Cardiovascular Ischemic Event Decision Support Tool
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Solution Overview
Problem
Current medical care systems lack the ability to actively utilize accrued medical record information for proactive, predictive purposes, particularly in monitoring patients at risk for cardiovascular ischemic events. This leads to fragmented care and challenges in grasping trends in a patient's health status, resulting in potential adverse events and complications.
Innovation Solution
A computerized decision support tool that utilizes time series spectrum analysis to determine a statistical forecast or probability of future ischemic events by analyzing physiological parameters such as serum or blood uric acid and C-reactive protein levels. This tool generates a composite index representing an ischemic event risk forecast, which can trigger appropriate responses or interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If EHR systems are used as passive repositories for storing medical information, then information storage capacity is improved, but the ability to actively predict future patient status deteriorates
Solution Approach 1:
The patent replaces the passive mechanical storage system with an active computational system that uses machine learning models and time-series analysis to automatically predict future patient status. The system transforms stored EHR data into predictive insights through algorithmic processing, substituting the need for manual clinical assessment with automated predictive analytics.
Solution Approach 2:
The system performs preliminary analysis of medical record patterns to predict future patient conditions before adverse events occur. By continuously analyzing temporal patterns in physiological parameters and medical records, the system proactively identifies patients at risk and alerts caregivers in advance, enabling preventive intervention before deterioration occurs.
2Adaptability or versatility
If multiple providers monitor patient conditions over time, then comprehensive care coverage is improved, but the ability to quickly grasp health trends deteriorates
Solution Approach 1:
The patent merges data from multiple providers and sources into a unified analytical system. The EHR system consolidates information from various clinicians, departments, and monitoring devices into a single integrated database that applies consistent analytical algorithms, eliminating the need for individual providers to separately synthesize information from fragmented sources.
Solution Approach 2:
The system implements continuous feedback loops that automatically monitor patient parameters and provide real-time alerts when trends indicate potential adverse events. This automated feedback mechanism replaces manual review processes, immediately notifying relevant providers of concerning patterns without requiring them to actively search through accumulating data.
3Ease of operation
If conventional risk estimation methods are used, then simplicity of assessment is improved, but sensitivity and specificity for predicting ischemic events deteriorates
Solution Approach 1:
The patent creates a composite predictive model that combines multiple data sources including physiological parameters, medical record information, and temporal patterns. This composite approach integrates diverse information types into a unified risk assessment that maintains operational simplicity while achieving high predictive accuracy through the synergistic combination of multiple data elements.
Solution Approach 2:
The system adds the temporal dimension to conventional risk assessment by analyzing changes in physiological parameters over time. Instead of relying solely on static risk factor profiles, the system incorporates temporal dynamics of biomarkers such as uric acid and CRP, transforming the assessment from a snapshot evaluation to a dynamic trajectory analysis that captures evolving patient status.
Data Source
AI summary
Decision support technology is provided for use with patients who may be prone to a cardiovascular condition such as acute coronary syndromes. A mechanism is provided to determine a patient's risk for experiencing a cardiovascular ischemic event at a future time interval based on temporal patterns determined using physiological parameters of the patient such as serum or blood uric acid and/or C-reactive protein (CRP). A forecast or score may be determined indicating whether or not temporal patterns merit intervention to prevent occurrence or reoccurrence of ischemic events, or for determining adherence to or efficacy of treatment or preventive interventions. Based on the forecast or score, appropriate response action such as automatically issuing an alert or notification to a caregiver associated with the patient, may be determined, recommended, or implemented.