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How Adventist Health Lodi Memorial Reduced New Nurse Turnover by Over 60% with KATE AI

Hospital decreased new nurse turnover from 40% to less than 15%, preventing the loss of critical early-career staff.

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Adventist Health Lodi Memorial Case Study

New nurses leaving in their first year has been a huge issue for us and has reduced significantly with KATE. Prior to KATE, our nurse turnover was 40% and it is now less than 15% with KATE. I would attribute 20% of that to KATE.

Donovan Stewart
CNO / Patient Care Executive, Adventist Health Lodi Memorial

Adventist Health Lodi Memorial At A Glance

45,682
Annual ED Visits
194
Beds
Oracle Cerner
EHR System
Acute Care
Acute-Care Hospital
The Challenge

A Retention Crisis Driven by Triage Pressure

Seeking to elevate ED workforce stability, Adventist Health Lodi Memorial (AHLM) identified a critical retention barrier. New nurses faced immense pressure at triage due to the cognitive load of high-stakes decision-making, where subjective evaluations contributed to a 40% undertriage rate. This environment created unsustainable stress levels, resulting in a 40% turnover rate among nurses with less than two years of experience and driving up significant recruitment and training overhead.

40%
New Nurse Turnover
Before Implementation
40%
Undertriage Rate From
Subjective Evaluation
Burnout
Cognitive Load Driving
Early-Career Attrition
The Solution

KATE AI: An Always-On Safety Net for the Triage Team

To protect their workforce and reduce burnout, AHLM leadership operationalized Mednition’s KATE AI as an always-on safety net for the ED triage team. KATE functions as a second set of eyes, trained on millions of outcomes to catch subtle cues and provide real-time clinical support during patient assessment. This significantly alleviated the cognitive burden on new nurses by objectively identifying risk, directly mitigating the anxiety of missed care and creating a safer, more supportive environment for clinical practice.

  • Always-on safety net for the triage team
  • Real-time clinical support during assessment
  • Trained on millions of patient outcomes
  • Objective risk identification reducing cognitive load
  • Safer, more supportive environment for new nurses

Measurable Results

Significant improvements across retention, workforce stability, and clinician support.

Reduction in New Nurse Turnover
60%+
New nurse turnover reduced from 40% to less than 15%
Improvement Attributed to KATE
20%
Leadership explicitly attributes 20% of the retention improvement directly to KATE
A Safer Triage Environment
Supported
Newer-to-practice clinicians felt supported rather than overwhelmed in their first years

A More Stable, Supported Nursing Workforce

The emergency department successfully reduced its new nurse turnover rate from 40% to less than 15%. While retention is a complex challenge requiring a multi-faceted approach, leadership explicitly attributes 20% of this improvement directly to KATE’s ability to support newer-to-practice nurses.

By improving triage acuity levels, KATE made throughput more manageable and reduced the specific stressors that drive early-career attrition. AHLM successfully leveraged AI as a critical tool that leveled the playing field for newer-to-practice clinicians, ensuring they felt supported rather than overwhelmed during their first years of practice.

Prior to KATE, our nurse turnover was 40% and it is now less than 15% with KATE. I would attribute 20% of that to KATE.

Donovan Stewart
CNO / Patient Care Executive, Adventist Health Lodi Memorial

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Adventist Health Lodi Memorial

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