Automated Patient Data Anomaly Detection System
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Solution Overview
Problem
Current data validation processes in medical research rely heavily on manual inspection, which is time-consuming and prone to errors, especially when dealing with large datasets, leading to potential anomalies being missed or misinterpreted, affecting the accuracy and reliability of research conclusions.
Innovation Solution
A computer-implemented system that automatically classifies patient parameter values by analyzing current and historical data to detect potential anomalies, using algorithms and machine learning models to identify deviations from normal variations, and presents these findings to users through a graphical interface, allowing for user confirmation and model improvement based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to validate patient data, then human judgment can identify potential anomalies, but the process is extremely time-consuming and may miss anomalies due to the volume of data
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-based system that uses algorithms to detect anomalies in patient data. The system automatically compares current parameter values against historical data and statistical thresholds, eliminating the need for human inspectors to manually review thousands of data points while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-service anomaly detection by automatically validating patient data without requiring human intervention. The automated system performs validation tests, identifies anomalies, and generates reports independently, allowing the data validation process to serve itself without consuming human time resources.
2Reliability
If comprehensive data validation is performed on all parameters, then anomaly detection coverage is improved, but the complexity and time required for review increases significantly
Solution Approach 1:
The patent segments the validation process into distinct automated components: data collection, statistical threshold comparison, anomaly detection algorithms, and report generation. Each parameter is independently evaluated against predefined criteria, allowing comprehensive validation of all parameters without requiring complex manual coordination across different validation stages.
3Loss of information
If human inspectors review all data parameters, then complete anomaly detection is possible, but the process becomes infeasible due to the volume of data and parameters
Solution Approach 1:
The patent replaces human inspection mechanics with automated computational systems that can process and analyze all data parameters simultaneously. The computer-based system evaluates every parameter against historical data and statistical thresholds, ensuring complete anomaly detection coverage while processing the entire dataset in a fraction of the time required for manual review.
Data Source
AI summary
A system for classifying patient parameter values may include at least one processor programmed to access first information associated with a plurality of patients, the first information including a plurality of patient parameters associated with the plurality of patients, the first information being accessed electronically via a database; determine a first value associated with a patient parameter of at least one of the plurality of patients; analyze second information associated with at least one patient to determine a second value of the patient parameter; detect, based on analysis of at least the first value and the second value, a potential anomaly in the second value; and cause a graphical user interface of a computing device to display at least one graphical element indicating the potential anomaly.