Document Type

Conference Proceeding

Language

eng

Format of Original

1360 p.; 27 cm

Publication Date

1998

Publisher

IOS Press

Source Publication

Medinfo '98 : proceedings of the Ninth World Conference on Medical Informatics : "Global health networking : a vision for the next millennium", Seoul, 1998

Source ISSN

9789051994070

Original Item ID

doi: 10.3233/978-1-60750-896-0-1318

Abstract

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value.

Comments

Published version. Published as part of the proceedings of the conference, Medinfo '98 : proceedings of the Ninth World Conference on Medical Informatics : "Global health networking : a vision for the next millennium", Seoul, 1998, 1998, 1318-1321. DOI. © IOS Press, 1998. Used with permission.

Dr. Adya was affiliated with University of Maryland at the time of publication.

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