Objective Impairment Injury Score Risk Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for assessing medical and physical impairments, such as those used in the healthcare and insurance industries, lack objectivity and fail to adequately consider the impact of impairments on job performance and occupation-specific factors, leading to inadequate compensation for injuries, particularly in high-risk professions.
Innovation Solution
An objective injury impairment score risk model utilizing a machine-learning model that generates questions to determine weighting factors based on injury data, including occupation, employment duties, and income sources, and stores this data securely using blockchain technology to provide an objectively determined impairment score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional subjective approaches are used for assessing impairment, then the assessment process is simple and quick, but the objectivity and accuracy of the impairment score is insufficient
Solution Approach 1:
The patent replaces subjective human assessment with an automated machine-learning model that processes injury data objectively. The system uses algorithms to analyze medical records, injury details, and occupation-specific factors, eliminating human bias and subjectivity while maintaining assessment accuracy.
Solution Approach 2:
The system enables self-assessment capability where the machine-learning model automatically generates impairment scores without requiring expert medical reviewers. The model self-adjusts weighting factors based on occupation data and injury characteristics, providing autonomous objective assessment.
2Adaptability or versatility
If traditional impairment assessment methods are used, then the assessment process is fast and simple, but the impact of occupation-specific factors on impairment is not adequately considered
Solution Approach 1:
The system pre-processes and stores occupation data, duty descriptions, and income information in databases before assessment is needed. When an injury occurs, the machine-learning model quickly retrieves pre-prepared occupation-specific parameters, eliminating the need for time-consuming data collection during the assessment process.
Solution Approach 2:
The system dynamically adjusts weighting factors based on occupation-specific parameters. The machine-learning model modifies the importance of different injury factors according to the patient's occupation, duties, and income level, providing customized impairment assessments that adapt to individual circumstances.
3Measurement precision
If detailed injury data is collected and stored, then the accuracy of impairment assessment is improved, but the security and privacy protection of sensitive medical data becomes challenging
Solution Approach 1:
The patent introduces a secure data storage and processing intermediary system that handles sensitive medical information. The machine-learning model processes data through encrypted channels and stored securely in protected databases, acting as a trusted intermediary between data collection and assessment output, thereby protecting patient privacy while maintaining assessment accuracy.
Data Source
AI summary
Various embodiments are directed to utilizing an objective impairment injury score risk model. A computing device may receive injury data from a user. The computing device may then perform a security action that protects against unauthorized sharing of the injury data by storing the injury data as a group of linked blocks in a distributed computing system. The computing device may then utilize a machine-learning model to generate a set of questions for the user. The questions may be utilized to determine weighting factors associated with the injury data. The computing device may then utilize the machine-learning model to determine an impairment injury score based on the weighting factors. The may include an objectively determined value associated with a degree of impairment resulting from an injury described in the injury data. Finally, the computing device may display an injury risk management report, based on the score, to the user.