Incidental Finding Follow-Up Recommendation System
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Solution Overview
Problem
There is a need for accurate and appropriate follow-up recommendations for incidental findings encountered during healthcare examinations, as failure to act on these recommendations can lead to severe health concerns for patients.
Innovation Solution
A method and system that extract incidental findings and associated follow-up recommendations from patient records, compare them with third-party-defined policies, identify inconsistencies, and update clinical care workflows based on user feedback to ensure appropriate treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinicians manually review and determine follow-up recommendations for incidental findings, then they can exercise clinical judgment and adapt to individual patient cases, but the process is time-consuming and error-prone, leading to missed recommendations
Solution Approach 1:
The system enables incidental findings to 'self-service' by automatically extracting findings from medical records, comparing them against guideline policies, and generating follow-up recommendations without requiring manual clinician review for each finding. The automated system performs the initial assessment and recommendation generation, freeing clinicians to focus on complex cases requiring human judgment.
Solution Approach 2:
The patent replaces the manual mechanical process of clinician review with an automated computer-based system that uses natural language processing to extract findings, compares them against stored guideline policies, and generates recommendations. This substitution eliminates human error and time consumption while maintaining guideline adherence.
2Productivity
If the system automatically extracts follow-up recommendations from medical records, then processing speed increases, but inconsistencies between extracted recommendations and third-party policies may occur
Solution Approach 1:
The system incorporates feedback mechanisms where extracted follow-up recommendations are automatically compared against third-party guideline policies. When inconsistencies are detected, the system flags these discrepancies for review, allowing clinicians to verify and correct recommendations. This feedback loop ensures policy compliance while maintaining high processing speeds through automated comparison.
Solution Approach 2:
The patent introduces an intermediary verification layer where extracted recommendations pass through a comparison process against third-party policies before finalization. This intermediary step acts as a quality control mechanism that identifies and highlights inconsistencies without blocking the automated processing flow, thus maintaining productivity while ensuring reliability.
3Reliability
If clinicians manually verify each follow-up recommendation against guidelines, then policy compliance is ensured, but the complexity of the workflow increases and errors may still occur
Solution Approach 1:
The system performs preliminary action by pre-comparing extracted recommendations against third-party policies before they are presented to clinicians. The automated system pre-identifies potential inconsistencies and flags them for review, so clinicians do not need to manually verify every recommendation from scratch. This preliminary verification reduces workflow complexity while maintaining high policy compliance.
Solution Approach 2:
The patent segments the verification process into distinct automated stages: extraction of findings, comparison against guidelines, generation of recommendations, and identification of inconsistencies. This segmentation allows each stage to be optimized independently, with automated handling of routine cases and human intervention reserved only for complex inconsistencies, thereby reducing overall workflow complexity while ensuring compliance.
Data Source
AI summary
Methods and systems for selecting a treatment for a patient. The system extracts an incidental finding from a record associated with a patient and an associated follow-up recommendation. The system then determines whether any inconsistencies exist between the follow-up recommendation from the report and a follow-up recommendation prescribed by institutional or administrative guidelines. Any inconsistencies may then be resolved to guarantee an appropriate workflow for patient care.