportunity that among the audience there will be nurses in senior administrative positions in their organizations. Those who can take the lead in influencing the decision in investing in big data analytics and artificial intelligence.
- This presentation highlights the great potentials of big data and data mining in improving clinical decision making, enhancing disease prevention, improving quality of care, improving treatment outcomes, reducing the cost of healthcare, improving operational efficiency, etc.
- This presentation about big data may shed the light on unprecedented research opportunities that can take the evidence based practice to a new level where making evidence available becomes innovatively efficient.