Context-Based Performance Benchmarking Engine
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Solution Overview
Problem
Current performance benchmarking approaches for sonographers, such as those performing echocardiograms, are biased due to factors outside a sonographer's control, like patient-specific clinical context and workflow, leading to inaccurate evaluations.
Innovation Solution
A system and method that utilize a performance benchmarking engine trained to learn factors impacting key performance indicators independently of individual performance, considering patient-specific clinical and workflow contexts to determine a more accurate and meaningful KPI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance benchmarking is used to evaluate sonographers, then performance evaluation can be performed, but the evaluation becomes biased due to factors outside sonographer control such as patient-specific clinical context and workflow context
Solution Approach 1:
The patent segments the performance evaluation by creating context-specific benchmark groups. Sonographers are evaluated separately within each clinical context (e.g., inpatient vs. outpatient) and workflow context (e.g., different equipment models), rather than using a single unified benchmark. This segmentation isolates the harmful contextual factors, allowing accurate evaluation of sonographer performance within each context while eliminating cross-context bias.
2Measurement precision
If context-based performance benchmarking is implemented, then accurate and unbiased KPI determination is achieved, but system complexity increases due to need to learn and account for multiple contextual factors
Solution Approach 1:
The performance benchmarking system automatically learns contextual factors and their impacts on KPIs from historical performance data without requiring manual configuration or expert intervention. The machine learning engine self-adjusts to identify patterns in how different clinical and workflow contexts affect performance metrics, then uses this learned knowledge to automatically create appropriate benchmark groups and determine context-adjusted KPIs, reducing the operational complexity despite the sophisticated analysis performed.
Data Source
AI summary
A system (102) includes a digital information repository (106) configured to store information about performances of individuals, including performances of an individual of interest. The system further includes a computing apparatus (103). The computing apparatus includes a memory (110) configured to store instructions for a performance benchmarking engine trained to learn factors of the performances that impact key performance indicators independent of the individuals’ performance. The computing apparatus further includes a processor (108) configured execute the stored instructions for the performance benchmarking engine to determine a key performance indicator of interest for the individual of interest based at least in part on the information in the digital information repository about the performances of the individual of interest and the learned factors that impact the key performance indicator of interest.