Healthcare administration and management are complicated because they involve multiple activities and responsibilities. The classification and clustering methods have been introduced to better carry, control, and maintain hospital activities. The US Food and Drug Administration is chosen to show how the methods are applied in healthcare delivery. The description of characteristics of patients, as well as overcoming challenges and issues in healthcare delivery, can be undertaken by classification and clustering application techniques.
Data mining is one of the techniques of classification and clustering that healthcare organizations use. This technique aims to draw information from healthcare data, which is essential for hospitals and healthcare delivery organizations to manage different resources of an organization (Duncan, 2021). An example is the application of this technique to the patient record process to predict the usage of the US Food and Drug Administration organization’s resources by patients or their timing of hospital stay (Duncan, 2021). The organization is mainly responsible for public health protection and medical products and food supplies safety assurance. This technique also helps classify disease clusters through statistical calculations and prognosis settings (Webster et al., 2021). The application of this technique is explained through the example of the US Food and Drug Administration (Dunskiy, 2021). The problem of new types of drugs developing appears when the chance of human errors increases due to the complicated process of chemical composition track (Dunskiy, 2021). Clustering and classification with their data mining technique help doctors and patients benefit from accurate data acquisition.
Negative impacts are also made on the health organization of the US Food and Drug Administration. The first destructive effect of this technique on the organization is represented by the difficulty of finding potential clusters, which slows specific processes (Loftus et al., 2022). The second issue involves clustering patients and diseases involving treatment response prediction and clinical enrollment. It is usually veiled due to specific errors, which negatively impact the organization’s functioning (Loftus et al., 2022). The optimal clustering is researched constantly to add value to clinical care.
Conclusively, the clustering and classification techniques are based on data mining and help healthcare organizations solve different problems connected with patients’ data management. Clustering and classification are applied in high-risk patient identification, hospital ranking, infection control, and disease identification. Healthcare administration requires constant data tracking to ensure correct and smooth treatment or organizations’ specific processes monitoring. Classification and clustering ensure fast overcoming of challenges and solutions to problems in healthcare delivery organizations.
References
Duncan, W. (2021). Understanding patient hospital stays: A classification and clustering analysis in R. Towards Data Sciences. Web.
Dunskiy, I. (2021). Data mining in the healthcare industry: Key benefits & examples. Demigos. Web.
Loftus, J.T., Shickel, B., Balch, J.A., Tighe, P.J., Abbott, K.L., Fazzone, B., Anderson, E.M., Rozowsky, J., Ozrazgat-Baslanti, T., Ren, Y., Berceli, S.A., Hogan, W.R., Efron, P.A., Moorman, J.R., Rashidi, P., Upchurch, G.R. Jr., & Bihorac, A. (2022). Phenotype clustering in health care: A narrative review for clinicians. Frontiers, 5. Web.
Webster, A. J., Gaitskell, K., Turnbull, I., Cairns, B. J., & Clarke, R. (2021). Characterisation, identification, clustering, and classification of disease. Scientific reports, 11(1). Web.