Clinical Context Prediction for Automated Care Gap Detection

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Solution Overview

Problem

Current tools for managing and analyzing patient clinical data are inefficient and ineffective in coordinating treatments and care, particularly for chronic care management, due to the complexity and variety of data sources.

Innovation Solution

A system utilizing a deep-learning reportability classifier and masking technique to automatically assign context labels to named entity phrases in clinical records, identify care gaps and adverse health trends, and update machine learning algorithms with human feedback for improved data interpretation and care coordination.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional manual methods are used to manage and analyze patient clinical data, then data accuracy and interpretability can be maintained through human expertise, but the system suffers from low efficiency and high time consumption in coordinating care and identifying care gaps

Engineering Contradiction:

Improveefficiency of clinical data analysisVSAvoidtime for care coordination and data interpretation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables automated self-service analysis of clinical data through machine learning algorithms that automatically interpret clinical records, identify care gaps, and generate coordination recommendations without requiring manual human analysis for each patient case

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of data review and care coordination are replaced with automated computational systems using deep learning algorithms and natural language processing to analyze clinical data and identify patterns

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If deep-learning algorithms and machine learning techniques are implemented for automated analysis, then analysis efficiency and productivity are improved, but the system complexity and difficulty of implementation increase

Engineering Contradiction:

Improveautomated data analysis throughputVSAvoidcomplexity of machine learning system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning system is designed to perform multiple functions including data normalization, entity recognition, context prediction, reportability assessment, and care gap identification within a single integrated platform, reducing the need for separate specialized systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system employs intermediary components such as trained machine learning models and preprocessed data representations that bridge the gap between raw clinical data and actionable insights, simplifying the overall system architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If automated machine learning systems are used to identify care gaps and interpret clinical data, then speed and throughput are improved, but the precision and reliability of clinical interpretations may be compromised

Engineering Contradiction:

Improvespeed of clinical data processingVSAvoidaccuracy of context prediction and care gap identification

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system incorporates feedback mechanisms where model predictions are evaluated against actual clinical outcomes and provider decisions, with performance metrics used to iteratively refine and improve the accuracy of context prediction and care gap identification

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions including extensive training of machine learning models on labeled clinical data and pre-processing of clinical records into standardized formats before deployment, establishing a foundation for accurate predictions

Inventive Principle:

Principle #10Preliminary action

4Measurement precision

If comprehensive analysis of all clinical data is performed to ensure accurate interpretation, then measurement precision is improved, but the loss of time and computational resources increases

Engineering Contradiction:

Improveaccuracy of clinical data interpretationVSAvoidcomputational time for data analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial action by focusing analysis on the most relevant features and contexts identified through trained models, rather than uniformly processing all possible data elements, achieving sufficient precision with reduced computational effort

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The analysis process is segmented into distinct stages including data preprocessing, feature extraction, context prediction, and care gap identification, allowing each stage to be optimized independently and processed efficiently

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12626799B2Systems and methods for weakly-supervised reportability and context prediction, and for multi-modal risk identification for patient populations

Publication Date: 2026.05.12 SQ CARE MANAGEMENT LLC

AI summary

Presented herein are systems and methods for automated analysis of patient data. More particularly, in certain embodiments, the invention relates to systems and methods for predicting the context of a particular phrase (e.g. the name of a diagnosis/condition) in a clinical record of a patient using a reportability classifier. In another aspect, the invention relates to systems and methods for automatically identifying a potential care gap and/or adverse health trend for a patient from clinical data.