Post-Discharge Monitoring With Time-Dependent Alert Thresholds
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Solution Overview
Problem
There is a risk that a subject's condition may deteriorate or not improve at an expected rate after discharge, leading to re-entry into clinical care, and existing monitoring methods lack adaptive alert thresholds that account for expected recovery over time.
Innovation Solution
A computer-implemented method sets time-dependent alert thresholds for condition-responsive measures post-discharge, which decrease as a function of time since discharge, facilitating early discharge and monitoring deviations from expected recovery trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static alert thresholds are used for post-discharge monitoring, then the monitoring system is simple to operate, but it cannot adapt to expected recovery trends over time, leading to increased alert fatigue and reduced detection precision
Solution Approach 1:
The patent implements dynamic alert thresholds that automatically adjust over time based on expected recovery trajectories. The system transitions from static thresholds to time-varying thresholds that decrease as the patient recovers, allowing the monitoring system to adapt to changing patient conditions without requiring manual intervention or complex configuration.
Solution Approach 2:
The patent changes the parameter of alert thresholds from fixed values to time-dependent values. The threshold parameter evolves over time according to a predefined recovery model, enabling the system to maintain high detection precision for deteriorations while adapting to expected improvements, thereby reducing false alerts without increasing operational complexity.
2Ease of operation
If fixed alert thresholds are maintained throughout the post-discharge phase, then the monitoring rules are easy to implement, but they generate excessive false alerts as patient condition naturally improves, increasing alert fatigue
Solution Approach 1:
The system employs dynamic threshold adjustment where alert thresholds automatically decrease over the post-discharge period to match expected patient recovery. This dynamic behavior reduces false alerts generated from natural improvement while maintaining ease of implementation through automated adjustment without complex manual configuration.
Solution Approach 2:
The patent incorporates feedback mechanisms where the monitoring system continuously compares patient measurements against time-varying thresholds and adjusts alert generation accordingly. This feedback loop ensures that as patients recover, the thresholds adapt to reflect expected improvements, maintaining high alert reliability while reducing false positives.
3Reliability
If time-dependent alert thresholds are implemented to track expected recovery, then alert reliability improves by reducing false positives, but the monitoring system complexity increases
Solution Approach 1:
The patent implements dynamic thresholds that automatically adjust over time based on recovery models, improving alert reliability by reducing false positives from natural patient improvement. The system manages complexity through automated threshold adjustment algorithms that require minimal configuration and run independently once deployed.
Solution Approach 2:
The monitoring system performs self-adjustment of alert thresholds based on predefined recovery trajectories without requiring manual intervention. The system serves itself by automatically updating thresholds over time, which improves reliability while keeping operational complexity low through automation rather than manual management.
4Productivity
If monitoring continues with static thresholds after discharge, then the system requires minimal changes to existing infrastructure, but it cannot facilitate early discharge by detecting deviations from recovery trends
Solution Approach 1:
The patent implements dynamic thresholds that enable early discharge detection by tracking deviations from expected recovery trajectories. The system facilitates higher productivity through earlier safe discharge while managing complexity through automated threshold adjustment that integrates with existing monitoring infrastructure.
Solution Approach 2:
The system prepares for early discharge detection by pre-defining recovery trajectories and threshold adjustment rules before patient discharge. This preliminary configuration enables the system to automatically detect deviations from expected recovery and facilitate early discharge decisions without requiring complex real-time analysis or manual intervention during the monitoring period.
Data Source
AI summary
A mechanism for setting one or more thresholds for post-discharge monitoring of a target subject. The thresholds are set to be time-dependent, so that the current value of the threshold depends upon a time since the target subject has been discharged from a clinical environment.