Machine Learning Prior Authorization System
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Solution Overview
Problem
The existing authorization processes for medical treatments and prescriptions are slow due to human oversight, leading to increased costs and inefficiencies, as they require manual review and decision-making, which can take days or even months, affecting patients, physicians, and pharmacies.
Innovation Solution
The implementation of a deep learning and machine learning system that uses neural networks and algorithms to automatically predict and authorize prior authorizations and renewals, reducing the need for human intervention by analyzing historical data and patient information to make rapid and accurate decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human oversight and manual review are used for prior authorization decisions, then decision-making accuracy and reliability are improved, but processing speed and productivity deteriorate significantly
Solution Approach 1:
The patent replaces the manual human review process with an automated machine learning system that uses natural language processing and predictive algorithms to analyze clinical documentation and make prior authorization decisions. This substitution maintains decision accuracy through trained models while dramatically increasing processing speed from days to seconds.
Solution Approach 2:
The patent introduces an automated authorization system as an intermediary between the healthcare provider's request and the final decision. This intermediary uses machine learning models to predict authorization outcomes and generate recommendations, serving as a bridge that maintains reliability while improving productivity.
2Reliability
If multiple reviewers and appeal processes are implemented, then authorization decision reliability is improved, but processing time and loss of time increase
Solution Approach 1:
The patent performs preliminary analysis and prediction using machine learning models before human review is needed. The system pre-evaluates authorization requests, identifies likely outcomes, and prepares recommendations in advance, reducing the need for multiple review cycles and appeals while maintaining decision reliability.
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning system learns from outcomes of authorization decisions and appeal results. This feedback loop continuously improves the model's accuracy, reducing reliance on multiple human reviewers and appeal processes while maintaining or improving decision reliability.
3Measurement precision
If comprehensive manual review of medical history and claims is performed, then measurement precision and reliability of authorization decisions are improved, but device complexity and operational complexity increase
Solution Approach 1:
The patent replaces complex manual review processes with automated machine learning systems that use natural language processing to analyze medical histories and claims. This substitution maintains comprehensive assessment accuracy while reducing operational complexity by automating data collection, analysis, and decision support.
Solution Approach 2:
The patent creates a universal authorization system that handles multiple types of medical claims, drug authorizations, and review scenarios through a single machine learning platform. This multi-functional system maintains comprehensive review capabilities while reducing overall system complexity through standardization and automation.
4Reliability
If human negotiation and detailed review processes are used, then authorization decision quality is improved, but costs increase due to extended processing time and resource requirements
Solution Approach 1:
The patent replaces resource-intensive human negotiation and review processes with automated machine learning systems that provide decision support and recommendations. This substitution maintains decision quality through trained models while significantly reducing processing costs by eliminating manual labor requirements.
Solution Approach 2:
The patent enables the authorization system to serve itself through automated machine learning models that independently analyze claims, predict outcomes, and generate recommendations without requiring extensive human intervention. This self-service capability maintains decision quality while reducing operational costs.
Data Source
AI summary
Methods and systems for selecting a deep learning/machine learning model are described. In one embodiment, a plurality of models are trained using the first subset of records, each of the plurality of models are implemented to predict a value of respective known target columns in each of a second subset of records, respective success rates for each of the plurality of models at predicting the respective known target columns for each of the second subset of records are determined, a subset plurality of the plurality of models based on success rate is selected, and the subset plurality of the plurality of models is implemented to decide a target column of a received authorization request based on variables in the received authorization request using a democratic process.