Can the US minimum data set be used for predicting admissions to acute care facilities?

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.