ePCR and Medical Device Record Matching for Accurate Care Handoffs
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Solution Overview
Problem
Existing systems face challenges in efficiently and accurately linking electronic patient care records (ePCR) with medical device case files during patient encounters, particularly in emergency situations, leading to inefficiencies and potential errors in patient care transitions.
Innovation Solution
A system and method for integrating medical device case files with electronic patient care records (ePCR) using a processor to generate search criteria based on matching parameters, automate the identification of relevant case files, and create an integrated patient encounter record, which can be confirmed and stored in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual linking of ePCR and medical device case files is performed, then flexibility and customization are maintained, but time consumption and error rates increase
Solution Approach 1:
The system enables automatic self-linking of ePCR and medical device case files through parameter matching algorithms. The processor automatically compares parameters such as patient ID, encounter time, and device identifiers to establish connections without requiring manual intervention from caregivers, thereby dramatically improving documentation efficiency while reducing time consumption.
Solution Approach 2:
The system performs preliminary parameter extraction and normalization from both ePCR and medical device case files before the actual linking process. By pre-processing and structuring the data with standardized parameter formats, the system prepares the data in advance for rapid automatic matching, reducing the time required during the actual integration phase.
2Measurement precision
If manual linking of ePCR and medical device case files is performed, then complex matching scenarios can be handled, but accuracy and error rates deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the processor evaluates multiple potential matches based on parameter similarity scores. When ambiguous matches are detected, the system can request additional confirming parameters or present options for verification. This feedback loop ensures high linking accuracy while maintaining reliability in patient care transitions by preventing incorrect associations.
Solution Approach 2:
The system transforms various parameter formats into a standardized representation for comparison. By normalizing parameters such as time formats, identifier structures, and data types, the system enables precise matching across different data sources. This parameter transformation approach handles complex matching scenarios while maintaining high accuracy through consistent comparison criteria.
3Measurement precision
If comprehensive parameter matching is performed for integration, then integration accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the integration process into distinct modular stages: parameter extraction, parameter normalization, parameter matching, and result validation. Each stage handles specific aspects of the integration independently, allowing comprehensive parameter matching to be performed through a series of manageable steps rather than a single complex operation, thereby maintaining accuracy while controlling system complexity.
Solution Approach 2:
The system employs a universal parameter matching framework that can handle multiple parameter types and matching scenarios through a single integrated process. The processor is designed to work with various parameter formats and matching criteria using the same core matching algorithms, reducing system complexity by avoiding the need for separate specialized processes for different integration scenarios.
Data Source
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AI summary
A system for integrating medical device case files with corresponding electronic patient care records (ePCRs) is provided. The system includes a database storing a plurality of medical device case files and at least one processor. The at least one processor receives, from a patient charting device, charting data documented in an ePCR for an encounter with a particular patient, generates a search criterion based at least in part on the charting data, the search criterion including a predetermined relationship between at least one first parameter from the charting data and at least one second parameter associated with a medical device case file, identifies the medical device case file based on the generated search criterion, and generates an integrated patient encounter record including at least a portion of medical device data from the medical device case file and the charting data.