Learning Filter for Healthcare Data Disorder Detection
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Solution Overview
Problem
Current systems face challenges in diagnosing rare disorders due to complex and secure medical data storage methods, which hinder the identification of patients who may be subjects of interest for observational research.
Innovation Solution
A system and method that utilize a learning filter to analyze patient data, comparing historical and current laboratory test results to a disorder profile, with deidentified data stored in a secure registry to improve the accuracy of identifying potential disorder cases and update criteria for future patient assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex and secure medical data storage methods (hash tables, encryption) are used, then data security is improved, but the ability to identify patients for observational research deteriorates
Solution Approach 1:
The system segments data into deidentified components stored in registries, separating patient identifiers from medical data. Hash tables divide the data structure into manageable segments that can be securely stored and efficiently queried without exposing patient identities, resolving the contradiction between security and identifiability.
Solution Approach 2:
Deidentification techniques act as an intermediary layer between secure storage and research analysis. The system introduces intermediate data structures (deidentified datasets, hash-mapped records) that enable research queries without directly exposing patient identifiers, thus maintaining security while enabling identification for research purposes.
2Reliability
If deidentified data is stored in secure registries, then patient privacy is protected, but the accuracy of disorder detection deteriorates
Solution Approach 1:
The system implements feedback loops where detection results and additional patient data continuously refine and update the filter criteria and disorder profiles. This iterative process improves detection accuracy over time while maintaining deidentified data storage, as the feedback mechanism operates on aggregated patterns rather than individual patient identifiers.
Solution Approach 2:
The system performs preliminary deidentification and data aggregation before analysis, preparing datasets in advance with embedded detection criteria. By pre-processing data into analyzable formats with embedded filters and profiles, the system maintains privacy protection while ensuring detection accuracy is not compromised during the actual analysis phase.
3Measurement precision
If a learning filter is applied to historical data, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The filter is designed as a dynamic, adaptive system that automatically learns and updates from new data without requiring manual reconfiguration. The filter criteria and disorder profiles evolve automatically through feedback from detection results, reducing the need for complex manual processing and maintenance while continuously improving accuracy.
Solution Approach 2:
The learning filter operates autonomously, self-updating its criteria and profiles based on incoming data and detection outcomes. This self-service capability reduces the burden of manual data processing complexity, as the system automatically refines its own detection algorithms without requiring extensive human intervention or complex external processing infrastructure.
Data Source
AI summary
A system includes a learning filter for deidentified healthcare data. The system provides self-improving data filtering of data for the detection of disorders for engaging patients in observational research to gather additional data. A processor performs operations including obtaining patient information about a patient and current order data. The current order data indicates an order for a laboratory test for the patient and obtaining, from a database, historical data indicating a previous laboratory test for the patient and a result of the previous laboratory test. The operations further include determining that the patient is a subject of interest for a disorder by applying the filter to historical data to produce filtered data and comparing the filtered data to a profile associated with the disorder. Additional laboratory tests can be performed, after which a hash-to-patient identifier (PID) mapping database and the filter can be updated.