Tone-Aware Care Plan Generation from Unstructured EMR Data
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Solution Overview
Problem
Existing healthcare systems face inefficiencies in managing population health data, including time-consuming EMR reviews, resource wastage, and lack of interoperability, leading to incorrect diagnoses and inconvenient appointment scheduling, overwhelming patient information, and inefficient information distribution.
Innovation Solution
A cognitive intelligence platform that integrates data sources, performs conversational analysis, and uses knowledge graphs to cognify unstructured data, providing personalized and efficient care plans, appointment scheduling, and curated information distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually review EMR data to generate care plans, then diagnostic accuracy is improved, but time consumption and resource wastage increase
Solution Approach 1:
The patent introduces an AI-based natural language processing intermediary that automatically analyzes unstructured EMR data, extracts relevant health artifacts, and generates structured care plans. This intermediary system bridges the gap between raw medical records and actionable care plans, maintaining diagnostic accuracy while eliminating manual review time.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system that uses machine learning algorithms to process and interpret medical data. This substitution transforms the manual cognitive task into an automated information processing task, significantly reducing time consumption while preserving diagnostic quality.
2Loss of information
If comprehensive patient data is provided to patients, then information completeness is improved, but information overload and convenience decrease
Solution Approach 1:
The patent applies local quality by customizing information delivery to each patient's specific needs, preferences, and comprehension level. Rather than providing uniform comprehensive data to all patients, the system selectively presents relevant information in appropriate formats, ensuring completeness for each individual while maintaining ease of access and understanding.
Solution Approach 2:
The patent segments comprehensive medical information into manageable, organized components tailored to patient needs. The system divides complex medical data into discrete, easily consumable units with varying levels of detail, allowing patients to access complete information when needed while providing simplified summaries for routine interactions.
3Device complexity
If traditional healthcare systems are used, then system simplicity is maintained, but interoperability and efficiency worsen
Solution Approach 1:
The patent implements a universal healthcare management platform that performs multiple functions including data aggregation from diverse sources, automated care plan generation, patient communication, and outcome tracking. This multi-functional system replaces multiple separate traditional systems, improving interoperability and efficiency while maintaining manageable complexity through integrated design.
Data Source
AI summary
A method for electronically generating a care plan is disclosed herein. The method includes comparing a first data structure with a second data structure. The first data structure includes a set of health artifacts pertaining to a first condition of the patient. The second data structure pertains to the patient and the first condition of the patient, and the second data structure includes a subset of the set of the health artifacts. Responsive to the comparing, the method also includes generating the care plan comprising another subset of the set of health artifacts, and modifying the another subset of the set of health artifacts in the care plan based on a detected tone of the patient, a detected emotion of the patient, a medical outcome desired by a physician, or some combination thereof.