Oncology EHR AI Pipeline for Continuous Cancer Prognostics
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Solution Overview
Problem
Current oncology systems face challenges in processing diverse siloed oncology data effectively, limiting the clinical applicability of AI and ML for personalized cancer treatment and prognostics due to fragmented information systems and inadequate data processing capabilities.
Innovation Solution
A system and method utilizing artificial intelligence and machine learning to process electronic health records (EHR) through natural language processing (NLP) and machine learning models, integrating structured and unstructured data to provide continuous cancer treatment and prognostics, including immunotherapy, targeted therapy, radiation therapy, and chemotherapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If comprehensive EHR systems are adopted to collect and store diverse oncology data, then the quantity and variety of available medical data increase, but the ability to process and integrate this fragmented data meaningfully remains insufficient
Solution Approach 1:
The patent combines multiple fragmented data sources including EHR systems, imaging systems, pathology systems, and genomics systems into a unified AI processing platform. This merging allows the system to handle diverse oncology data types (structured and unstructured) through integrated natural language processing and machine learning models, resolving the contradiction between data quantity and processing capability
Solution Approach 2:
The patent introduces AI and ML algorithms as intermediary components between raw medical data and clinical decision-making. These intermediaries process, interpret, and synthesize fragmented data from multiple siloed systems, transforming unstructured notes, imaging data, and lab results into actionable clinical insights without requiring direct integration of all source systems
2Measurement precision
If AI and ML models are developed to process unstructured medical notes and diverse data types, then the prognostic accuracy and treatment personalization improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex AI processing system into specialized modules: natural language processing components for unstructured notes, imaging analysis components for radiology and pathology images, genomics components for molecular data, and integration components for synthesizing results. This segmentation allows each module to specialize in specific data types while reducing overall system complexity through modular architecture
Solution Approach 2:
The patent develops multi-functional AI models that can process multiple data types (structured EHR data, unstructured clinical notes, imaging data, genomics data) through a unified framework. These universal models reduce system complexity by eliminating the need for separate specialized systems for each data type while maintaining high prognostic accuracy across diverse oncology contexts
3Reliability
If continuous monitoring and updating of patient data is implemented, then real-time prognostic updates and treatment adjustments are enabled, but the computational load and data processing time increase
Solution Approach 1:
The patent implements periodic updating of AI models with new patient data at clinically appropriate intervals rather than continuous real-time processing. The system schedules model retraining and prognostic updates based on clinical events (e.g., after each treatment cycle, imaging study, or clinical visit), maintaining reliable prognostics while avoiding unnecessary computational overhead from continuous processing
Solution Approach 2:
The patent pre-processes and structures medical data during initial patient intake and routine clinical encounters, organizing unstructured notes and diverse data types into standardized formats suitable for AI analysis. This preliminary action reduces the computational burden during critical prognostic updates by having data ready for rapid processing when clinical decisions are needed
Data Source
AI summary
Oncology faces a digital chasm in its quest for personalized treatments. Despite the adoption of electronic health records (EHR), most hospitals are ill-equipped for data science research. Embodiments herein describe a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence to address unmet clinical needs. Embodiments describe systems and methods for improved cancer prognostics, including by obtaining electronic medical records and performing natural language processing thereon. Additional embodiments apply term frequency inverse document frequency to identify terms that are predictive for cancer survival. Additional embodiments are capable of performing in silico clinical trials based on information comprised in a collection of health records.