Filled 45 Nursing Positions in 30 Days Across 3 Provinces

Healthcare Staffing Group · Healthcare, Canada

A healthcare staffing group filled 45 nursing positions across Ontario, BC and Alberta in 30 days — against a previous run rate of eight a month — with candidate drop-off falling from 58% to 12%.

Results at a Glance

  • Positions Filled: 8/month → 45 in 30 days
  • Time to First Interview: 5 days → 3 hours
  • Candidate Drop-off: 58% → 12%

Background

Nursing is a candidate's market: the same nurse is being courted by several agencies at once, so the agency that reaches a decision first usually wins. A five-day wait for a first interview is not a scheduling inconvenience, it is the loss.

The Challenge

- Healthcare staffing company serving hospitals in Ontario, BC, and Alberta - Nurses applying after night shifts could not schedule daytime interviews - 5-day wait for a first interview meant most candidates accepted other offers - Candidate drop-off rate was 58% - Traditional ai hiring tools could not accommodate shift workers

What They Tried First

Before bringing in The Cognitive, the team tried to solve the problem with faster manual phone screens, more calendar chasing, and standard hiring software. None of it fit nurses coming off night shifts. Recruiters still needed candidates to be awake, available, and reachable during office hours, so the same applicants who looked qualified on paper kept disappearing before a first conversation.

The Solution

- The Cognitive provided 24/7 ai video interviewing, available even at 2 AM after a shift - AI interviewing technology conducted behavioral and situational interviews tailored to healthcare - Verified credentials discussed in conversation - Flagged candidates ready for immediate placement - No scheduling coordination needed

How the Rollout Went

The rollout focused on replacing the scheduling bottleneck rather than rebuilding the whole process. Healthcare-specific behavioral and situational interviews were configured, credential checks were worked into the conversation, and recruiters agreed what ready for immediate placement should mean. The roughest part was trust: some staff initially treated the AI evaluation as another screen to re-check, especially on credential nuance. That settled as they saw the credential discussion in context.

The Results

- Filled 45 positions in 30 days across 3 provinces - Candidate drop-off fell from 58% to 12% - 24/7 availability was critical for shift workers - Hospital clients reported higher satisfaction with candidate quality - AI evaluation more consistent than phone screens - See AI interview pricing for high-volume hiring teams. - Read more: How AI conducts live video interviews 24/7

What It Was Worth

Time to first interview fell from 5 days to 3 hours. Drop-off fell from 58% to 12% — meaning roughly half the pipeline that used to disappear now stays in it, which is where most of the 45 placements came from.

What Transfers to Other Teams

In shift-based healthcare hiring, the binding constraint is rarely candidate supply; it is the delay between interest and interview. Compressing that window converts a pipeline that already existed.

Nurses work 12-hour shifts. They cannot take a screening call at 2 PM on a Tuesday. The AI interviews them at midnight and we have a scorecard by morning. That changed everything for us.

VP Talent Acquisition, Healthcare Staffing Group, Toronto

Questions About This Rollout

Why did candidate drop-off fall so sharply?

Time to first interview went from 5 days to 3 hours, and candidates could interview at any hour rather than during clinic hours. Most drop-off in nursing recruitment happens in the wait, not at the interview.

Does this work across provinces with different licensing?

Licensure is a hard requirement, so it is handled as a knockout gate before interview rather than as something an interviewer checks. The interview then assesses clinical judgement consistently across all three provinces.

Other hiring teams we have done this for

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