AI Radiology Reporting Workflow for Legacy RIS Integration
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Solution Overview
Problem
Radiology centers face inefficiencies and challenges in managing day-to-day operations due to outdated IT systems, leading to increased error rates, staff stress, and difficulty in integrating emerging technologies and continuing medical education, which affects the quality of radiological assessments and customer service.
Innovation Solution
An intelligent healthcare facility operations tracking and record generating system that integrates a CRM platform with a RIS, utilizing machine learning models to generate summary electronic reports tailored to physician preferences, and provides an objective scoring system for radiology centers based on operational metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy IT systems are used in radiology centers, then existing infrastructure is maintained, but operational efficiency deteriorates and error rates increase
Solution Approach 1:
The patent introduces an intermediary AI-powered platform that sits between legacy radiology systems and modern operational requirements. This intermediary layer processes data from outdated systems, applies machine learning models to generate optimized workflows, and delivers improved operations without requiring complete system replacement, thus resolving the contradiction between maintaining infrastructure and improving efficiency
Solution Approach 2:
The system transforms operational parameters by using AI models to analyze legacy system data and generate optimized workflows. Machine learning algorithms process historical data to identify patterns and recommendations for improving efficiency, effectively changing operational parameters without changing the underlying legacy infrastructure
2Productivity
If radiology centers increase scan volume, then revenue increases, but quality of service and reporting precision deteriorate
Solution Approach 1:
The AI system performs self-service by automatically analyzing scan data, generating reports, and providing quality feedback without requiring additional manual intervention. Machine learning models process high volumes of data independently, maintaining reporting precision even as scan volume increases, thus resolving the contradiction between productivity and precision
Solution Approach 2:
The system implements feedback loops where AI models continuously analyze reporting quality and provide recommendations for improvement. This feedback mechanism ensures that reporting precision is maintained or improved even as scan volume increases, as the system learns from each interaction to refine its output
3Productivity
If medical staff workload increases due to legacy systems, then operational capacity is maintained, but staff productivity and satisfaction deteriorate
Solution Approach 1:
The AI system performs self-service by automating routine tasks such as data collection, report generation, and quality monitoring. This automation frees medical staff from repetitive manual work while maintaining operational capacity, significantly improving ease of operation and staff satisfaction without sacrificing productivity
Solution Approach 2:
The patent replaces mechanical manual processes with AI-powered automation. Machine learning models handle data processing, report generation, and quality checks that previously required manual intervention, substituting human labor with intelligent systems that maintain or improve operational capacity while reducing staff workload
4Reliability
If continuing medical education is integrated into RIS, then quality of care improves, but system complexity increases
Solution Approach 1:
The system merges CME functionality with the existing RIS platform by integrating AI-powered learning modules directly into the workflow. This combination allows quality of care to improve through continuous education while avoiding the complexity of separate integrated systems, as the AI layer unifies multiple functions within the existing infrastructure
Data Source
AI summary
Described is a method, comprising: receiving, a template electronic report configured to display information in a graphical format associated with one or more preferences of a physician; receiving, patient information associated with a medical assessment conducted on a patient of the physician; generating a summary electronic report by populating the template electronic report at least in part by processing the patient information and the one or more preferences of the physician with a trained machine learning model; wherein the summary electronic report is configured to display a summary of the medical assessment in the graphical format associated with the one or more preferences of the physician; wherein the summary of the medical assessment comprises a subset of information comprised within the complete electronic record of the medical assessment; and wherein the subset of information comprises at least one image.