Pre-Authorization Request Modeling for Faster Clinical Document Intake
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Solution Overview
Problem
Current medical pre-authorization systems are inefficient due to the reliance on nonclinical staff submitting requests, varying levels of clinical knowledge, and lack of guidance for required clinical documents, leading to prolonged decision times.
Innovation Solution
A system utilizing machine learning models to analyze structured and unstructured historical data, identify required clinical information, and provide proactive guidance to requesters, reducing the need for manual input and improving document accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If nonclinical staff submit pre-authorization requests with textual information and attached documents, then the system is easy to operate, but the turnaround time is prolonged due to varying experience levels and lack of clinical knowledge
Solution Approach 1:
The system enables self-service by allowing nonclinical staff to submit pre-authorization requests through an automated interface that guides them through the process. The machine learning model automatically analyzes submitted documents and identifies missing clinical information, eliminating the need for manual review and reducing turnaround time while maintaining ease of operation.
Solution Approach 2:
The patent replaces manual clinical review (mechanical human process) with an automated machine learning system. The ML model analyzes clinical documents, identifies required information, and generates requests for additional data automatically, substituting the manual workflow with an automated electronic system that reduces turnaround time while keeping the interface user-friendly.
2Reliability
If attestation-based solutions require multiple questions to be answered, then clinical information is captured more completely, but the ease of operation decreases as submitters need clinical knowledge
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a bridge between nonclinical staff and clinical requirements. The ML model analyzes submitted documents, identifies missing clinical information, and automatically generates targeted requests for additional data, eliminating the need for submitters to have clinical knowledge while ensuring complete information capture.
Solution Approach 2:
The system implements feedback by having the machine learning model automatically review submitted pre-authorization requests and identify missing clinical information. The model provides feedback in the form of specific requests for additional documents or information, guiding submitters through the process without requiring them to know clinical requirements beforehand, thus maintaining both completeness and ease of use.
3Reliability
If manual review of pre-authorization requests is performed, then accurate clinical decisions are made, but the productivity of the system decreases due to prolonged turnaround time
Solution Approach 1:
The system segments the pre-authorization review process into two parts: an automated machine learning phase that handles initial document analysis and identification of missing information, and a human review phase that focuses on final clinical decision-making. This segmentation allows parallel processing of multiple requests while maintaining accurate clinical review, thereby increasing throughput without sacrificing decision accuracy.
Solution Approach 2:
The machine learning model performs preliminary analysis of pre-authorization requests, including document classification, information extraction, and identification of missing clinical data, before human reviewers examine the cases. This preliminary action prepares and organizes the workload, enabling human reviewers to focus on critical decision-making tasks, thus increasing overall system productivity while maintaining accurate clinical decisions.
Data Source
AI summary
A method for reducing pre-authorization turnaround time is disclosed. The method includes, at a database, receiving historical data including a historical pre-authorization request and clinical information associated with a historical pre-authorization request. The method further includes receiving real-time data using an API gateway including real-time pre-authorization requests wherein the real-time data includes the real-time pre-authorization procedure and a clinical document category. The method further includes removing irrelevant data from real-time data and historical data to produce clean historical data and clean real-time data. The method further includes extracting data features required to train a machine learning model from the clean historical data and clean real-time data. The method further includes training the machine learning model by applying the extracted data features from the clean historical data and clean real-time data. The method further includes identifying prediction data results by applying the trained machine learning model.