BRAVO Diabetes Risk Engine for U.S. Population Prediction

The patent applies local quality by developing population-specific risk equations tailored to different demographic groups (European, African-American, Hispanic, Asian) rather than using a single universal model. Each population group receives customized risk predictions based on their specific baseline hazards and risk factor relationships, thereby improving prediction validity for each local population while maintaining the overall versatility of the system.

The patent implements parameter changes by updating the baseline hazards and risk factor coefficients to reflect current U.S. population data from the ACCORD trial, replacing the outdated UKPDS parameters. This allows the model to adapt to changing demographic compositions, treatment algorithms, and screening methods in the U.S. population, resolving the contradiction between maintaining a stable model structure and adapting to population differences.

The patent applies preliminary action by proactively updating the risk engine with current U.S. population data from the ACCORD trial before deployment, rather than relying on outdated UKPDS data. This preliminary data refresh ensures the model starts with accurate, contemporary information about baseline hazards, risk factor relationships, and population characteristics, thereby improving prediction accuracy while preventing information loss.

The patent implements feedback by validating the updated risk engine against independent U.S. population datasets and comparing predictions to observed outcomes. This feedback mechanism ensures the model maintains high accuracy by identifying and correcting any drift or biases that develop over time, addressing the issue of data currency through continuous verification.

The patent applies the intermediary principle by introducing a computational layer that handles the complex calculations and interactions between multiple risk factors and complications. This intermediary computing system processes the sophisticated risk equations and returns simplified risk scores and recommendations to clinicians, thereby maintaining ease of operation while preserving the ability to account for complex disease interactions.

The patent replaces manual mechanical computation with automated electronic calculation systems. The complex risk equations involving multiple interacting variables are computed automatically by software, eliminating the need for manual calculations while maintaining full model complexity. This substitution preserves clinical usability by providing instant results without requiring clinicians to understand or perform complex computations.