AI Two-Way Questionnaire Generation for Medical Communication
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Solution Overview
Problem
Effective communication between patients and medical services providers is hindered by limited interaction time and low health literacy, with only 12% of Americans having proficiency in healthcare literacy, leading to challenges in identifying health problems and appropriate treatments.
Innovation Solution
A computer system and method using machine learning to generate two-way documents, one for patients and one for medical services providers, based on patient and provider data, to facilitate effective questioning and education on health conditions, improving health literacy and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional face-to-face medical consultations are used, then direct communication between patient and provider is maintained, but interaction time is limited and communication effectiveness is reduced due to low health literacy
Solution Approach 1:
The system performs preliminary actions by generating customized questionnaires and educational materials before the medical consultation. These pre-prepared documents allow patients to prepare their questions and understand their conditions in advance, making the limited face-to-face interaction time more productive and focused on critical decision-making.
Solution Approach 2:
The patent introduces an AI-generated document system as an intermediary between patient and provider. These documents serve as a mediator that facilitates communication by structuring information, guiding discussions, and ensuring that key topics are covered within the limited consultation time, thereby improving communication effectiveness without requiring more time.
2Loss of information
If comprehensive medical information is provided to patients, then health literacy and understanding improve, but the complexity and length of information increases making it harder to process
Solution Approach 1:
The system applies local quality by customizing the information content specifically for each patient based on their health condition, education level, and preferences. Rather than providing generic comprehensive information to all patients, the AI generates tailored documents that include only the relevant information needed by each individual patient, reducing complexity while maintaining completeness of essential information.
Solution Approach 2:
The patent changes parameters of information presentation by adjusting language complexity, document structure, and content focus based on patient characteristics. The AI system dynamically modifies information parameters such as reading level, detail depth, and organizational structure to match the patient's health literacy level, ensuring comprehensive information delivery without overwhelming complexity.
3Ease of operation
If generic medical questionnaires are used for all patients, then standardization and ease of use are maintained, but adaptability to individual patient needs and conditions is reduced
Solution Approach 1:
The system transitions from static generic questionnaires to dynamic customized documents. The AI generates questionnaires that adapt in real-time based on patient inputs, health conditions, and consultation context. This dynamic approach maintains ease of use through automated generation while achieving high adaptability to individual patient needs, combining the benefits of both standardized and personalized approaches.
Solution Approach 2:
The patent creates a universal system that serves multiple functions: it generates customized questionnaires, provides patient education, guides provider-patient discussions, and adapts to different patient populations. This multi-functional document system replaces multiple separate tools (generic questionnaires, educational materials, discussion guides) with a single adaptive platform that maintains ease of operation while providing patient-specific customization.
Data Source
AI summary
A method, a computer system, and a computer program product for two-way document generation for medical communication are provided. Patient data from a patient may be received. Medical services provider data from a medical services provider may be received. The patient data and the medical services provider data may be run through a machine learning model so that the machine learning model may produce a first document and a second document. The first document may be presented to the patient. The second document may be presented to the medical services provider.