Dynamic Decision Forest for Clinical Data Capture
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Solution Overview
Problem
Medical professionals face challenges in efficiently capturing and documenting clinical information during patient consultations due to information overload and the need for additional technical skills when using electronic medical record systems, which can divert attention from patient interaction.
Innovation Solution
A dynamic decision forest system that uses a series of interconnected decision trees without predefined relationships, allowing for adaptive and efficient data acquisition through a user-friendly interface on a tablet device, guiding physicians with tailored queries based on patient-specific data and enabling natural language text generation for clinical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional EMR systems with multiple fields and transcription processes are used, then clinical information can be captured systematically, but the physician's attention is diverted from patient interaction and the process becomes time-consuming
Solution Approach 1:
The system performs automatic speech-to-text transcription and populates EMR fields without requiring manual typing or complex navigation by the physician. The transcription service automatically processes spoken clinical information and structures it into appropriate medical record fields, allowing the physician to simply speak rather than manually enter data.
Solution Approach 2:
A transcription service acts as an intermediary between the physician's spoken words and the EMR system. This intermediary automatically converts speech to text, interprets clinical information, and populates the appropriate fields, eliminating the need for the physician to directly interact with complex EMR interfaces.
2Loss of information
If comprehensive clinical data fields are required in EMR systems, then complete patient information can be recorded, but information overload occurs making it difficult to capture clinical notes efficiently
Solution Approach 1:
The system extracts only the essential clinical information from the physician's spoken narrative and populates only the necessary EMR fields. Rather than requiring the physician to fill out every possible field, the transcription service intelligently identifies and extracts relevant clinical data points and places them in the appropriate locations.
Solution Approach 2:
The system pre-structures the EMR template with appropriate fields and formatting before the physician begins speaking. The transcription service is ready to immediately populate these pre-prepared fields as speech is converted to text, eliminating the need for the physician to manually organize information into multiple fields during the consultation.
3Manufacturing precision
If physicians manually type and navigate EMR systems during consultations, then detailed clinical records can be created, but the process requires new technical skills and reduces time for patient engagement
Solution Approach 1:
The system replaces the mechanical process of manual typing and mouse navigation with an automated speech-to-text transcription process. The physician's voice serves as the input mechanism, and the transcription service automatically handles the conversion and formatting, eliminating the need for manual keyboard or mouse interaction during the patient consultation.
Data Source
AI summary
A dynamic decision forest may be used to guide clinical or other decision queries such that multiple decision trees are followed in sequence and jumps occur between decision trees based on previously received query responses and the system's determination of what useful data has not been gathered. For example, rather than storing data concerning all possible paths between various decision trees, a system using the dynamic decision forest may determine at the time of use that a jump should occur from one decision tree to another decision tree despite the system not being pre-populated with data representative of a relationship between the source and destination of the jump. A clinical note may be created based on the path followed through the dynamic decision forest, and iterative verification may be used in order to improve the likelihood that the correct path is being followed.