Addressing Modifiable Healthcare Spending Through Dynamic Modeling Approaches
Julie Lauffenburger, Brigham & Women’s Hospital
Jan 01, 2018 - Mar 31, 2019
Summary of the Project:
This study seeks to improve the ability to use routinely-collected claims data to predict high spending and intervene to prevent it by (1) using group-based trajectory modeling to classify and predict dynamic health care spending patterns over multiple years, (2) isolating and examining patterns of modifiable and non-modifiable spending, and (3) pinpointing specific health care services, events and other potentially-modifiable factors that precede significant spending increases. The work is expected to produce new tools that improve cost predictions and cost-containment efforts, which could be helpful to policymakers and third-party payers.
Related Grantee Work
October 19, 2020
Use of Data-Driven Methods to Predict Long-term Patterns of Health Care Spending for Medicare PatientsLearn More
Author: Lauffenburger J, Mahesri M, Choudhry N,
August 17, 2020
"Not there yet: using data-driven methods to predict who becomes costly among low-cost patients with type 2 diabetes"
This article describes a data-driven approach to identify diabetes patients likely to experience rapidly increasing diabetes spending and highlights targets for early intervention that may prevent this progression.Learn More
Author: Lauffenburger J, Mahesri M, Choudhry N, BMC