Text Mining Techniques for Healthcare Provider Quality Determination

Methods for Rank Comparisons
Erscheinungsjahr 2009
ISBN: 978-1-60566-752-2
Verlag: Medical Information Science Reference

Buch, Englisch, 399 Seiten, Hardback, Format (B × H): 221 mm x 286 mm, Gewicht: 1291 g

Reihe: Advances in Data Mining and Database Management

Useful to healthcare providers, severity indices conclude which patients are most at risk for infection as well as the intensity of illness while in the hospital. ""Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons"" discusses the general practice of defining a patient severity index for risk adjustments and comparison of patient outcomes to assess quality factors. This ""Premier Reference Source"" examines the consequences of patient severity models and investigates the general assumptions required to perform standard severity adjustment.

Autoren/Hrsg.

Cerrito, Patricia

Inhaltsverzeichnis

Central limit theorem Hospital reimbursements Kernel density estimation Introduction to ranking models Patient severity index Predictive modeling based on providers Predictive modeling in SAS enterprise miner Provider quality measures Risk adjustment based upon resource utilization Risk adjustment models for provider reimbursements Statistical examination of the Charlson Index Text mining to define patient severity

Über Autor(innen)

Patricia Cerrito, PhD, has made considerable strides in the development of data mining techniques to investigate large, complex medical data. In particular, she has developed a method to automate the reduction of the number of levels in a nominal data field to a manageable number that can then be used in other data mining techniques. Another innovation of the PI is to combine text analysis with association rules to examine nominal data. The PI has over 30 years of experience in working with SAS software, and over 10 years of experience in data mining healthcare databases. In just the last two years, she has supervised 7 PhD students who completed dissertation research in investigating health outcomes. Dr. Cerrito has a particular research interest in the use of a patient severity index to define provider quality rankings for reimbursements.