Sign Up Now
Get instant access to over 1 million+ study documents
OR
Already registered? click here to login
By creating your account, you agree to our terms of service, privacy policy and student honor code.
2001, p.160).
Results also indicated that patients between the ages of forty-six and sixty-four incurred significantly larger financial losses for the facility than the rest of the patients and Medicare and Medicaid nonexempt patients admitted via routine admission had the highest average loss for inpatient visits. Also, on Tuesdays, the average loss per patient was significantly greater for patients in the region. Only with data-mining could such an apparently insignificant finding as the predominance of Tuesday admittances come to light. The reason for this was not random: "Examination of Tuesday's admitting physicians" revealed that "several of the physicians were in the same medical specialty. This specialty cared for patients that typically required a high level of service intensity over a long period of time. The identification of a subset of patients with disproportionately high costs has prompted the institution to reevaluate its admission criteria to this unit" and also the diagnostic approach of the physicians in...
p. 160).
One potentially negative effect of this case study is that it merely targets which patients are most costly to the system, rather than seeks to provide solutions for dispensing effective care at a lower cost. While this case study did bring to light financially inefficient decision-making criteria on the part of a specific group of physicians, data mining does not necessarily produce superior care or offer a solution, other than to reduce the intake of these specific patients. This raises ethical questions about discrimination as a method of cost control and more analysis may be needed to ensure that reforms are undertaken in a compassionate and sensitive manner.
References
Silver, Michael. Taiki Sakata; Hua-Ching Su; Charles Herman; Steven B. Dolins; & Michael J. O'Shea. (2001, Summer). "Case study: How to apply data mining techniques in a healthcare data warehouse." Journal of Healthcare Information Management. 15(2). Retrieved June 5, 2010 at http://www.himss.org/content/files/jhim/15-2/him15208.pdf
Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive
The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data,
The ability to parse through the many records of transactions, customer contacts, and many other items stored electronically creates the foundation for data mining's definition. Data mining specifically is defined as the process of data selection, exploration and building models using vast data stores to uncover previously unknown patterns, insights, and observations that lead to strategies for effective differentiation and growth. Central to the development of data modeling is the creation of data and prediction models based
Data Mining The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this,
data mining? The foundational elements of data mining are multidisciplinary in nature, encompassing analytics, computer science, database systems integration and management, statistics and artificial intelligence. Often these technologies are used to create a single system of record used for analysis and advanced queries by the enterprises who build them. Data mining is often included in business intelligence (BI) suites and the analytics layer of an enterprise-wide computing system, as each
Data Mining in Health Care Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010). Benefits of using data mining in health care Care providers can make use of data analysis in