Remote Image Interpretation Management Apparatus
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Solution Overview
Problem
Current remote image interpretation systems lack the ability for clients to select image interpretation companies or doctors based on reliable quality evaluations, leading to inconsistent and potentially low-quality diagnosis reports due to varying evaluation standards and arbitrary ratings.
Innovation Solution
A remote image interpretation management apparatus that normalizes evaluation values for image interpretation facilities and doctors by calculating statistics and mean values based on a predetermined number of evaluations, ensuring reliable and comparable quality assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If evaluation values are collected from multiple evaluators, then the quantity of evaluation data increases, but the reliability of evaluation results decreases due to varying evaluation standards between evaluators
Solution Approach 1:
The patent transforms evaluation values by applying statistical parameters (mean and standard deviation) to normalize data from different evaluators. This parameter transformation allows aggregation of large quantities of evaluation data while maintaining reliability by accounting for individual evaluator characteristics through statistical normalization.
Solution Approach 2:
Statistical parameters (mean and standard deviation) serve as intermediaries between raw evaluation values and aggregated results. These intermediaries normalize the data by adjusting for evaluator-specific biases, enabling reliable aggregation of evaluation data from multiple sources with different standards.
2Reliability
If evaluation standards are made uniform across evaluators, then the reliability of evaluation results improves, but the ability to capture individual evaluator perspectives decreases
Solution Approach 1:
Rather than forcing uniform evaluation standards, the patent transforms each evaluator's data using their own statistical parameters (mean and standard deviation). This approach maintains the adaptability to individual evaluator perspectives while achieving reliability through statistical normalization that accounts for personal evaluation tendencies.
Solution Approach 2:
The patent applies different statistical transformations to each evaluator's data based on their individual characteristics. This local quality approach allows each evaluator's unique perspective to be preserved while their data is normalized to a common scale, capturing individual perspectives while ensuring overall reliability.
3Productivity
If all evaluation values are aggregated without filtering, then the productivity of evaluation processing increases, but the quality of results decreases due to inclusion of unreliable evaluations
Solution Approach 1:
The patent uses statistical parameters (standard deviation) to identify and filter outlier evaluation values. By transforming evaluation data and comparing standardized values against thresholds, the system efficiently filters unreliable evaluations while maintaining high productivity through automated statistical processing rather than manual review.
Solution Approach 2:
The patent replaces manual quality assessment with automated statistical processing. Mechanical filtering based on standardized deviation thresholds substitutes for human judgment, enabling efficient high-volume processing of evaluation data while maintaining quality through objective statistical criteria.
Data Source
AI summary
A remote image interpretation management apparatus includes a hardware processor. The hardware processor obtains, from a storage storing evaluation values about quality of image interpretation reports by evaluator and by image interpretation facility and/or image interpretation doctor, evaluation values of evaluators who have evaluated a predetermined number of image interpretation reports or more. Based on the obtained evaluation values of the evaluators, for each of the evaluators, the hardware processor calculates a statistic of the evaluation values of the evaluator and normalizes the evaluation values with the calculated statistic, thereby obtaining normalized evaluation values for the respective evaluators. For each image interpretation facility and/or each image interpretation doctor, the hardware processor calculates a mean value based on the normalized evaluation values of the respective evaluators.