Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 81, Issue -, Pages 93-101Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.03.015
Keywords
Insurance claims; Prediction; Inflammatory bowel disease; Hospitalization; Biologics; Topic modeling
Funding
- QCB Collaboratory Postdoctoral Fellowship
- UCLA-California Institute of Technology Medical Scientist Training Program [NIH T32 GM08042]
- William M. Keck Foundation
- AbbVie USA [H13 HumiraCD 05-SR21]
- Eisenhower Medical Center Department of Internal Medicine
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available