Clinical Decision Support Device for Efficient Patient Preference Estimation
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Solution Overview
Problem
Current clinical decision support systems face challenges in efficiently acquiring and understanding patient preferences, as they require significant time and effort to uncover, and often cannot account for preferences of varying importance, leading to inefficient communication between patients and medical staff.
Innovation Solution
A clinical decision support device with processing circuitry that acquires patient attribute information and first preference parameters, estimates second preference parameters using an estimation function, determines preferable selections, and displays them in association with each other, facilitating efficient preference acquisition and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical staff conduct detailed conversations to draw out patient preferences, then the accuracy of preference identification is improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple preference categories and their associated parameters before the actual preference acquisition process. This allows the estimation function to quickly determine patient preferences based on pre-established frameworks rather than conducting extensive conversations from scratch, thus reducing time while maintaining accuracy.
Solution Approach 2:
The estimation function acts as an intermediary between the raw patient data and the final preference determination. It processes and interprets patient information through predefined preference categories, translating complex conversation data into structured preference parameters more efficiently than direct manual analysis would require.
2Quantity of substance
If medical staff ask about all patient preferences, then the completeness of preference information is improved, but the efficiency of communication decreases
Solution Approach 1:
The system segments preference information into distinct predefined categories (e.g., treatment outcomes, side effects, cost, time). This segmentation allows the estimation function to systematically evaluate each category independently based on patient responses, ensuring comprehensive preference coverage while maintaining efficient communication by targeting specific categories rather than overwhelming patients with general questions.
Solution Approach 2:
The system employs partial action by focusing estimation on the most relevant preference categories based on patient characteristics and clinical context. Rather than equally weighting all possible preferences, the system selectively estimates preferences that are most pertinent to the patient's situation, improving communication efficiency while maintaining sufficient completeness for clinical decision-making.
3Adaptability or versatility
If the system estimates all preference parameters, then the comprehensiveness of patient preference understanding is improved, but the computational complexity increases
Solution Approach 1:
The estimation function applies local quality by tailoring the estimation approach to specific preference categories based on patient characteristics and clinical context. Different preference categories receive different levels of estimation intensity and computational resources depending on their relevance to the individual patient, achieving comprehensive understanding where needed while reducing complexity in less critical areas.
Data Source
AI summary
A clinical decision support device of an embodiment includes processing circuitry. The processing circuitry acquires at least one of attribute information of a patient and first parameters regarding a first preference category for the patient. The processing circuitry estimates second parameters of the patient with respect to a second preference category on the basis of at least one of the attribute information and the first preference category. The processing circuitry determines preferable selections of the patient with respect to a predetermined preference category on the basis of the second parameters. The processing circuitry displays the preferable selections and the second parameters in association with each other.