Clinical Benchmarking With Patient-Level Risk Impact Metrics
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Solution Overview
Problem
Existing healthcare delivery units face challenges in accurately predicting health outcomes due to varying availability and impact of contributing factors, and existing risk prediction methods fail to effectively mitigate these risks across different units and patients.
Innovation Solution
A system and method for analyzing data from multiple healthcare delivery units, using analytical models to compute expected health outcome frequencies, disentangling modifiable and unmodifiable risk factors, and generalizing these into individual-level impact metrics, enabling fair comparisons and prioritization of interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If risk prediction methods are applied across different healthcare delivery units, then health outcome prediction capability is improved, but reliability deteriorates due to varying availability and impact of contributing factors between units
Solution Approach 1:
The patent applies local quality by customizing the risk prediction model for each healthcare delivery unit based on their specific contributing factors. The system identifies and weights factors that are locally available and impactful at each unit, rather than applying a uniform model across all units. This allows each unit to have a tailored prediction approach that maximizes reliability given their specific data availability and patient population characteristics.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the contributing factors and their weights based on local conditions at each healthcare delivery unit. The system modifies the prediction model parameters to reflect the specific availability and impact of risk factors at each unit, enabling the model to adapt to varying data quality, completeness, and relevance across different locations and patient populations.
2Measurement precision
If comprehensive risk factor analysis is performed for all patients, then prediction accuracy is improved, but device complexity increases due to handling multiple data sources and factors
Solution Approach 1:
The patent extracts and focuses on the most relevant contributing factors for each specific healthcare delivery unit and patient population, rather than analyzing all possible risk factors universally. The system identifies and extracts the subset of factors that have the greatest impact locally, reducing the complexity of data collection and analysis while maintaining or improving prediction accuracy through targeted factor selection.
Solution Approach 2:
The patent segments the risk factor analysis by organizing contributing factors into distinct categories and analyzing them separately for each healthcare delivery unit. The system divides the comprehensive risk assessment into manageable segments that can be processed independently, reducing overall system complexity while maintaining comprehensive coverage of important risk factors through structured organization.
Data Source
AI summary
A computer-implemented method and system for analyzing data related to a plurality of healthcare delivery units includes: collecting data from different data sources related to the plurality of healthcare delivery units; computing, based on the collected data, for individual patients associated with any one of the plurality of healthcare delivery units, an expected frequency of at least one type of health outcome for each of the individual patients; and generalizing the computed expected frequencies of the at least one type of health outcome for the individual patients into one or more individual-level impact metrics associated with one or more risk factors for the at least one type of health outcome. The one or more risk factors include one or more modifiable risk factors and one or more un-modifiable risk factors.