Automated Patient Data Matching via Temporal Standardization
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Solution Overview
Problem
Current methods for matching unassigned patient data from various sources are laborious, prone to human error, and lack automation, making it challenging to create a complete and reliable patient care record.
Innovation Solution
An automated tool employing temporal matching algorithms, statistical methods, and machine learning to standardize and match unassigned patient data across different systems, providing a probabilistic estimate of the match quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to match patient data from various sources, then flexibility and adaptability are maintained, but the process becomes laborious, time-consuming, and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical process of data matching with an automated computer-based system that uses algorithms and machine learning models to match unassigned patient data with patient records, eliminating human labor and significantly reducing time while improving accuracy
Solution Approach 2:
The system enables self-service by automatically performing data matching without human intervention, where the computer system independently collects, processes, and matches data from multiple sources using automated algorithms, freeing clinicians from this laborious task
2Productivity
If automated tools are used to match unassigned patient data, then efficiency and speed are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex data matching system into distinct functional modules including data collection components, processing components, and matching components, each handling specific tasks such as collecting data from different sources, cleaning and standardizing data, and performing the actual matching operations
Solution Approach 2:
The patent introduces intermediary components such as data standardization layers and processing buffers that mediate between diverse data sources and the matching algorithm, simplifying the overall system architecture by providing uniform interfaces and handling data transformation tasks
3Loss of information
If data from multiple sources is collected and matched, then a complete patient care picture is achieved, but data heterogeneity and integration difficulty increase
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple types of data sources (EMRs, machine logs, sensor data) through a common processing pipeline, allowing the system to universally process and integrate diverse data formats using standardized procedures
Solution Approach 2:
The patent transforms data from different sources by changing their parameters and formats to a standardized structure, including converting timestamps to universal time zones, normalizing data formats, and transforming heterogeneous data into a common schema that facilitates efficient matching and integration
Data Source
AI summary
Systems, apparatuses, and methods provide for matching unassigned patient data to individual patients. For example, such operations include collecting data from a plurality of data sources in a plurality of formats. Data information Machine time stamps are converted from collected data to universal time zone data time stamps. A same patient is matched to the collected data based on the universal time zone data time stamp. A quality estimate of the match is quantified. The match and quality estimate of the match are transferred to a user interface.