Provider Performance Index Using ML-Weighted Target Normalization

The patent introduces an index engine as an intermediary system that sits between raw provider data sources and performance assessments. This index engine automatically processes data through machine learning models, normalizes targets, adjusts for external factors, and generates provider performance indexes without requiring direct complex analysis by human operators. The intermediary system handles the computational complexity while providing simplified, accurate performance metrics.

The patent replaces manual human analysis of provider performance data with automated machine learning models. Instead of human operators subjectively evaluating provider metrics, the system uses trained ML algorithms to objectively process data, normalize targets, account for external factors, and generate performance assessments. This substitution of mechanical (human) analysis with automated computational systems improves precision while managing complexity through standardized algorithms.

The patent performs preliminary actions by pre-processing provider data in real-time as it is generated. The index engine continuously monitors and processes performance data, normalizing targets and adjusting for external factors before final assessments are needed. This preliminary processing enables rapid, real-time performance evaluation without requiring complex on-demand analysis, improving productivity through continuous rather than batch processing.

The system implements continuous real-time processing of provider performance data through the index engine. Rather than periodic batch analysis, the system maintains continuous monitoring and processing of data streams, enabling immediate performance assessment when needed. This continuous useful action improves productivity by eliminating delays between data generation and assessment while managing complexity through established continuous processing pipelines.

The patent segments the performance assessment into multiple independent targets (e.g., treatment effectiveness, patient satisfaction, cost efficiency). Each target can be processed separately through dedicated machine learning models, allowing complex multi-dimensional performance measurement to be broken down into manageable components. This segmentation improves measurement precision by addressing each metric individually while reducing overall processing complexity through modular architecture.

The system handles multiple targets and features by dynamically adjusting parameters such as weights and normalization factors. The index engine modifies target values based on external factors like provider characteristics, patient population demographics, and healthcare system context. These parameter changes enable precise performance assessment across diverse conditions without requiring complex processing for each scenario, as the same framework adapts parameters to accommodate variability.

The patent performs preliminary adjustments for external factors during the initial data processing stage. The index engine automatically normalizes targets and accounts for external variables like provider experience, patient demographics, and healthcare system characteristics before final performance indexes are generated. This preliminary action ensures fair comparisons are built into the assessment framework from the start, improving measurement precision without requiring time-consuming post-processing adjustments.

The system continuously processes and adjusts for external factors in real-time as provider data is generated. Rather than performing adjustments after assessment completion, the index engine continuously monitors and incorporates external variable data into performance calculations as they become available. This continuous adjustment maintains measurement precision while minimizing time loss by eliminating sequential processing steps and enabling parallel computation of target values and external factor corrections.