Describe data warehousing, data mining, and analytics and their role in improving healthcare quality and discuss significant differences between knowledge and information.

Data Warehousing is the collection and management of data from numerous sources such as financial data, departmental data, and administrative data for easier access and analysis (Khan & Hoque, 2015). Data mining refers to the discovery of patterns among large data sets using statistical models, database systems, and machine learning. On the other hand, data analytics refers to the analysis (qualitative and quantitative analysis) of raw data to conclude the data for human consumption (Khan & Hoque, 2015). The introduction and widespread adoption of EHRs and HIT across the healthcare industry have resulted in the collection of vast troves of information (data) that can be exploited to further our knowledge of the different functions of healthcare.

Qualitative and Quantitative Data Analysis

Through the use of data analytics, public health officials, and administrators of healthcare organizations can apply qualitative and quantitative data analysis to help augment their decisions in planning, management, learning, and measurements. An example of the benefits and applications of data analytics is seen through CMS (Center for Medicare and Medicaid Services) used robust data analytics to reduce hospital readmission rates and save more than a hundred million dollars (Islam et al., 2018).

Data mining can help in identifying common patterns in otherwise unrelated things and help in the development of better disease prediction, diagnosis, and treatments. This is estimated to assist in saving an estimated four hundred and fifty billion dollars annually (Islam et al., 2018). The patterns established and explored through data mining will help in the prediction of disease occurrence and aid in the development of more robust outreach programs.

Through data warehousing, all relevant data within the hospital is collected and classified to allow for easier access, exploitation, and various analytics. The insights received from the data spanning from issues of the best structure of patient care, marginalized areas that need assistance, and improving the overall health of the community (Suresh, 2016). The data collected will aid in the reduction of healthcare access costs and the improvement of the quality of care. An analysis of the occurrence of medical errors will help to determine the significant challenges encountered, the success rate of different devices, and a wide range of insights that will help to improve the overall quality of care within the hospitals while also augmenting the public health function.

Knowledge vs. Information

Knowledge refers to the comprehension of an idea or a subject based on the analysis, interpretation, and synthesis of the concept or subject. On the other hand, information refers to a refined form of data used to develop an understanding of the issues at hand. Another difference is that information is structured to improve the representation of data, whereas knowledge is structured to enhance an individual’s consciousness. Information can also be easily transferred from one point to another, whereas knowledge can only be transferred through learning. Information is generally insufficient for any meaningful prediction, as it is mainly concerned with the development of an understanding. On the other hand, knowledge enhances the prediction capabilities of an individual’s model (Ochola et al., 2015).

The simplistic nature of information means that it can be reproduced with a high degree of accuracy. In contrast, knowledge is dynamic and evolves with each encounter making it very difficult to reproduce accurately. These differences have illustrated the need for knowledge gathered through data mining techniques. The use of data analytics is indicative of gathering information, while data mining is indicative of learning or acquiring knowledge.

References

Islam, M. S., Hasan, M. M., Wang, X., & Germack, H. D. (2018, June). A systematic review on healthcare analytics: Application and theoretical perspective of data mining. In Healthcare (Vol. 6, No. 2, p. 54). Multidisciplinary Digital Publishing Institute.

Khan, S. I., & Hoque, A. S. M. L. (2015, May). Towards the development of health data warehouse: Bangladesh perspective. In 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-6). IEEE.

Ochola, J. E., Persson, D. M., Schumacher, L. A., & Lingo, M. D. (2015). Wikipedia: the difference between information acquisition and learning knowledge. First Monday, 20(12).