Fall Prediction Tool
Department of Veterans Affairs · VHA: Veterans Health Administration
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- High-impactSource: a) High-impact
- Impact justification
- Not reported
- Start date
- Not reported
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Health and Medical
- Operational functions
- Risk scoring and triage Derived by keyword rules; see methodology
Problem the AI is intended to solve
The Morse Fall Scale (MFS), as it is currently utilized, often fails to accurately assess fall risk, highlighting the need for more precise and timely fall risk assessment tools to prevent falls.
Expected benefits
The goal is to provide a more accurate fall assessment score than the existing Morse Fall Score which could improve patient safety outcomes. Better assessing fall risk and applying proper fall prevention interventions would also translate to cost avoidance and savings.
System outputs
A numerical Fall Risk Score for fall risk stratification.
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- Not reported
- Custom-developed code
- Not reported
- Public source code
- Not reported
Sourcing
- How it was built
- Not reported
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- Not reported
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- Not reported
- Demographic features
- Not reported
- Training and evaluation data
- Not reported
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Potential impacts and how they were identified
Not reported