Define the data strategy and drive performance accountability
The best proof here is not reporting volume. It is repeated use of objective evidence to improve targeting, adherence, intervention timing, and program design.
Corrected mis-targeted high-cost programs
Delivered evidence to the State of Texas showing that a high-cost case-management model was disproportionately serving relatively well-controlled adults with type 2 diabetes rather than the intended high-risk population.
- Clarified risk stratification using objective evidence
- Improved alignment between support intensity and need
- Shows discipline in resource allocation and patient qualification
Standards-of-care visibility
Designed a South Texas program to educate patients about provider HEDIS obligations after analysis showed only about 60% of patients were receiving standard of care.
- Turned performance gaps into operational action
- Improved transparency and accountability
- Useful analogy for DTP quality governance
Early reinforcement drives persistence
Observed that early behavioral biometric feedback was the hook that kept patients engaged. Most patients can sustain a short commitment on hope alone, but reinforcement becomes essential quickly.
- Tracked A1c, SMBG, weight, and lifestyle signals
- Built around practical reinforcement, not generic coaching
- Direct relevance to GLP-1 persistence and drop-off risk
Collaborative metabolic data
Rarify’s eDiary model allowed site dietitians and patients to work with the same metabolic-condition data in real time, with direct patient and site entry.
- Shared signal for action, not delayed review
- Highly applicable to obesity / GLP-1 support models
- Bridges patient behavior and governed execution