With over 400,000 patient-harm related deaths annually and costs of more the $1 billion, health systems urgently need ways to improve patient safety. One promising safety solution is patient harm risk assessment tools that leverage machine learning.
An effective patient safety surveillance tool has five core capabilities:
1. Identifies risk: provides concurrent daily surveillance for all-cause harm events in a health system population.
2. Stratifies patients at risk: places at-risk patients into risk categories (e.g., high, medium, and low risk).
3. Shows modifiable risk factors: by understanding patient risk factors that can be modified, clinicians know where to intervene to prevent harm.
4. Shows impactability: helps clinicians identify high-risk patients and prioritize treatment by patients who are most likely to benefit from preventive care.
5. Makes risk prediction accessible: integrates risk prediction into workflow tools for immediate access.