AI Diagnostic Module Database for Patient Diagnosis Prediction
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Solution Overview
Problem
Current healthcare systems lack efficient methods for predicting patient diagnoses and integrating patient data with diagnostic criteria, leading to suboptimal diagnostic processes and inefficiencies in healthcare delivery.
Innovation Solution
A system and method for generating and implementing a diagnostic module database that initializes diagnosis modules from medical resources, extracts language signals, transforms them into diagnosis indicators, and stores them in a database for accessing and predicting diagnoses during patient encounters, using provider portals to facilitate accurate and efficient diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual diagnostic processes are used, then diagnostic accuracy can be maintained through expert judgment, but diagnostic efficiency and productivity are reduced due to time-consuming manual review of patient data
Solution Approach 1:
The patent introduces an artificial intelligence model as an intermediary between patient data and diagnostic conclusions. This AI model automatically analyzes patient data, extracts relevant features, and generates diagnostic suggestions, thereby resolving the contradiction by automating the diagnostic process without replacing the ultimate clinical decision-making authority
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated AI-based system. The AI model processes patient data, identifies patterns, and generates diagnostic recommendations, substituting the time-consuming manual mechanical review with an efficient automated computational system that maintains diagnostic accuracy while significantly improving productivity
2Measurement precision
If comprehensive patient data is manually reviewed, then diagnostic accuracy is improved, but the complexity of the diagnostic process increases
Solution Approach 1:
The patent segments the complex diagnostic process into distinct modular components: data collection module, AI analysis module, and recommendation generation module. Each module handles specific tasks independently, reducing overall process complexity while maintaining comprehensive data analysis for accurate diagnosis
Solution Approach 2:
The AI model serves as an intermediary that systematically processes comprehensive patient data through structured algorithms. This intermediary organizes the complex information flow, automatically identifies relevant patterns, and presents simplified diagnostic recommendations, thereby maintaining high diagnostic accuracy while reducing perceived process complexity for clinicians
3Productivity
If AI models are used to predict diagnoses, then diagnostic efficiency is improved, but the reliability of diagnosis may be compromised without proper validation
Solution Approach 1:
The patent implements feedback mechanisms where AI diagnostic predictions are continuously validated against actual patient outcomes and clinician decisions. This feedback loop allows the system to learn from real-world performance, adjust its algorithms, and improve reliability over time while maintaining high diagnostic efficiency
Solution Approach 2:
The patent performs preliminary validation and training of AI models using extensive datasets before deployment. This preliminary action ensures that the AI system is thoroughly tested and validated for reliability before being used in clinical settings, thereby maintaining both efficiency and trustworthiness in diagnostic predictions
Data Source
AI summary
One variation of a method includes, during a first time period: initializing a diagnosis module corresponding to a diagnosis; extracting language signals, corresponding to the diagnosis, from a medical resource; interpreting a set of diagnosis indicators supporting the diagnosis based on the language signals; populating the diagnosis module with the set of diagnosis indicators; and storing the diagnosis module in a diagnostic module database. This variation of the method also includes, during a second time period succeeding the first time period: receiving confirmation of an encounter between a patient and a provider; predicting a positive diagnosis for the diagnosis exhibited by the patient based on a set of patient data extracted from a health record associated with the patient; generating a prompt to review the positive diagnosis for the patient; and serving the prompt to the provider.