Medical Service Support Apparatus for Dynamic Examination Interval Calculation
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Solution Overview
Problem
Current medical service support systems face challenges in determining optimal examination intervals for diseases like cancer, as they struggle to balance frequent examinations with financial constraints, and existing systems lack efficient methods for analyzing and recommending examination schedules based on historical data.
Innovation Solution
A medical service support apparatus with a recording unit, search unit, and output unit that extracts and analyzes examination data to determine a recommended examination interval by classifying data chronologically and setting reference dates, allowing for the extraction of time-series data to calculate optimal examination intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent examinations are performed for early detection, then detection reliability is improved, but medical expenses increase
Solution Approach 1:
The system dynamically changes the examination interval parameter based on individual patient risk factors, disease progression patterns, and historical data. By adjusting the time parameter adaptively rather than using fixed intervals, the system achieves optimal balance between early detection reliability and cost efficiency for each patient
Solution Approach 2:
The system implements feedback loops where examination results, patient responses, and outcome data are continuously fed back into the decision-making algorithm. This feedback mechanism allows the system to learn from past examinations and optimize future scheduling, improving detection reliability while avoiding unnecessary examinations that would increase costs
2Quantity of substance
If examination intervals are extended to reduce expenses, then medical expenses decrease, but early detection capability deteriorates
Solution Approach 1:
The system adjusts examination interval parameters dynamically based on patient-specific risk profiles, disease characteristics, and temporal patterns in the data. High-risk patients receive more frequent examinations while low-risk patients receive less frequent ones, optimizing both cost efficiency and detection capability
Solution Approach 2:
The system applies different examination frequency qualities to different patient groups or time periods based on local conditions and risk assessments. Rather than a uniform approach, each patient receives a customized examination schedule tailored to their specific needs, ensuring adequate detection capability where required while reducing unnecessary examinations elsewhere
3Measurement precision
If manual analysis of examination data is performed, then data accuracy is maintained, but analysis time increases
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational algorithms that process examination data. These algorithms maintain high accuracy through sophisticated data processing while dramatically reducing the time required for analysis compared to manual methods
Solution Approach 2:
The system creates digital copies and models of examination data that can be processed, analyzed, and simulated without handling original data. This allows rapid iterative analysis and validation while preserving data accuracy, eliminating the time-consuming nature of manual analysis
Data Source
AI summary
A recording unit holds a plurality of examination data including the examination date of a performed examination. A search unit extracts examination data matching a set condition, of the plurality of examination data held in the recording unit. An output unit outputs a search result by the search unit. A first narrowing unit extracts examination data in which the examination date is included within a designated first period. A reference data determination unit classifies the examination data extracted by the first narrowing unit by an examinee and determines, for every examinee, one piece of the examination data to be a reference in accordance with a predetermined rule. A second narrowing unit sets, for every examinee, a second period designated in at least one of the past direction and the future direction starting from a reference date that is the examination date of the examination data determined by the reference data determination unit and extracts examination data in which the examination date is included within the second period.