Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases - Research report - key findings

Aug 3, 2026

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Inflammatory bowel disease is linked to higher health care utilization, creating pressure on ambulatory capacity and resources. Forecasting high-resource utilizers can support better allocation and proactive monitoring. This retrospective study develops machine learning models to cluster patients by clinical utilization patterns and to predict longitudinal utilization trajectories using baseline clinical characteristics. Adults with IBD from two academic centers (2015–2021) were analyzed with outcomes including encounters, steroid prescriptions, and biologic therapy initiation. The models separated low, medium, and high utilizers and achieved 81%–85% predictive accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Background

Methods

Data source and outcomes

Modeling approaches

Results

Conclusion

UC Irvine UC Irvine Previously Published Works Title Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases. Permalink [https://escholarship.org/uc/item/7mr936rv](https://escholarship.org/uc/item/7mr936rv) Journal Inflammatory Bowel Diseases, 30(5) Authors Limketkai, Berkeley Maas, Laura Krishna, Mahesh et al. Publication Date 2024-05-02 DOI 10.1093/ibd/izad127 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases Berkeley N. Limketkai, MD, PhD,*, Laura Maas, MD,† Mahesh Krishna, BA,† Anoushka Dua, MD,* Lauren DeDecker, MD,* Jenny S. Sauk, MD,* and Alyssa M. Parian, MD† From the * Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA †Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA Address correspondence to: Berkeley N. Limketkai, MD, PhD, 100 UCLA Medical Center Plaza, Suite 345, Los Angeles, California 90095, USA (berkeley. [limketkai@gmail.com](limketkai@gmail.com)) . Background: Inflammatory bowel disease (IBD) is associated with increased health care utilization. Forecasting of high resource utilizers could improve resource allocation. In this study, we aimed to develop machine learning models (1) to cluster patients according to clinical utilization patterns and (2) to predict longitudinal utilization patterns based on readily available baseline clinical characteristics. Methods: We conducted a retrospective study of adults with IBD at 2 academic centers between 2015 and 2021. Outcomes included different clinical encounters, new prescriptions of corticosteroids, and initiation of biologic therapy. Machine learning models were developed to characterize health care utilization. Poisson regression compared frequencies of clinical encounters. Results: A total of 1174 IBD patients were followed for more than 5673 12-month observational windows. The clustering method separated patients according to low, medium, and high resource utilizers. In Poisson regression models, compared with low resource utilizers, moderate and high resource utilizers had significantly higher rates of each encounter type. Comparing moderate and high resource utilizers, the latter had greater utilization of each encounter type, except for telephone encounters and biologic therapy initiation. Machine learning models predicted longitudinal health care utilization with 81% to 85% accuracy (area under the receiver operating characteristic curve 0.84-0.90); these were superior to ordinal regression and random choice methods. Conclusion: Machine learning models were able to cluster individuals according to relative health care resource utilization and to accurately predict longitudinal resource utilization using baseline clinical factors. Integration of such models into the electronic medical records could provide a powerful semiautomated tool to guide patient risk assessment, targeted care coordination, and more efficient resource allocation. Key Words: machine learning, health care utilization, quality improvement Introduction Inflammatory bowel disease (IBD) characteristically presents with a relapsing and remitting course, while often requiring the use of powerful immunosuppressants that do not necessarily provide complete or durable control of intestinal inflammation.1,2 Patients with IBD may subsequently experience disease progression and disease-related complications that pose significant morbidity and increased health care utilization.3–5 These patients generally experience increased need for communication with their gastroenterology providers, laboratory and other diagnostic testing,

FAQs

What is the goal of the study on inflammatory bowel disease patients?

To develop machine learning models that (1) cluster patients by clinical utilization patterns and (2) predict longitudinal utilization patterns from baseline characteristics.

How were patients and outcomes defined in the retrospective cohort?

Adults with inflammatory bowel disease were followed from two academic centers between 2015 and 2021. Outcomes included clinical encounters, new corticosteroid prescriptions, and initiation of biologic therapy.

What performance did the machine learning models achieve for predicting longitudinal utilization?

They predicted longitudinal health care utilization with 81% to 85% accuracy, with an area under the ROC curve of 0.84 to 0.90, outperforming ordinal regression and random choice methods.

Aug 3, 2026

Upload by Noah