Infectious Disease Notification Algorithm Using EMR Data
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Solution Overview
Problem
Current hospital workflows often result in delayed notification of infectious disease specialists, leading to delayed treatment and worsened patient outcomes due to the lack of an automated system for early notification of potential infectious diseases.
Innovation Solution
A system and method utilizing a machine learning monitoring algorithm trained on electronic medical record datasets to predict the probability of infectious diseases, comparing calculated probabilities with a threshold value, and automatically notifying infectious disease specialists when the probability exceeds this threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual notification by attending physician is used, then system complexity is low, but notification time is delayed and reliability is poor
Solution Approach 1:
The patent replaces the manual mechanical notification system with an automated electronic monitoring and notification system. The system continuously monitors patient data from electronic medical records, automatically calculates infection risk probabilities, and sends notifications to infectious disease specialists without human intervention, thereby improving reliability while managing complexity through automation.
Solution Approach 2:
The patent introduces an intermediary automated notification system that acts as a mediator between patient data and infectious disease specialists. This intermediary system processes patient information, determines risk levels, and triggers appropriate notifications, eliminating the need for direct manual assessment by attending physicians while maintaining system reliability.
2Speed
If automated monitoring system is implemented, then notification speed is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically monitoring patient data, calculating infection risks, and generating notifications without requiring external intervention. The automated monitoring algorithm continuously assesses patient conditions and triggers notifications based on predefined criteria, achieving rapid notification while managing complexity through self-contained automation.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring and analyzing patient data before infections fully develop. The automated algorithm detects early signs of infection risk and triggers notifications in advance, enabling early intervention while maintaining manageable system complexity through proactive monitoring.
3Measurement precision
If continuous monitoring is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system implements periodic monitoring at strategically determined intervals rather than continuous real-time analysis. The automated algorithm assesses patient data at intervals based on clinical relevance and risk factors, maintaining high detection precision while reducing computational energy consumption by avoiding unnecessary continuous processing.
Data Source
AI summary
A system is for infectious disease notification. The system includes at least one processor, configured to use a machine learning monitoring algorithm, trained on a large number of EMR datasets of patients, to calculate a probability for an infectious disease from a provided EMR dataset and compare the probability of the provided EMR dataset calculated with a known value. In training of the monitoring algorithm, the value represents whether there was an onset of an infectious disease or not and the monitoring algorithm is designed to adjust parameters of the monitoring algorithm. And in evaluating a notification, the value is a threshold value and the system is designed to output a notification upon the probability being greater than the threshold value.