Research indicates that high utilizers of the health care system are more likely to have mental illness, to be from socially disadvantaged groups, and to have limited access to community-based services. In this retrospective study, three definitions of high utilization were examined: (1) across time: non-high utilization versus high-utilization, (2) single year versus multi-year, and (3) year-to-year. Univariate logistic regression models were fit to a set of 20 theory-selected predictors of high utilization. An optimal multiple predictor model was then derived via penalized multiple logistic regression (via elastic net, a machine learning algorithm). Three factors were identified in the optimized model as increasing the likelihood of high utilization: having a diagnosis of schizophrenia, having a co-occurring personality disorder diagnosis, and having less than a high school education. Given the complex needs of psychiatric high utilizers, innovative approaches should be considered to improve patient outcomes and reduce costly psychiatric hospitalizations.