Dr. Philip Smith provides a discussion that is devoted to the introduction to Clinical Business Intelligence (CBI). The discussion does not appear to clearly and explicitly define CBI. However, from the speech, it can be concluded that CBI involves all the processes that are employed by healthcare organizations to turn data into actionable wisdom, and this definition coincides with those that can be found in the articles by Brooks, El-Gayar, and Sarnikar (2015) and Foshay and Kuziemsky (2014). It can be pointed out that Brooks et al. (2015) discern between organizational, business, and technological process, but in any definition, it is clear that CBI is a complex phenomenon. As a result, Dr. Smith only tries to touch upon the most important components and features of CBI.
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The first CBI topic that Dr. Smith considers is Data Analytics (DA), and it is an important part of CBI, which used to be its primary and only element (Brooks et al., 2015). The ultimate aim of this activity is to make the health industry “smarter” (Burke, 2013, p. 2) through the employment of a variety of data. In this connection, Dr. Smith mentions the hierarchy of knowledge (DIKW), which starts with data and has wisdom on its top (Ronquillo, Currie, & Rodney, 2016). Dr. Smith explains that DA is used to upgrade data to the higher levels of noodle. In healthcare, the data in the bottom of the pyramid is called the clinical data, that embraces multiple elements, including operational, supply chain, medication, market share, and other areas. Apart from that, Dr. Smith mentions the “never events,” which indicate issues in an institution’s activities.
All the data gathered by a health institution is turned into a tool for improvement by CBI. Dr. Smith points out that the mentioned areas can be optimized by searching for the means to advance efficiency and safety, maximize profit, and minimize costs with the help of benchmarks and trends in the industry (Foshay & Kuziemsky, 2014). This information helps institutions to set goals for their performance and find the means of achieving them. Therefore, CBI makes data actionable. Wisdom is not a simple concept, but it can be defined as the knowledge that is applied to (clinical) cases appropriately to resolve issues or satisfy needs (Ronquillo et al., 2016). In other words, it is a practically applicable, actionable knowledge the application of which yields positive results, which implies that wisdom (the top of DIKW) is the ultimate goal of CBI.
Finally, it is noteworthy that CBI processes are continuous. Sr. Smith highlights the fact that the data needs to be always timely and, therefore, updated. Dr. Smith also points out the fact that the benchmarks are not rigid: they have a lower and an upper value, both which take into account natural variations in performance. Apart from that, DIKW is in the state of constant flux (Ronquillo et al., 2016), and the tools that are used by DA grow and improve over time (Burke, 2013).
The reason for the implementation of CBI is the constant pressure for improvement, which is experienced in the healthcare because of its growing costs and the need for patient safety and service quality (Foshay & Kuziemsky, 2014). Brooks et al. (2015) also mention that the modern trend for greater accumulation of data in the world and the implementation of electronic health records in healthcare can be regarded as stimuli for the development of CBI. Thus, CBI is a crucial activity for modern healthcare, which Dr. Smith demonstrates through multiple examples.
Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A framework for developing a domain specific business intelligence maturity model: Application to healthcare. International Journal of Information Management, 35(3), 337-345. Web.
Burke, J. (2013). Health analytics: Gaining the insights to transform health care. New York, NY: John Wiley & Sons.
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Foshay, N. & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20-27. Web.
Ronquillo, C., Currie, L., & Rodney, P. (2016). The evolution of data-information-knowledge-wisdom in nursing informatics. Advances in Nursing Science, 39(1), E1-E18. Web.