AI Medical History Interview Evaluation Using LLM-Based Cultural Analysis
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Solution Overview
Problem
Current medical history evaluation interviews face challenges due to the limited availability of trainers, which leads to insufficient evaluation of trainees, potential HIPPA issues with additional trainers in the room, and inherent biases in manual transcript reviews, lacking cultural awareness.
Innovation Solution
An artificially intelligent medical history evaluation system that transcribes and analyzes speech during interviews, providing real-time feedback on politeness, empathy, and jargon use, using a large language model (LLM) to assess trainees' performance without human trainers, adaptable to cultural norms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trainer reviews a transcript or recording of the trainee's interview with the patient, then the evaluation can be conducted, but the trainer is limited to information in the transcript, may exhibit inherent bias, and may be unaware of cultural norms associated with the interviewee
Solution Approach 1:
The patent introduces an AI-based evaluation system as an intermediary between the trainee's interview performance and the evaluation process. This AI system analyzes audio and video data directly, serving as a mediator that overcomes the limitations of human trainers who rely on transcripts and may lack cultural awareness. The AI intermediary processes the raw interview data with cultural sensitivity algorithms while providing objective, bias-free evaluation.
Solution Approach 2:
The patent replaces the mechanical system of human trainers reviewing transcripts with an AI-based automated evaluation system. This substitution eliminates the limitations of manual review including bias, cultural awareness gaps, and reliance on incomplete transcript information. The AI system directly processes multimedia interview data to provide comprehensive and accurate evaluations.
2Measurement precision
If additional trainers are placed in the room during the interview to evaluate trainees, then more comprehensive evaluation is possible, but HIPPA issues arise and the number of available trainers is limited
Solution Approach 1:
The AI-based evaluation system acts as an intermediary that captures and analyzes interview data without requiring additional human trainers in the room. This intermediary approach maintains HIPPA compliance by using secure, automated processing of interview recordings while still enabling comprehensive evaluation of trainee performance across multiple dimensions including communication skills, empathy, and cultural awareness.
Solution Approach 2:
The evaluation system performs self-service by automatically capturing, transcribing, analyzing, and evaluating interview performance without requiring human trainers to be physically present. The system independently processes the interview data, generates evaluations, and provides feedback, eliminating the need for additional trainers while maintaining comprehensive assessment capabilities and ensuring HIPPA compliance through automated secure processing.
3Measurement precision
If human trainers review every medical interview, then thorough evaluation is achieved, but the limited number of trainers prevents universal evaluation of all interviews
Solution Approach 1:
The AI-based evaluation system provides self-service by automatically evaluating all medical interviews without requiring human trainers. The system independently processes interview recordings, analyzes performance across multiple criteria, generates detailed evaluations, and provides feedback to trainees. This self-service capability enables universal evaluation of all interviews while maintaining thorough assessment standards, overcoming the productivity limitations of manual review by limited trainer resources.
Solution Approach 2:
The patent transforms the evaluation process by changing the fundamental parameter of who performs the evaluation from human trainers to an AI system. This parameter change enables the evaluation throughput to scale from being limited by the number of trainers to being capable of processing all interviews universally, while maintaining or improving evaluation thoroughness through the AI's ability to consistently apply comprehensive evaluation criteria across all cases.
Data Source
AI summary
An evaluation device receives captured data of a medical history evaluation interview between a patient and a medical provider. The evaluation device transcribes audio of the captured data into transcribed text and segments the transcribed text into segmented lines according to a speaker of the transcribed text within the audio. The evaluation device generates a plurality of prompts, each prompt corresponding to one of a plurality of interview analysis variables, to control a large language model (LLM) to analyze each of the segmented lines in context of the transcribed text. The evaluation device transmits the plurality of prompts to the LLM and receives LLM responses from the LLM for each of the plurality of prompts. The evaluation device analyzes the LLM responses with respect to a scoring rubric and generates a detail report defining performance of the medical provider during interaction between the patient and the medical provider.