Consultation Transcript Scoring for Patient Communication Feedback
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Solution Overview
Problem
Patients struggle to remember key details from healthcare consultations, especially those related to treatment decisions for complex conditions like cancer, and healthcare providers lack feedback on their communication quality, leading to suboptimal patient understanding and satisfaction.
Innovation Solution
A method using random forest models to analyze transcripts of consultations, extracting word groups related to specific topics like life expectancy or side effects, and scoring the consultation based on the detail provided.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consultations are recorded and analyzed to extract detailed information for patient understanding, then patient knowledge and satisfaction improve, but the complexity of processing and analyzing consultation data increases
Solution Approach 1:
The consultation transcript is divided into multiple word groups, each associated with specific topics (e.g., life expectancy, prognosis, side effects, treatment options). This segmentation allows the system to process and analyze specific aspects of the consultation separately, making the overall complex task manageable and systematic.
Solution Approach 2:
A random forest model serves as an intermediary between the raw consultation transcript and the extracted information. The model automatically identifies and extracts relevant word groups without requiring manual analysis, bridging the gap between unstructured speech and structured information while reducing system complexity.
2Loss of information
If healthcare providers provide detailed patient-specific information during consultations, then patient understanding improves, but the difficulty of obtaining feedback on communication quality increases
Solution Approach 1:
The system automatically generates feedback on consultation quality by analyzing whether key information about treatment tradeoffs was communicated. The random forest model evaluates the presence and quality of information delivery, providing actionable feedback to healthcare providers to improve their communication skills without requiring manual assessment.
Solution Approach 2:
The consultation analysis system performs self-assessment by automatically evaluating its own extraction results. The system determines whether required information was present in the consultation and generates quality scores without external intervention, enabling automated improvement tracking for healthcare providers.
3Measurement precision
If consultations are analyzed to extract specific topic-related word groups, then the precision of information extraction improves, but the time required to process consultations increases
Solution Approach 1:
The system is pre-trained with a random forest model that has learned to identify patterns associated with key information topics. This preliminary training allows the system to quickly and accurately extract relevant word groups during actual consultations without requiring time-consuming manual analysis or complex real-time processing.
Data Source
AI summary
A method of extracting information from a consultation between a healthcare provider and a patient and subsequently scoring the consultation comprises receiving data associated with a transcript of the consultation between the healthcare provider and the patient. The method further comprises analyzing the data to extract a plurality of word groups, each word group including or more words spoken by the healthcare provider during the consultation. The method further comprises determining whether each respective word group of the plurality of word groups is associated with a predetermined topic. The method may further comprise assigning a score to at least one word group of the plurality of word groups that is determined to be associated with the predetermined topic, the score indicating a level of detail of the at least one word group.