Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka
Chapman and Hall/CRC
October 10, 2019 Forthcoming
Reference - 112 Pages
ISBN 9780367342906 - CAT# 320626
Series: Chapman & Hall/CRC Big Data Series
SAVE ~$15.99 on each
Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem.