Patient State Classification for Chronic Pain Monitoring
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Solution Overview
Problem
Current systems for managing chronic pain with neurostimulation devices are inefficient in monitoring patient states, triaging interventions, and providing remote treatment adjustments, leading to delayed and inadequate management of patient pain and quality of life.
Innovation Solution
A system that categorizes pain patients into distinct states based on multiple parameters, allowing for remote monitoring, triaging, and adjustment of treatment plans, using a state-based classification system that includes physiological, psychological, and physical metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote patient monitoring is implemented without state-based classification, then patient data can be collected, but it becomes difficult and time-consuming to determine which patients need intervention
Solution Approach 1:
The system implements automated feedback mechanisms where patient data is continuously monitored and automatically processed through state-based classification algorithms. The system provides real-time feedback to clinicians about which patients require intervention, eliminating manual review of all patient data and significantly improving monitoring efficiency while reducing time loss.
Solution Approach 2:
The monitoring system performs self-service by automatically categorizing patients into states based on their data patterns. The system autonomously identifies which patients need intervention without requiring clinician time for manual assessment, allowing the system to serve itself in the initial triage function before clinician involvement is needed.
2Measurement precision
If comprehensive patient parameters are collected for accurate state classification, then patient management accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of patient monitoring into distinct state classifications (e.g., stable, at-risk, critical states). By dividing patient data into manageable categorical states based on multiple parameters, the system achieves high classification accuracy while keeping the underlying complexity managed through modular state definitions rather than monolithic complex algorithms.
Solution Approach 2:
The state-based classification system serves multiple functions simultaneously: it classifies patient risk levels, prioritizes intervention needs, tracks patient progress over time, and guides treatment decisions. This multi-functionality allows comprehensive parameter collection to be processed through a unified framework that improves accuracy without proportionally increasing perceived system complexity.
3Speed
If real-time patient state data is provided to clinicians, then intervention timing improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and pre-categorizing patient states before clinician review is needed. Patient data is continuously processed and classified into states in advance, so when clinicians need to make decisions, the classification is already complete. This preliminary data processing enables fast intervention response while distributing energy consumption over time rather than concentrating it at decision moments.
Data Source
AI summary
Various embodiments of the present subject matter use health-related information to improve the monitoring and treatment provided to remotely managed patient populations by generating patient states to simplify the triaging of patient conditions. In an example, the system can receive from a user via a user interface, criteria related to one or more goals to accomplish while receiving therapy to treat a condition. The system can translate, by at least one hardware processor, the criteria related to the one or more goals to a state-based classification defining a plurality of patient states defined by a plurality of parameters and receive a first set of parameter values. The system can further identify a first patient state from among the plurality of patient states using the first set of parameter values and generate an output including the first patient state.