  Due to escalating healthcare costs, accurately predicting which patients will
incur high costs is an important task for payers and providers of healthcare.
High-cost claimants (HiCCs) are patients who have annual costs above
$\$250,000$ and who represent just 0.16% of the insured population but
currently account for 9% of all healthcare costs. In this study, we aimed to
develop a high-performance algorithm to predict HiCCs to inform a novel care
management system. Using health insurance claims from 48 million people and
augmented with census data, we applied machine learning to train binary
classification models to calculate the personal risk of HiCC. To train the
models, we developed a platform starting with 6,006 variables across all
clinical and demographic dimensions and constructed over one hundred candidate
models. The best model achieved an area under the receiver operating
characteristic curve of 91.2%. The model exceeds the highest published
performance (84%) and remains high for patients with no prior history of
high-cost status (89%), who have less than a full year of enrollment (87%), or
lack pharmacy claims data (88%). It attains an area under the precision-recall
curve of 23.1%, and precision of 74% at a threshold of 0.99. A care management
program enrolling 500 people with the highest HiCC risk is expected to treat
199 true HiCCs and generate a net savings of $\$7.3$ million per year. Our
results demonstrate that high-performing predictive models can be constructed
using claims data and publicly available data alone, even for rare high-cost
claimants exceeding $\$250,000$. Our model demonstrates the transformational
power of machine learning and artificial intelligence in care management, which
would allow healthcare payers and providers to introduce the next generation of
care management programs.

Machine Learning;Machine Learning