Ensemble Diagnosis System Using Specialty Matching
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Solution Overview
Problem
Conventional disease diagnosis methods are inefficient and inaccurate due to the lack of consideration for diversity among medical specialties and the absence of cognitive intelligent agents, leading to prolonged and costly diagnostic processes.
Innovation Solution
A computer-implemented diagnosis method that processes user-input symptoms, assigns them to a team of physicians and cognitive agents based on specialty matching, and produces a consensus diagnosis through clustering techniques and feedback loops, utilizing machine learning classifiers and cloud computing for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-physician diagnosis methods are used, then the diagnostic process is simpler to manage, but the diagnosis accuracy and precision deteriorate
Solution Approach 1:
The patent segments the diagnosis task by assigning different physicians with specialized knowledge to specific disease categories. Each physician acts as an independent expert module that evaluates symptoms within their specialty domain, allowing the system to handle complex diagnostic tasks through divided expertise rather than requiring a single omniscient physician.
Solution Approach 2:
The patent merges the diagnostic capabilities of multiple physicians with different specialties into a unified diagnostic system. By combining the assessments of various specialists (e.g., cardiologists, neurologists, gastroenterologists) who each evaluate the same symptom set from their specialized perspective, the system achieves higher diagnostic accuracy than any single physician could provide alone.
2Measurement precision
If multiple specialists are involved in diagnosis, then the diagnosis accuracy improves, but the time required for diagnosis increases
Solution Approach 1:
The system performs preliminary actions by pre-organizing physicians into specialty-based teams and pre-defining which specialists should evaluate which symptom patterns. When a patient presents symptoms, the system immediately activates the appropriate pre-configured team, eliminating the time needed to search for and coordinate multiple specialists during the actual diagnostic process.
Solution Approach 2:
The patent implements feedback mechanisms where physicians provide diagnostic assessments that are aggregated and refined through iterative evaluation. The system collects feedback from each specialist, compares their assessments, and uses this feedback loop to converge on a consensus diagnosis more efficiently, reducing the overall time required compared to sequential consultations.
3Reliability
If diverse medical specialties are considered, then the reliability of diagnosis improves, but the complexity of selecting and matching physicians increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a smart matching layer between patient symptoms and physician specialties. This intermediary automatically analyzes the symptom profile, identifies relevant disease categories, and selects the appropriate specialists based on predefined expertise mappings, thereby managing the complexity of diverse specialty matching without requiring manual coordination.
Solution Approach 2:
The system manages physician selection complexity by changing the parameters of the matching process from manual, qualitative assessment to automated, quantitative algorithms. By transforming the matching criteria into measurable parameters (symptom patterns, disease categories, specialty tags), the system can efficiently evaluate and select diverse specialists based on objective data rather than subjective judgment.
4Productivity
If conventional communication methods between patient and physician are used, then the system is easier to operate, but the diagnostic efficiency and productivity deteriorate
Solution Approach 1:
The patent replaces the mechanical system of direct patient-physician communication with an automated digital platform that routes symptom data to appropriate specialists through computer algorithms. This substitution of automated information routing for manual communication processes dramatically increases diagnostic efficiency by instantly connecting patients with the right experts without requiring phone calls, referrals, or scheduling coordination.
Data Source
AI summary
A diagnosis method, system, and computer program product, include processing a user input comprising medical symptoms, assigning the user to a team of physicians and a cognitive agent based on the processed medical symptoms, each of the physicians having a different medical specialty corresponding to a disease associated with the medical symptoms, and producing a consensus diagnosis from the team of physicians and the cognitive agent.