School of Nursing
For most medical problems, clinical (patient) heterogeneity influences treatment efficacy and results in variations in outcome in one-treatment-fits-all settings. However, an opportunity exists to improve outcomes while reducing costs using currently existing treatments when we understand how clinical heterogeneity influences treatment efficacy and how much of a difference exists among treatment options. Knowledge of how to personalize treatment to account for this clinical heterogeneity is the key to optimizing outcome and improving treatment efficiency.
Projects by this research group use this opportunity to improve healthcare outcomes, whose evidence is extracted from electronic health records. These projects are:
- Mining personalized Alzheimer's Disease treatment from data
- Predicting a cognitive decline curve for Alzheimer disease
- Using a data-mining approach to facilitate efficient use of nursing resources
In general, each project derives evidence of improved-outcome evidence associated with treatment options, patient characteristics, and interactions. They benefit largely from MSI computing resources.