دانلود رایگان مقاله تحلیل کلان داده: درک قابلیت ها و مزیت های آن برای سازمان های سلامت

abstract

To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.

6. Limitation, future research and conclusion

Through analyzing big data cases, our research has provided a better understanding how healthcare organizations can leverage big data analytics as a means of transforming IT to gain business value. However, like any other study, ours has limitations. The primary limitation of this study is the data source. One challenge in the health care industry is that its IT adoption usually lags behind other industries, which is one of the main reasons that cases are hard to find. Although efforts were made to find cases from different sources, the majority of the cases identified for this study came from vendors. There is therefore a potential bias, as vendors usually only publicize their “success” stories. Further and better discovery could be done through collecting and analyzing primary data. Given the growing number of healthcare organizations adopting big data analytics, the sample frame for collecting primary data becomes larger. Examining the impact of big data analytics capabilities on healthcare organization performance with quantitative analysis method based on primary data could shed different lights. In addition to requiring empirical analysis of big data analytics enabled transformation, our study also expose the needs for more scientific and quantitative studies, focusing on some of the business analytics capability elements we identified. This especially applies to analytical and decision support capabilities, which are cited frequently in the big data cases. With a growing amount of diverse and unstructured data, there is an urgent need for advanced analytic techniques, such as deep machine learning algorithm that allows computers to detect items of interest in large quantities of unstructured data, and to deduce relationships without needing specific models or programming instructions. We thus expect future scientific studies to take developing efficient unstructured data analytical algorithms and applications as primary technological developments.