AI Empowers HR Agency: 3x Efficiency, 92% Fewer Cancellations

A I • May 30,2024

A caregiver staffing agency faced unreliable caregivers with frequent last-minute cancellations. They implemented an AI solution that analyzed historical data to predict caregiver churn. This enabled them to proactively manage staffing and achieve a 92% reduction in cancellations, boosting efficiency by 3 times.

Client:

A well-known caregiver staffing agency in New York that faced significant challenges due to high caregiver churn rates and an ineffective staffing methodology.

Problem Statement:

● Last-minute cancellations: Caregivers frequently canceling assignments created chaos, leaving the agency scrambling for replacements.
● Missed appointments and delays: These disruptions caused clients inconvenience, potentially leading to missed appointments and delays in care delivery.
● Increased costs: The need for frequent replacements and overtime to cover gaps resulted in higher labor expenses.
● Damaged client satisfaction: Unreliable care eroded client trust and harmed the agency’s reputation.

Results:

● 92% Churn Reduction: AI predicted high-risk caregivers, allowing proactive action and a dramatic drop in cancellations.
● 3x Staffing Efficiency: Optimized scheduling ensured coverage and reduced last-minute replacements, boosting efficiency by 3x.
● Reduced Costs: Minimized cancellations and optimized staffing led to significant labor cost savings.

AI Solution:

Faced with frequent last-minute cancellations from caregivers, the caregiver staffing agency has implemented Obviously AI, an artificial intelligence (AI) platform.

This AI solution leverages the power of machine learning. The agency provided Obviously AI with their historical data on caregivers and patients. This data included demographics, age, gender, location, preferred work times, and past instances of cancellations. By analyzing these factors, Obviously AI trained a model to predict which caregivers were more likely to cancel their shifts.

The AI solution identified high-risk caregivers and automatically assigned them to shifts that minimized their chance of canceling. This data-driven approach allowed the agency to optimize its staffing process and ensure it had reliable caregivers available when needed.