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Home / Health / BAYADA Introduces AI-Driven Home Care Model to Enhance Senior Safety

BAYADA Introduces AI-Driven Home Care Model to Enhance Senior Safety

17 Nov

•

Summary

  • BAYADA launches Enhanced Quality of Care Model to improve home care
  • AI-supported approach combines daily nurse oversight and predictive tech
  • Aims to identify risks early and enable preventative interventions
BAYADA Introduces AI-Driven Home Care Model to Enhance Senior Safety

As of November 17th, 2025, BAYADA Home Health Care has introduced a new AI-supported approach to enhance the quality of care for older adults living at home. The company's Enhanced Quality of Care Model (EQoC) combines daily nurse oversight with predictive technology to identify risk factors early and enable preventative interventions.

Falls continue to be a major concern, affecting one in four seniors each year and often resulting in readmissions and mortality. BAYADA's EQoC model aims to address this challenge by providing care teams with trend analysis tools to improve daily monitoring and risk prevention. The model employs AI-generated insights to pinpoint predictors of adverse events and create individual risk profiles, allowing for timely adjustments to care plans.

The EQoC model includes monitoring of over 40 indicators, such as wounds, pain levels, mental health status, hospital admissions, and falls. This data-driven approach, supported by registered nurse supervision and continuous evaluations, enables BAYADA to tailor visit frequency and deliver appropriate care at the right moment, ultimately preventing hospitalizations and enhancing the quality of life for older adults.

Disclaimer: This story has been auto-aggregated and auto-summarised by a computer program. This story has not been edited or created by the Feedzop team.
BAYADA has introduced the Enhanced Quality of Care Model (EQoC), an AI-supported approach designed to improve safety and health outcomes for older adults living at home.
The EQoC model combines daily nurse oversight with predictive technology to identify risk factors early and enable preventative interventions, addressing the high rates of falls and hospitalizations among seniors.
The model includes monitoring of over 40 indicators, such as wounds, pain levels, mental health status, hospital admissions, and falls, and uses AI-generated insights to pinpoint predictors of adverse events and create individual risk profiles.

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