Automated Patient-Provider Matching System for Behavioral Health
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Solution Overview
Problem
There is a low percentage of people newly diagnosed with behavioral health conditions accessing care with a behavioral health provider, indicating a need to improve access and engagement with mental health services, particularly for those with comorbidities, as early treatment cessation and high healthcare costs are prevalent.
Innovation Solution
An automated method for entity field correction and multidimensional provider matching, involving machine learning models, eligibility determination, exclusion criteria, and provider data integration through multiple APIs to identify and match patients with suitable healthcare providers, facilitating targeted outreach and improved treatment adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual patient-provider matching methods are used, then provider-patient relationships can be established, but the process is time-consuming and inefficient, resulting in low patient access to behavioral health services
Solution Approach 1:
The patent replaces manual mechanical matching processes with an automated computer-based system that uses machine learning models and algorithms to perform patient-provider matching, eliminating the need for manual review and significantly reducing processing time
Solution Approach 2:
The patent introduces an automated matching system as an intermediary between patients and providers, using eligibility determination modules, exclusion criteria filters, and machine learning-based matching algorithms to facilitate connections without direct manual intervention
2Measurement precision
If comprehensive eligibility criteria and exclusion filters are implemented, then patient matching accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex eligibility determination process into separate modular components: an eligibility determination module that applies inclusion criteria, an exclusion determination module that applies exclusion filters, and a machine learning-based matching module, allowing each component to be independently optimized and maintained
Solution Approach 2:
The patent uses machine learning models that can dynamically adjust matching parameters and criteria based on learned patterns from historical data, enabling the system to maintain high accuracy while adapting to changing requirements without manual reconfiguration
3Productivity
If automated machine learning-based matching is used, then matching efficiency and scalability are improved, but the initial setup and training requirements increase system complexity
Solution Approach 1:
The patent implements preliminary training of machine learning models using historical patient-provider matching data before deployment, so that the models are pre-equipped with learned patterns and can perform high-speed matching without requiring complex real-time computations
Solution Approach 2:
The patent designs the machine learning models to be universally applicable across different patient populations and provider networks, using standardized feature extraction and matching algorithms that can handle diverse data types without requiring separate specialized systems
Data Source
AI summary
A method for automated entity field correction includes receiving one or more target health conditions, obtaining a set of multiple patient entries, stored patient data, stored claims data and stored prescription data, and determining an eligibility status for each patient entry according to specified eligibility criteria, indicative of the patient entry being eligible for targeted outreach regarding the target health condition(s). For each patient entry in an eligible subset, the method includes determining an exclusion status for the patient entry according to specified exclusion criteria, indicative of the patient entry being excluded from targeted outreach regarding the target health condition(s). The method includes accessing stored provider data, and for each patient entry in the non-excluded subset, determining a provider match for the patient entry according to at least a portion of the stored provider data, and transmitting the provider match to a computing device associated with the patient entry.