Prior Authorization Routing Using Population Data Thresholds
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Solution Overview
Problem
Current pharmaceutical fulfillment systems face inefficiencies and delays in processing temperature-sensitive medications and disease mitigation, particularly in managing prior authorizations (PAs) for medications, leading to sluggish responses and increased disease spread.
Innovation Solution
An enhanced computing architecture that tracks treatment characteristics in real-time, using machine learning to automate PA processes when conditions meet thresholds, and switches to manual review for complex or ambiguous cases, reducing latency and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review processes are used for all prior authorization requests, then accuracy and reliability are maintained, but processing time and productivity deteriorate
Solution Approach 1:
The authorization process is segmented into two distinct pathways: an automated processing channel for straightforward cases and a manual review channel for complex or ambiguous cases. This segmentation allows the system to handle routine requests efficiently through automation while reserving human expertise for cases requiring judgment, thereby resolving the contradiction between speed and accuracy.
Solution Approach 2:
The system dynamically routes authorization requests to appropriate processing channels based on real-time analysis of request characteristics, population data, and risk factors. This dynamic allocation optimizes resource utilization by matching task complexity with processing capability, achieving both high throughput for simple cases and high accuracy for complex cases.
2Productivity
If automated processes are used for all prior authorization requests, then productivity and processing speed improve, but reliability and accuracy deteriorate
Solution Approach 1:
An intelligent routing intermediary is introduced that assesses each authorization request and directs it to the appropriate processing channel. This intermediary analyzes request characteristics, population health data, and risk factors to determine whether automated processing or manual review is more appropriate, ensuring that accuracy is maintained for complex cases while maximizing throughput for straightforward cases.
Solution Approach 2:
The system changes the processing parameter (automated vs. manual) based on the characteristics of the authorization request. By evaluating multiple parameters including population risk data, request complexity, and urgency, the system dynamically adjusts the processing approach to optimize both speed and accuracy for each specific case.
3Reliability
If computing resources are allocated to analyze all population data for every request, then authorization accuracy improves, but energy consumption and computational cost increase
Solution Approach 1:
The system performs partial data analysis by selectively applying computational resources only to the extent necessary for each request. Straightforward cases receive minimal processing with basic checks, while only complex or high-risk cases trigger comprehensive population data analysis. This partial action approach maintains accuracy for cases that need it while significantly reducing overall computational energy consumption.
Solution Approach 2:
The system applies different levels of analysis quality to different requests based on their characteristics. High-risk or complex requests receive thorough population data analysis with high computational resources, while routine requests receive streamlined processing with minimal resource usage. This local quality differentiation optimizes the balance between accuracy and energy consumption across the entire authorization workload.
Data Source
AI summary
Technology identifies at least one object part of provided data from an object repository that corresponds to provided objects that were provided from the object repository, and extrapolates a first attribute associated with a condition based on the at least one object part. The technology receives a request for a first object associated with a first user and the condition. The technology determines whether to execute an automatic process to determine whether to permit the request based on the first attribute, when the automatic process is determined to be executed to determine whether to permit the request, automatically analyzes user data of the first user and an object attribute associated with the first object to determine whether to permit the request, and when the automatic process is determined not to be executed, assigning the request to a queue for a manual review to determine whether to permit the request.