ML Prediction Engine for Medical Imaging Request Approval
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Solution Overview
Problem
The existing process for reviewing medical imaging requests in healthcare settings is time-consuming and costly, leading to increased patient and provider complaints due to lengthy appeal processes when requests are initially denied.
Innovation Solution
A computerized method using a machine learning model to automatically process medical imaging records by generating likelihood estimates based on historical data, applying approval criteria, and selectively identifying exceptions to provisional outcomes, thereby streamlining the approval process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning models are used to process medical imaging requests, then processing speed and productivity improve, but system complexity increases
Solution Approach 1:
A machine learning prediction engine serves as an intermediary between the automated approval system and human reviewers. The engine generates likelihood estimates that guide the approval workflow, acting as a mediator that reduces the burden on human administrators while maintaining system complexity at an acceptable level through modular architecture
Solution Approach 2:
The approval system is segmented into distinct functional modules: automated criteria evaluation, machine learning prediction, exception handling, and human review. This segmentation allows each component to be independently optimized and maintained, managing overall system complexity while enabling high-speed automated processing
2Loss of time
If automated approval criteria are applied to medical imaging requests, then processing time is reduced, but approval accuracy may deteriorate
Solution Approach 1:
The system implements feedback loops where machine learning models are continuously trained on historical appeal data and outcomes. The prediction engine receives feedback from actual appeal results and adjusts its likelihood estimates accordingly, improving accuracy over time while maintaining fast automated processing
Solution Approach 2:
The system performs preliminary automated evaluation using multiple criteria before human review or appeal. This preliminary action filters out clearly eligible requests and prepares structured information for complex cases, reducing overall processing time while maintaining accuracy through multi-stage validation
3Loss of energy
If machine learning models predict appeal outcomes, then appeal process costs are reduced, but data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features from historical medical imaging records and appeal data for training the machine learning model. By selecting and extracting only critical data elements rather than processing complete datasets, the system reduces appeal process costs while managing data processing complexity through feature selection
Data Source
AI summary
A computerized method of automatically processing a medical imaging record using a machine learning model includes training a machine learning model prediction engine with historical medical imaging records, receiving a first medical imaging record from a first system, applying a set of specified approval criteria to the first medical imaging record to determine a provisional outcome, and in response to the provisional outcome being negative, selectively identifying an exception to the provisional outcome in response to input received by a user interface. In response to the exception not being identified, the method includes inputting a feature vector based on the first medical imaging record to the machine learning model prediction engine to generate a likelihood estimate, comparing the generated likelihood estimate to a target threshold, and in response to the generated likelihood estimate being greater than the target threshold, transmitting a signal indicating approval to the first system.