Cognitive Intelligence Platform for Real-Time Infectious Disease Risk Assessment
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Solution Overview
Problem
Infectious diseases are challenging to track and control within populations due to inadequate monitoring, proactive measures, and preventative actions, especially when new diseases emerge with limited initial knowledge about risk factors.
Innovation Solution
A cognitive intelligence platform that integrates data from various sources, uses a knowledge graph and logical structure to determine infectious disease risk for patients by matching medical information with authoritative risk factors, and performs preventative actions based on real-time risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for infectious diseases, then the system is simple to operate, but the tracking and monitoring effectiveness is insufficient
Solution Approach 1:
The system segments the population into risk groups based on clinical risk profiles, dividing the monitoring task into manageable segments. High-risk patients receive intensive monitoring while low-risk patients receive standard monitoring, improving tracking effectiveness without requiring complex resources for entire populations.
Solution Approach 2:
The system performs preliminary risk assessment by analyzing clinical data before disease outbreaks occur. By pre-identifying high-risk individuals and establishing monitoring protocols in advance, the system enables rapid response when diseases emerge without requiring complex real-time decision-making during crises.
2Measurement precision
If comprehensive patient data aggregation is implemented across multiple health IT resources, then the measurement precision of risk assessment is improved, but the device complexity increases
Solution Approach 1:
The system introduces a standardized data interface layer that acts as an intermediary between multiple health IT resources and the risk assessment engine. This intermediary layer translates diverse data formats into a unified structure, enabling precise risk assessment without requiring direct complex integration of all underlying systems.
Solution Approach 2:
The system develops a universal data aggregation framework that can interface with multiple types of health IT resources (electronic health records, laboratory systems, population health management tools) through standardized protocols. This multi-functional interface reduces complexity by handling diverse data sources through a single unified approach.
3Productivity
If real-time risk assessment and proactive measures are implemented, then the productivity of disease control is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary risk stratification by analyzing clinical data in advance and assigning risk levels to patients before outbreaks occur. This pre-processing enables real-time decision-making during disease events without requiring intensive data processing at critical moments, thus maintaining productivity while minimizing time loss.
Solution Approach 2:
The system implements continuous feedback loops where risk assessments are updated in real-time as new data becomes available. The system monitors disease trends, updates risk profiles dynamically, and triggers proactive measures automatically when thresholds are exceeded, enabling rapid disease control without manual intervention delays.
Data Source
AI summary
A method for determining a risk for an infectious disease for a patient using a cognitive intelligence platform is disclosed. The method includes receiving, from an authoritative source, factors indicative of a person being infected by the infectious disease, wherein the factors include medical information pertaining to the infectious disease. The method also includes determining whether any of the factors are present in a patient graph of the patient, wherein the patient graph includes medical information pertaining to the patient and the determining is performed by matching the medical information pertaining to the infectious disease to the medical information pertaining to the patient. The method also includes, responsive to determining the factors are present in the patient graph of the patient, determining the risk for the infectious disease for the patient, and performing a preventative action based on the risk for the infectious disease.