Modular Patient Analytics System for Clinical Data Overload
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinicians face challenges in identifying important patient health information due to data overload from test results and charts, leading to difficulties in making real-time treatment decisions, and there is a lack of motivation to chart data as it contributes to the overload without clear importance in treatment decisions.
Innovation Solution
A modular patient analytics system integrating machine learning modules with EMR systems to process and present patient data, allowing clinicians to select and validate machine learning models, train models using site-specific data, and provide real-time risk assessments and care alerts, thereby simplifying data output and encouraging more comprehensive data charting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If clinicians chart comprehensive patient data, then data completeness improves, but data overload increases making it difficult to identify important information
Solution Approach 1:
The system extracts only the most critical patient information from comprehensive data sets using machine learning algorithms. The NLP module identifies and extracts key entities and relationships, while the ML models prioritize which information is most relevant for clinical decision-making, presenting only essential data to clinicians.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and clinical presentation. This layer includes NLP processors, machine learning models, and risk calculation engines that mediate between raw comprehensive data and clinically actionable insights, filtering and transforming data to reduce overload while preserving completeness.
2Loss of information
If comprehensive patient data is collected and charted, then information availability improves, but time to process and analyze data increases
Solution Approach 1:
The system performs preliminary processing of patient data through NLP extraction and machine learning analysis before clinical review. Risk scores are pre-calculated, key findings are pre-identified, and critical information is pre-prioritized, allowing clinicians to immediately see actionable insights without processing raw data during time-critical decisions.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated machine learning systems. ML models automatically analyze comprehensive patient data, identify patterns, calculate risks, and generate insights without human intervention, dramatically reducing processing time while maintaining or improving information availability.
3Measurement precision
If machine learning models are customized and trained with site-specific data, then model accuracy improves, but system complexity and training requirements increase
Solution Approach 1:
The patent segments the machine learning system into modular components: pre-trained base models, site-specific customization layers, and configurable parameters. This allows incremental customization where sites can adjust specific model parameters or retrain only on local data without rebuilding entire systems, reducing complexity while improving accuracy.
Solution Approach 2:
The system enables customization through parameter adjustments rather than complete model retraining. Sites can modify model parameters, thresholds, and configuration settings to adapt to local conditions, achieving improved accuracy for their specific population without the complexity of full model development and training infrastructure.
Data Source
AI summary
A modular patient analytics system, including: an integrated machine learning module configured to receive patient data and to apply machine learning models to the received patient data; machine learning services that includes default machine learning models and trained machine learning models; a testing service configured to receive real time patient data, to run a machine learning model to produce a model output, and to provide the model output to the integrated machine learning module; a model training service configured to receive site training data, to train a machine learning model using the site training data, and to provide the trained machine learning model to the machine learning services; and a classification service configured to receive and provide pseudo labels for patient data from the site data for use in semi supervised learning of machine learning models as well as model validation on updated and existing machine learning models.