Provide a description of big data, data mining, and data warehousing.
Provide an analysis of how data mining can be beneficial to a healthcare system.
Explain the purpose, characteristics, and components of a data warehouse.
Explain how the type of data warehousing used can impact the ability to mine data.
Describe examples of the successful use of guided data mining and automated data mining within healthcare.
Support your work with references from this week’s Learning Resources and the three articles you found in the Walden Library.
Provide references in APA style
Analysis Tools, Big Data, Data Mining, and Data Warehouses
Big data refers to large amounts of data with high variety that arrives in increasing volumes. In healthcare, the main sources of big data include patient medical records, hospital records, and results obtained from medical examinations (Cozzoli et al., 2022). Data mining refers to the process of analyzing big data to identify any trends and patterns that may be used to generate new information. Data mining can inform healthcare decision making by uncovering patterns in complex healthcare data. Insights obtained from data mining can also be used to improve operation efficiency, reduce healthcare costs, improve the accuracy of patient diagnosis, and promote effective healthcare planning (Kolling et al., 2021).
A data warehouse refers to a central database system where information is stored for analysis. The data warehousing process facilitates integration of information from many sources into one database (Liu & Wang, 2022). The type of data warehousing has a significant impact on the ability to engage in data mining based on the features provided. The enterprise data warehouse, for instance, provides users with the ability to classify data based on subjects and brings data from different areas of the organization together. This type of data warehouse facilitates easy extraction and transformation of information (Wu et al., 2021).
Automated data mining offers automation in the mining process through the use of machine learning techniques which capture and analyze actionable data. In healthcare, automated data mining can be used in electronic health records to rapidly determine which common exposures exist among patients who are part of a disease outbreak. An example of guided data mining is when healthcare providers compare disease causes, symptoms, and treatment to determine which treatment is most effective for patients (Sheidaei et al., 2022).
Cozzoli, N., Salvatore, F. P., Faccilongo, N., & Milone, M. (2022). How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08167-z
Liu, Y., & Wang, Z. (2022). An early warning risk and control model for manpower capital investment using data warehousing and Computational Intelligence. Computational Intelligence and Neuroscience, 2022, 1–9. https://doi.org/10.1155/2022/7624135
Kolling, M. L., Furstenau, L. B., Sott, M. K., Rabaioli, B., Ulmi, P. H., Bragazzi, N. L., & Tedesco, L. P. (2021). Data mining in Healthcare: Applying strategic intelligence techniques to depict 25 years of research development. International Journal of Environmental Research and Public Health, 18(6), 3099. https://doi.org/10.3390/ijerph18063099
Sheidaei, A., Foroushani, A. R., Gohari, K., & Zeraati, H. (2022). A novel dynamic bayesian network approach for Data Mining and Survival Data Analysis. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-02000-7
Wu, W.-T., Li, Y.-J., Feng, A.-Z., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021). Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Medical Research, 8(1). https://doi.org/10.1186/s40779-021-00338-z