Medical Document Generation via Priority-Based Derivation Method Selection
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Solution Overview
Problem
Current medical document creation systems lack efficient methods for deriving and recording patient information across multiple record items, leading to inconsistencies and increased workload due to reliance on single derivation methods and varying medical worker interpretations.
Innovation Solution
An information processing apparatus that acquires designation information to select multiple derivation methods based on priority order for deriving record information from patient data, utilizing diversion, classification rules, and trained models to generate medical document data, while accommodating item name discrepancies through exchange data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single derivation method is used to derive record information, then the system structure is simple, but the reliability and consistency of record information derivation deteriorates due to reliance on single method and worker interpretations
Solution Approach 1:
The derivation process is segmented into multiple independent derivation methods (first derivation method, second derivation method, third derivation method) that can be selectively applied. Each method handles specific aspects of record information derivation, allowing the system to divide the complex derivation task into manageable segments with different approaches, thereby improving reliability without overwhelming complexity
Solution Approach 2:
The system dynamically selects which derivation method to apply based on the specific record item and available patient information. The processor determines the appropriate derivation method flexibly, making the system adaptive rather than rigid. This dynamic approach allows the system to optimize for each specific derivation task while maintaining overall simplicity through on-demand method selection
2Reliability
If multiple derivation methods are implemented, then the reliability and consistency of record information derivation is improved, but the device complexity increases
Solution Approach 1:
The processor serves as a universal controller that can execute multiple derivation methods (first derivation method, second derivation method, third derivation method) through a single integrated architecture. This multi-functional design allows one processor to handle diverse derivation tasks without requiring separate dedicated systems for each method, thereby improving reliability while controlling complexity through shared infrastructure
Solution Approach 2:
The system prepares multiple derivation methods in advance with predetermined priority orders and selection criteria. The derivation methods are pre-configured with their respective rules and parameters, so when derivation is needed, the system can quickly select and apply the appropriate method without complex real-time decision-making. This preliminary preparation reduces operational complexity while maintaining high reliability
3Productivity
If derivation methods are selected based on priority order, then the productivity of document creation is improved, but the adaptability to handle diverse derivation scenarios decreases
Solution Approach 1:
The system employs dynamic priority ordering where the processor can adjust which derivation method is applied based on the specific record item, available patient information, and derivation success status. The priority order is not fixed but can be dynamically modified during the derivation process, allowing the system to maintain high productivity through efficient method selection while adapting to diverse scenarios through flexible reordering
Solution Approach 2:
The system incorporates feedback mechanisms where the processor monitors the results of derivation attempts and adjusts the selection of derivation methods accordingly. If a high-priority derivation method fails to produce valid record information, the system can switch to alternative methods based on feedback from the failed attempt. This feedback loop maintains productivity by avoiding repeated failures while ensuring adaptability through learned adjustments
4Adaptability or versatility
If the system handles incomplete patient data, then the adaptability is improved, but the measurement precision of derived record information deteriorates
Solution Approach 1:
The system applies partial derivation methods that can operate with incomplete patient information available. Rather than requiring complete data for all derivation methods, the system can apply methods that utilize only the available partial information to derive record information. This partial action approach maintains adaptability to incomplete data scenarios while accepting that precision may vary based on the completeness of input data
Solution Approach 2:
The system prepares multiple derivation methods with different data requirements in advance, creating a cushion against incomplete patient data. When data is incomplete, the system can switch to derivation methods that are designed to handle partial information, thereby cushioning against the impact of missing data. This preliminary preparation ensures adaptability to various data completeness levels while maintaining reasonable precision through appropriate method selection
Data Source
AI summary
An information processing apparatus includes at least one processor. The processor is configured to: acquire designation information for designating a plurality of derivation methods to derive record information to be recorded in at least one record item related to a patient; derive the record information by applying a derivation method, which is selected according to a preset priority order from among the plurality of derivation methods designated through the designation information, based on patient information related to the patient, for the record item; and generate medical document data in which the derived record information is recorded in the record item.