UCLA

Objective

Inflammatory Bowel Disease (IBD) is an inflammatory disorder of the gastrointestinal tract that can necessitate hospitalization and the use of expensive biologics. Models predicting these interventions may improve patient quality of life and reduce expenditures.

Materials and methods

We used insurance claims from 2011 to 2013 to predict IBD-related hospitalizations and the initiation of biologics. We derived and optimized our model from a 2011 training set of 7771 members, predicting their outcomes the following year. The best-performing model was then applied to a 2012 validation set of 7450 members to predict their outcomes in 2013.

Results

Our models predicted both IBD-related hospitalizations and the initiation of biologics, with average positive predictive values of 17% and 11%, respectively - each a 200% improvement over chance. Further, when we used topic modeling to identify four member subpopulations, the positive predictive value of predicting hospitalization increased to 20%.

Discussion

We show that our hospitalization model, in concert with a mildly-effective interventional treatment plan for members identified as high-risk, may both improve patient outcomes and reduce insurance expenditures.

Conclusion

The success of our approach provides a roadmap for how claims data can complement traditional medical decision making with personalized, data-driven predictive medicine.