AI-Enabled EHR Improves 16 of 18 SNF Quality Measures Across 94 Skilled Nursing Communities

Skilled nursing facilities struggle with reactive care models that miss early warning signs for adverse outcomes such as falls, depression, and functional decline. Providers lacked the large-scale data and real-time monitoring tools needed to proactively allocate clinical resources and prevent deterioration before it occurs.

MatrixCare deployed its Clinical Advanced Insights (CAI) platform — an AI and machine learning layer integrated into its EHR — across 94 skilled nursing communities. Introduced in 2021, CAI analyzes millions of resident data points to predict risk and trigger early clinical interventions across fall prevention, depression factors, and respiratory factors. Clinicians access dashboards at the facility and individual-resident level to guide proactive staffing and care decisions.

A study published in the Journal of the American Medical Directors Association compared 94 AI-enabled EHR communities against 124 non-AI EHR communities and found statistically greater improvement in 16 of 18 quality measures. AI-enabled facilities saw a 9% greater reduction in major falls and a 22% greater reduction in residents needing help with activities of daily living, along with improvements in depressive symptoms and antipsychotic medication use.

• Early identification drives outcomes: every quality measure that improved did so because issues were flagged and addressed proactively, before deterioration.
• AI impact extended beyond its original focus areas — CAI was designed primarily for falls but ended up improving four distinct quality metrics.
• Adoption barriers in SNF settings are largely perceptual; modern AI/ML tools are embedded in existing workflows and require no technical expertise to use.