Multisource Pain Assessment Regularization Against Subjective Bias
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Solution Overview
Problem
Pain assessment in clinical settings is highly subjective and inconsistent due to human judgment confounding factors, leading to disproportionate pain management that can hinder patient recovery.
Innovation Solution
A pain assessment regularizing system that collects and weighs multiple heterogeneous inputs, including clinician appraisals, patient self-reports, image capture, and physiological measurements, to derive a regularized pain assessment trend using a learning model to normalize and combine these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple heterogeneous inputs are collected and weighed using a learning model, then measurement precision and objectivity are improved, but device complexity increases
Solution Approach 1:
The pain assessment system segments the assessment into multiple independent input sources (clinician appraisals, patient self-reports, image capture, physiological measurements), each processed separately before being combined. This segmentation allows each component to be optimized independently while maintaining overall assessment accuracy.
Solution Approach 2:
A learning model serves as an intermediary that automatically weighs and integrates the multiple heterogeneous inputs. This intermediary processes the complex relationships between different input types and produces a unified pain assessment, reducing the need for manual integration complexity.
2Reliability
If manual pain assessment methods are used, then ease of operation is maintained, but reliability and consistency deteriorate due to human judgment subjectivity
Solution Approach 1:
The system incorporates multiple feedback loops where clinician appraisals, patient self-reports, image capture data, and physiological measurements continuously inform and adjust the pain assessment. This multi-source feedback mechanism reduces reliance on single human judgment while maintaining operational flow.
Solution Approach 2:
The learning model automatically performs the weighing and integration of multiple inputs without requiring manual intervention for each assessment component. The system serves itself by autonomously processing heterogeneous data sources and generating consistent assessments, reducing operational burden while improving reliability.
Data Source
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AI summary
Methods and systems implement a pain assessment regularizing system to autonomously observe pained expressions and physiological measurements of a patient, in order to systematically collect data inputs which may be converted to pain assessment factors. The pain assessment regularizing system, by collecting this data, may combine it with clinical appraisals of pain intensity and patient self-reporting of pain intensity, weighing each factor appropriately in a manner sensitive to the progression of a patient care program, so as to lessen confounding effects of subjective pain assessment. The pain assessment regularizing system may generate a time series of regularized pain assessment factors, and further forecast a regularized pain assessment trend. A clinician may further operate the pain assessment regularizing system to review a visualization of both the time series and the forecast, providing the clinician with rigorously sampled and analytically predicted data which cannot be derived through manual and mental efforts.