Gradient Detection of Incidental Disease Indicators
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Solution Overview
Problem
Current methods for identifying incidental medical findings in clinical reports are inconsistent and often overlooked due to the reliance on human skill and time constraints, leading to potential missed diagnoses or over-reporting of insignificant findings.
Innovation Solution
A dynamic framework utilizing natural language processing techniques to parse medical reports, identify clinical cues, and generate condition alerts based on gradient levels of risk and severity, allowing for selective and accurate detection of significant incidental findings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicians manually review medical reports to identify incidental findings, then diagnostic accuracy may be improved through human expertise, but time consumption and workload increase significantly
Solution Approach 1:
The patent introduces an intermediary system that includes natural language processing circuitry and incidental finding circuitry. This intermediary automatically processes medical reports, extracts clinical cues, and identifies incidental findings, thereby reducing the time burden on physicians while maintaining diagnostic accuracy through automated analysis
Solution Approach 2:
The system enables self-service by allowing the medical report processing and incidental finding identification to occur automatically without requiring physician intervention for each report. The apparatus processes reports, derives textual components, identifies medical findings, and generates condition alerts autonomously
2Reliability
If all incidental findings are reported to ensure comprehensive diagnosis, then diagnostic completeness improves, but false alarms and unnecessary follow-ups increase
Solution Approach 1:
The patent applies local quality by differentiating between significant and insignificant incidental findings through gradient severity levels. The incidental finding circuitry assigns different weights and priorities to various findings based on their clinical significance, allowing the system to focus attention on high-priority findings while filtering out low-priority ones, thus reducing false alarms
Solution Approach 2:
The system changes parameters by introducing gradient severity levels and risk scores to characterize incidental findings. By transforming the binary significant/insignificant classification into a multi-level severity gradient, the system can prioritize follow-up actions and reduce unnecessary interventions for low-severity findings
3Reliability
If physicians review every medical report in detail, then detection of significant findings improves, but productivity and efficiency decrease
Solution Approach 1:
The patent implements preliminary action by having the natural language processing circuitry and incidental finding circuitry process medical reports and identify potential findings before physician review. The system pre-processes reports, extracts relevant information, and generates condition alerts, allowing physicians to focus their attention only on reports with significant findings rather than reviewing every report in detail
Solution Approach 2:
The patent segments the medical report processing task into distinct functional components: natural language processing circuitry for text extraction, incidental finding circuitry for finding identification, and condition alert generation. This segmentation allows parallel processing and automation of routine tasks, significantly improving physician productivity while maintaining detection accuracy
Data Source
AI summary
Computer program products, methods, systems, apparatus, and computing entities are described for identifying significant incidental findings from medical records. In one example embodiment, an example computing device receives a medical report and derives a textual component from the medical report. The computing device then identifies one or more medical findings from the textual component and determines a clinical context for each of the one or more medical findings. The computing device then identifies one or more clinical cues from the one or more medical findings and generates one or more condition signals from the one or more clinical cues. The computing device then generates a condition alert from the one or more condition signals. The condition alert is indicative of a significant incidental finding. Using various embodiments contemplated herein, significant incidental findings can be identified for follow-up by a user.