- Smaller demographic groups lacked available historical data to train prediction models.
- Individual medical history data was dispersed across hundreds of hospitals and, therefore, inaccessible for analytics.
- Centralizing the disbursed medical data would expose the organization to HIPPA and CCPA/CPRA scrutiny and come with long delays and high costs
- Devron collaborated with the association’s analytics team to deploy on historical data wherever it resided.
- Devron sped up model training and increased accuracy with medical claims data from sources like internal, clearinghouse, public, and health data companies.
- Increased data access meant faster model training and rapid predictions of likely pre-diabetics, which helped address causes earlier and ultimately save lives.
Improve Patient Outcomes
More Data for Analysis
Reduce Speed of Diagnosis
Reduce Regulatory Risk