Patient Provider Matching System Personalization Complexity
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Solution Overview
Problem
Patients face challenges in finding medical care providers that meet their unique needs, as existing systems lack personalized search capabilities and rely heavily on referrals or insurance directories, which do not adequately consider individual preferences for physical, mental, and emotional health care.
Innovation Solution
A patient provider matching system that uses a database and predictive models to rank medical providers based on patient and provider data, incorporating natural language processing to analyze patient reviews and generate a personalized list of search results, including provider qualifications, claims data, and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional referral systems or insurance directories are used to find providers, then the search process is simple and relies on existing networks, but the results lack personalization and do not adequately consider individual patient preferences and needs
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing patient preferences, provider qualifications, and claims data before the actual provider search. Predictive models are pre-trained with historical data to enable rapid personalized matching when patients search for providers, resolving the contradiction by preparing personalization capabilities in advance without requiring complex real-time processing
Solution Approach 2:
The patent introduces an intermediary matching system that sits between traditional insurance directories and patients. This intermediary layer processes patient preferences through predictive models and natural language processing to generate personalized provider recommendations, adding personalization capability while managing complexity through a dedicated intermediate component rather than requiring complete system redesign
2Measurement precision
If comprehensive patient and provider data is collected and analyzed using predictive models, then the accuracy of provider matching improves, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial action by using predictive models to analyze only the most relevant features and data points for matching, rather than processing all available data equally. Natural language processing selectively extracts key information from patient reviews and provider qualifications, achieving high matching accuracy while reducing computational resource consumption by focusing on critical factors
Solution Approach 2:
The patent changes parameters by transforming unstructured data (patient reviews, provider descriptions) into structured features through natural language processing. This parameter transformation enables efficient computational processing of comprehensive data while maintaining high matching accuracy, as the processed parameters are optimized for predictive model input
3Loss of information
If natural language processing is used to analyze patient reviews and provider qualifications, then the system can extract meaningful insights and improve matching quality, but the processing complexity and time required increase
Solution Approach 1:
The system extracts only the most relevant information from patient reviews and provider qualifications using natural language processing, rather than analyzing entire texts in detail. Key features such as provider specialties, patient satisfaction indicators, and qualification keywords are extracted and fed into predictive models, reducing processing time while maintaining information extraction capability
Solution Approach 2:
Natural language processing performs preliminary action by pre-processing and structuring provider qualifications and patient reviews before they are used in matching. This preliminary extraction and categorization of key information reduces the complexity of subsequent analysis and accelerates the overall matching process
Data Source
AI summary
Systems and methods herein describe a patient provider matching system. The matching system receives a query from a patient user on a client device, generates supplemental questions, receives responses to the supplemental questions, identifies search results related to the query, for each search result in the identified set of search results: determines a probability that the patient user will select a provider associated with the search result based on a set of patient data associated with the patient user and a set of provider data associated with the provider and ranks the search result based on the determined probability and a set of system preference criteria, and causes display of the ranked set of search results on the graphical user interface of the client device.