Event Tracking System for Clinical Process Prediction
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Solution Overview
Problem
Clinical processes, such as radiological imaging, are challenging to predict due to unforeseen conditions, leading to unreliable duration predictions and sub-optimal workflow, scheduling, resource management, and patient management.
Innovation Solution
An event tracking system that receives event data from sensors, accesses a snapshot stack of previous events, updates snapshots based on new data, and predicts future snapshots by analyzing patterns in the updated snapshot stack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prediction methods are used for clinical processes, then simplicity is maintained, but prediction reliability deteriorates due to inability to account for unforeseen conditions and synthesize information from multiple participants
Solution Approach 1:
The system segments the clinical process into discrete snapshots representing different states or moments in time. Each snapshot captures specific process parameters and events, allowing the system to analyze and predict process duration by examining sequential segments rather than treating the entire process as a single unpredictable entity.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual process events and comparing them against predicted timelines. This feedback loop allows the system to learn from deviations and improve future predictions, accounting for unforeseen conditions that arise during clinical processes.
2Measurement precision
If detailed tracking of all process events is implemented, then prediction accuracy improves, but information processing complexity and resource requirements increase
Solution Approach 1:
The system extracts only the most relevant process events and parameters from the complete clinical workflow, storing them as structured snapshots. This selective extraction maintains measurement precision for critical process aspects while reducing the overall data volume that requires processing and storage.
Solution Approach 2:
The system performs preliminary organization and structuring of process data into standardized snapshot formats as events occur. This pre-processing arrangement enables more efficient subsequent analysis and prediction operations, reducing the computational complexity required during the prediction phase.
3Productivity
If manual synthesis of process information is used, then system complexity is kept low, but workflow continuity deteriorates due to interrupted and discontinuous predictions
Solution Approach 1:
The system enables self-service automation where the snapshot stack and prediction algorithms automatically synthesize process information and generate duration predictions without requiring manual intervention. This automated synthesis maintains workflow continuity by providing continuous, uninterrupted predictions that adapt to real-time process changes.
4Productivity
If comprehensive process monitoring is implemented, then resource management quality improves, but system operational complexity increases
Solution Approach 1:
The snapshot stack structure serves multiple functions simultaneously: it stores process state information, tracks events chronologically, provides data for prediction algorithms, and enables retrospective analysis. This multi-functionality improves resource management quality through comprehensive monitoring while avoiding the need for separate specialized systems for each function.
Data Source
AI summary
Methods, apparatuses and systems provide for technology that translates detected physical events to provide information about the current state of a patient process and predict the timing of subsequent states. Events may be decomposed into a series of snapshots associated with timestamps. The embodiments herein determine patterns between the events to identify and predict future states. For example, some embodiments may generate a snapshot stack, and generate a predicted next snapshot based on the snapshot stack.