Healthcare ROI Statistical Model for Population Savings
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Solution Overview
Problem
Existing methods for determining the Return on Investment (ROI) for healthcare management programs are inaccurate and inefficient, often overstating or understating outcomes, and involve cumbersome processes.
Innovation Solution
A statistical model-driven system that uses robust demographic, geographic, health status, and illness burden adjustment methodologies to simulate ROI for custom populations, providing a graphical user interface for input and validation, and calculating savings with confidence levels, reducing bias and regression analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art approaches are used to determine ROI for healthcare management programs, then the calculation process can be completed, but the results are inaccurate and often overstate or understate actual outcomes
Solution Approach 1:
The patent transforms the ROI calculation from using simple aggregate metrics to using multiple adjusted parameters including demographic characteristics, geographic factors, health status indicators, and illness burden measures. This parameter transformation enables more accurate and reliable outcome predictions by accounting for the complexity of population health variations.
Solution Approach 2:
The patent introduces a statistical model as an intermediary between raw healthcare data and ROI calculations. This model acts as a mediator that processes complex population characteristics and translates them into accurate savings forecasts, eliminating the need for direct but inaccurate prior art calculation methods.
2Productivity
If prior art methods are used to calculate ROI, then the process can be completed, but it involves inefficient, cumbersome and time consuming processes
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing population characteristics, demographic factors, and health metrics in structured formats before ROI calculations are needed. This preprocessing enables rapid retrieval and computation during actual ROI analysis, dramatically reducing calculation time while maintaining accuracy.
Solution Approach 2:
The patent replaces manual, mechanical calculation processes with automated statistical modeling and computer-based analysis. This substitution eliminates cumbersome manual procedures and enables efficient processing of complex demographic and health data to produce accurate ROI forecasts.
3Ease of operation
If a simple and easy-to-use graphical user interface is provided, then user accessibility is improved, but the complexity of robust statistical modeling must be managed
Solution Approach 1:
The graphical user interface acts as an intermediary layer between the user and the complex statistical modeling system. It provides simple, intuitive controls for inputting population characteristics and retrieving ROI forecasts, while the complex statistical processing occurs automatically in the background without requiring user expertise in statistical methods.
Solution Approach 2:
The system performs self-service by automatically handling the complex statistical modeling, data processing, and calculation operations without requiring user intervention or expertise. Users simply input basic parameters through the simple interface and receive processed results, while the system independently manages all complex analytical operations.
Data Source
AI summary
A method and system for determining custom population Return on Investment (ROI) forecasted savings estimates for use in evaluating the desirability of active health care management programs and the depth of penetration of such programs. The method and system further include a graphical user interface and returns a statistical confidence of the predicted savings or loss.