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Contextualizing Oncologic Imaging Utilization Through End-of-Life Spending Patterns.

Timothy P Copeland¹, John M Hillman², Benjamin L Franc¹

¹Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.

Journal of the American College of Radiology : JACR

|July 16, 2017

Summary

Spending patterns in a patient's final year of life significantly predict high-cost imaging use in their last three months. Trajectory modeling reveals specific spending patterns are stronger predictors than proximity to death.

Keywords:

Utilization management cancer imaging end-of-life care

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Area of Science:

Health Economics
Oncology
Radiology

Background:

Understanding factors influencing high-cost medical imaging utilization is crucial for healthcare cost management.
Patient spending patterns in the final year of life may offer insights into end-of-life care resource allocation.

Purpose of the Study:

To evaluate the association between spending patterns in the final year of life and the utilization of high-cost imaging in the final three months of life.

Main Methods:

A retrospective analysis of cancer patient data from an academic comprehensive cancer center.
Group-based trajectory modeling to identify distinct spending patterns in the final year of life.
Multivariate logistic regression to assess the impact of spending trajectories on high-cost imaging utilization (CT, MRI, PET) in the final three months of life.

Main Results:

Six distinct spending trajectories were identified.
Membership in the 'late rising' spending trajectory was the strongest predictor of high-cost imaging use (OR=11.61).
Diagnosis timing (12-6 months premortem) was also a significant predictor (OR=7.49), while spending trajectory improved predictive accuracy over temporal proximity to death.

Conclusions:

Patient spending trajectories and clinical severity measures are important considerations for hospital administrators estimating oncologic imaging utilization.
This analytical approach can inform evaluations for participation in advanced payment models and value-based care initiatives.