Automated Fall Risk Stratification System
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Solution Overview
Problem
Accurately assessing patient fall risk in care facilities is challenging due to the complexity of contributing factors and the need for continuous updates in risk assessment methods, making it difficult to efficiently allocate resources for prevention.
Innovation Solution
A system that automatically aggregates patient data from healthcare information systems, accesses facility-specific rules for fall risk stratification, and generates alerts and tasks for caregivers to mitigate fall risks, using a cloud-based or local network infrastructure to communicate with various devices and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual fall risk assessment methods are used by healthcare providers, then flexibility in applying clinical judgment is maintained, but assessment accuracy and consistency deteriorate due to human error and variability
Solution Approach 1:
The system automatically performs fall risk assessments by retrieving patient data from electronic health records and applying falls-related rules without requiring manual input from healthcare providers. The processor autonomously calculates fall risk scores, determines stratifications, and generates prevention protocols, eliminating human error while maintaining clinical accuracy through consistent application of evidence-based criteria
Solution Approach 2:
The patent replaces manual mechanical assessment processes with an automated computational system. The processor executes algorithms that automatically aggregate patient data, apply falls-related rules, and generate assessments, substituting human cognitive processes with machine-based calculations to improve precision and consistency
2Measurement precision
If comprehensive falls-related data aggregation from multiple healthcare information systems is implemented, then assessment accuracy improves, but system complexity and data integration challenges increase
Solution Approach 1:
The system is designed to interface with multiple different healthcare information systems including electronic health records, pharmacy systems, and laboratory systems through standardized data aggregation processes. The processor can retrieve various types of patient data from diverse sources using universal communication protocols, enabling comprehensive fall risk assessment without requiring separate specialized systems for each data source
Solution Approach 2:
The patent introduces an intermediary data aggregation layer that sits between multiple healthcare information systems and the fall risk assessment engine. This intermediary component standardizes data formats, handles data reconciliation, and manages integration complexities, allowing the assessment system to access comprehensive patient data without being directly coupled to each individual information system
3Productivity
If automated fall risk assessment systems are implemented, then resource allocation efficiency improves, but initial implementation costs and infrastructure requirements increase
Solution Approach 1:
The automated system continuously monitors patient fall risk and self-adjusts prevention protocols without requiring manual resource allocation decisions. The processor automatically generates updated prevention protocols when fall risk stratification changes, enabling real-time resource optimization while reducing the need for manual intervention and improving overall resource allocation efficiency
Solution Approach 2:
The system performs preliminary fall risk assessments at patient admission and continuously updates assessments throughout the care episode. By proactively identifying high-risk patients before falls occur and pre-generating prevention protocols, the system enables advance resource allocation and prevention planning, improving productivity while the infrastructure complexity is offset by the preventive nature of the system
4Loss of time
If continuous monitoring and repeated assessments are performed, then fall risk detection timeliness improves, but processing time and computational resources increase
Solution Approach 1:
The system performs fall risk assessments at periodic intervals including patient admission, transfer between facilities, and discharge, as well as continuously monitoring for changes in patient condition. This periodic assessment approach ensures timely detection of fall risk changes while optimizing computational resource usage by assessing only when clinically relevant events occur rather than continuously processing all patient data
Data Source
AI summary
Automatically gathering and processing data relating to risk of patient falls. The data is analyzed to determine a level of risk each patient has for falling. Patient risk for falls is stratified and protocols are implemented to mitigate that risk. The system communicates instructions and alerts to caregivers to complete tasks or provide aid to patients. Updates to patient medical information and updates to best practices in fall risk management result in updates to patient risk stratifications. In turn, tasks and alerts are updated to reflect updates to patient risk stratifications.