Dynamic Electronic File Generation for Health Data Analysis
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Solution Overview
Problem
Current systems face challenges in efficiently analyzing and structuring longitudinal health information for healthcare professionals, leading to complex data processing and excessive memory usage due to inclusion of unnecessary data in static electronic files.
Innovation Solution
A method and system for generating dynamically structured electronic files that match and combine healthcare professional and health information, deidentify private data, and allow users to specify file type and destination, reducing data redundancy and processing power by only including requested insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static electronic files include all available health information for comprehensive analysis, then data completeness is improved, but memory usage and processing complexity increase excessively
Solution Approach 1:
The system extracts only the specific health information and statistical values that are requested by the user for the HCP file, rather than including all available data. The file generation process selectively pulls relevant data from the longitudinal health information database based on user specifications, eliminating unnecessary data inclusion.
Solution Approach 2:
The health information is segmented into discrete, selectable components that can be individually chosen for inclusion in the HCP file. Users can specify which statistical values and types of information to include, creating modular file content that balances completeness with efficiency.
2Loss of information
If static electronic files include all available health information for comprehensive analysis, then data completeness is improved, but processing complexity increases excessively
Solution Approach 1:
The system transitions from static file generation to dynamic file generation, where the HCP file structure and content are determined at runtime based on user requests. The file generation process adapts to specific user needs by dynamically selecting which statistical values and health information to include, rather than using a fixed comprehensive structure.
Solution Approach 2:
The system performs partial action by generating only the portion of the health information file that is actually needed by the user, rather than processing and including all available data. This selective approach reduces processing complexity while maintaining data completeness for the requested information.
3Reliability
If deidentified private health information is processed to ensure privacy security, then data security is improved, but data processing time increases
Solution Approach 1:
The deidentification of private health information is performed in advance, before the HCP file generation process. By pre-processing the data to remove or mask identifying information, the system ensures data security is established early, and subsequent file generation operations work with already-deidentified data, reducing overall processing time.
Data Source
AI summary
A method for generating an electronic file associated with health care professionals (HCPs). The method includes receiving health information from a covered entity computing system and a file generation request from a user computing device. The method further includes matching HCP information with the health information and determining one or more statistical values based on at least one of the matched HCP information or the matched health information. The method further includes generating the electronic file including the one or more statistical values and at least a portion of the matched HCP information and providing the file to the destination address of the file generation request. The electronic file is generated as the file type identified by the file generation request, and is structured based on at least one of the file generation request or the file destination.