Virtual Home Fall Risk Assessment Using PFRI and EFRI
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Solution Overview
Problem
Existing healthcare systems lack tools to assess home environments with clinical precision, particularly during transitions of care, leading to heightened fall risks for vulnerable populations like the elderly and disabled, and there is a need for an integrated approach connecting health status to environmental safety.
Innovation Solution
A virtual system comprising an app that analyzes home environment data and health information to generate personalized fall risk indices (PFRI and EFRI) and provides tailored recommendations, connecting users to professionals and resources for fall prevention and home safety modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hospitals and health systems use traditional discharge planning methods, then the discharge process is simple and quick, but they lack the ability to assess home environment safety with clinical precision, leading to heightened fall risks
Solution Approach 1:
The system creates a virtual digital replica of the patient's home environment by capturing images and videos during a virtual home visit. This digital copy allows clinicians to assess environmental fall risks remotely with clinical precision without physically visiting the home, resolving the contradiction between assessment precision and system complexity.
Solution Approach 2:
The patent introduces a virtual home visit intermediary system that bridges the gap between clinical assessment needs and home environment evaluation. This intermediary uses AI-powered image analysis to translate visual home environment data into clinically relevant fall risk assessments, enabling precise measurement without direct clinician presence.
2Measurement precision
If clinicians conduct comprehensive in-person home environment assessments, then they can identify all fall risks accurately, but it requires significant time and resources, reducing productivity
Solution Approach 1:
The system enables patients to conduct self-service virtual home visits by capturing their own home environment images and videos. The AI system then automatically analyzes these images to identify fall risks, eliminating the need for clinician time during the actual home visit while maintaining comprehensive assessment accuracy.
Solution Approach 2:
The patent replaces the mechanical process of clinician-driven in-person home assessments with an automated AI-based image analysis system. This substitution maintains measurement precision by using sophisticated algorithms to identify environmental fall risks while dramatically improving productivity by eliminating the time-consuming nature of manual assessments.
3Loss of information
If the system collects and analyzes extensive home environment data through virtual visits, then it generates comprehensive fall risk indices and personalized recommendations, but the data processing and analysis complexity increases
Solution Approach 1:
The system extracts only the most clinically relevant environmental features from the comprehensive home environment data captured during virtual visits. The AI algorithm identifies and extracts key fall risk elements such as carpet edges, loose rugs, poor lighting areas, and bathroom hazards, filtering out unnecessary information to reduce analysis complexity while maintaining information completeness.
Solution Approach 2:
The patent segments the complex data analysis process into distinct modules: image capture, feature detection, fall risk identification, and recommendation generation. This segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining comprehensive information analysis.
4Adaptability or versatility
If the system provides detailed personalized recommendations and connects users to contractors and funding sources, then it comprehensively addresses fall prevention needs, but the system coordination complexity increases
Solution Approach 1:
The system is designed as a universal platform that integrates multiple functions: environmental assessment, personalized recommendation generation, contractor matching, and funding source identification. This multi-functional approach allows the same core system to handle diverse fall prevention needs across different patients and home environments without requiring separate specialized systems for each function.
Data Source
AI summary
A virtual system of fall prevention and home safety and optionally hospital discharge, comprising an application to implement a method comprising obtaining data on a resident of the resident's home environment and obtaining data on the resident's health;analyzing said data with an algorithm and generating a Personalized Fall Risk Index (PFRI) and an Environmental Fall Risk Index (EFRI), thereby allowing a clinician and/or an artificial intelligence (“AI”) system to assess the resident's fall risk at the home environment and to generate a list of personalized recommendations;suggesting products to said user and/or said resident, connecting said user and/or resident to local contractors, installers and other professionals, and/or connect the user and/or resident to source of grant funding. The PFRI is generated through applied coefficients to predictor variables. The EFRI is derived first as unbound score by averaging three individual scores: weighted average response score, room score, and occupancy score.