Clinical Risk Stratification via Intermediary Statistical Models
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Solution Overview
Problem
Current prognostic models for medical therapies are complex and not practically integrated into clinical decision-making, lacking a mechanism for timely and effective application, and existing risk-stratification methods are invasive, costly, and require extensive processing time, failing to provide actionable outcomes for patient-specific treatment strategies.
Innovation Solution
A system and method for communicating via networks with research and intermediary agencies to supply statistical models that determine treatment outcomes and risks using patient-specific parameters, reducing data redundancy, and providing rapid dissemination of risk assessments to healthcare professionals and patients, facilitating informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex prognostic models are used to improve risk assessment accuracy, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent introduces a web-based intermediary system that bridges complex prognostic models and clinical practice. The system includes a database of patient data, a set of prognostic models, and a user interface that simplifies model application. This intermediary layer allows clinicians to access sophisticated risk assessment tools without needing to understand or manage the underlying model complexity directly.
Solution Approach 2:
The system enables self-service risk assessment by allowing clinicians to input patient data through a standardized interface and automatically receive risk predictions. The web-based platform automatically selects appropriate models, processes data, and presents results without requiring manual configuration or expert intervention in the modeling process.
2Measurement precision
If existing risk-stratification models are applied to improve treatment decision-making, then measurement precision is improved, but loss of time increases due to lack of practical mechanism for timely application
Solution Approach 1:
The patent pre-loads comprehensive patient data into a centralized database before clinical decisions are needed. This includes demographic information, medical history, laboratory results, and imaging data that are ready for immediate analysis. The system also pre-configures multiple prognostic models with different risk stratification criteria, enabling rapid selection and application without time-consuming data preparation during clinical encounters.
Solution Approach 2:
The system replaces manual, time-consuming risk assessment processes with automated computer-based evaluation. Instead of requiring clinicians to manually calculate risk scores or review complex guidelines, the system automatically processes patient data through programmed algorithms and returns risk stratification results instantly, substituting mechanical manual calculation with electronic computation.
3Measurement precision
If traditional risk assessment methods are used to identify patients needing intervention, then measurement precision is improved, but loss of time increases due to extensive processing time
Solution Approach 1:
The patent segments the risk assessment process into distinct modular components: data collection modules, model selection modules, calculation modules, and result interpretation modules. Each component can process specific data types independently and efficiently. The system also segments patient populations into different risk categories using multiple predefined models, allowing parallel processing and rapid identification of patients needing intervention without requiring sequential analysis of all possible risk factors.
Data Source
AI summary
A statistical processing system includes a server operably configured with program instructions implementing a plurality of statistical models to at least one of (a) predict a health outcome based on questionnaire responses, (b) assist a patient's choice of therapeutic modality based on questionnaire responses, and (c) assess a health risk or status based on questionnaire responses. Also provided is a research agency communicating with the server and contracted to provide the statistical models using a visual interface communicated by the server. The server is configured to analyze requests received from users relating to a plurality of said statistical models to reduce redundancy in requests for patient data.