Automated Radiology Analyzer for Cross-Institutional Utilization Metrics
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Solution Overview
Problem
Current methods for gauging radiology utilization across institutions are impractical due to variations in billing codes and imaging protocols, lacking a standardized and efficient way to quantify radiology performance, especially in terms of positive scan rates and clinical appropriateness criteria.
Innovation Solution
A comprehensive database and automated software tool utilizing natural language processing (NLP) and automated analysis to optimize keyword searches, parse data, and generate accurate positive scan rates for imaging protocols, enabling efficient comparison and analysis of radiology utilization across different departments and institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If economic based quantification system is used to gauge radiology utilization, then internal departmental analysis can be performed, but the system becomes impractical for cross-institution comparison due to variations in billing codes and cost allocation
Solution Approach 1:
The patent transitions from economic parameters (billing codes, RVU, cost allocation) to statistical parameters (positive scan rates, imaging findings) for gauging radiology utilization. This parameter change enables cross-institution comparison by using standardized diagnostic criteria that are independent of institutional billing practices and cost structures.
2Adaptability or versatility
If statistical quantification system based on positive scan rates is used, then cross-institution comparison becomes feasible, but the system lacks information on clinical appropriateness criteria satisfaction
Solution Approach 1:
The patent segments the evaluation system into multiple independent components: (1) positive scan rate metrics for cross-institution comparison, and (2) appropriateness criteria assessment for clinical quality evaluation. This segmentation allows each component to fulfill its specific function without being constrained by the limitations of the other.
Solution Approach 2:
The patent introduces automated natural language processing tools as intermediaries to extract and analyze appropriateness criteria information from unstructured radiology reports. This intermediary system bridges the gap between statistical metrics and clinical quality assessment by automatically parsing and evaluating criteria satisfaction from text data.
3Measurement precision
If manual research methods are used to identify positive scan rates, then accurate imaging positivity rates can be obtained, but the process becomes laborious and time-intensive
Solution Approach 1:
The patent replaces manual mechanical research methods with automated computational systems. Natural language processing algorithms and automated data extraction tools substitute for human researchers, enabling rapid and accurate identification of positive scan rates from large volumes of radiology reports without the time-consuming manual review process.
Solution Approach 2:
The patent implements self-service automated systems that independently extract, analyze, and calculate positive scan rates from radiology data without requiring manual intervention. The automated analyzer performs all necessary steps including data retrieval, natural language processing, and metric calculation, enabling institutions to generate accurate positivity rates autonomously and efficiently.
Data Source
AI summary
A sequential series of methods for optimized searching within a text (or alphanumeric string) database to retrieve specific and relevant results, followed by optimized restructuring and parsing of text data (or alphanumeric string), followed by creation/application of a natural language processing engine, followed by the creation/application of an automated analyzer is presented.