In 2015, 23% of new nurses were ready. By 2020, 9%.

This isn’t an opinion about a generation – it’s measured. Today, the majority of new graduate nurses aren’t meeting expected competency standards. It’s the gap your preceptors feel on every shift, and the one no system has been able to see.

91%

Not meeting competency standards

22%

First-year RN turnover

$60K

To replace each one

See the evidence

The readiness gap, measured

The pattern

Readiness has been falling for a decade.

It isn’t one snapshot – it’s two studies, years apart, from the same lead researcher. Across 2011–2015, 23% of new graduate nurses met expected entry-level competency. By 2020, 9% did. The line points one way.

It shows up downstream. More than one in five newly hired nurses leave within their first year – and each departure costs about $60,000 to replace, before counting the preceptor hours that walk out with them.

The reframe

It isn’t that education is inadequate. It’s that no one can see whether a nurse is ready.

Every health system educates its new nurses. What no system has had is a way to know – before a patient depends on it – whether that education produced true readiness or simply completion. Readiness has been invisible, so the gap stays hidden until it surfaces at the bedside.

What changes

Make readiness visible – then close the gap.

OMS turns clinical preparation into something you can see. New nurses make real clinical decisions in simulated scenarios, and the platform captures how they reason, where they hesitate, and what they miss. Readiness stops being a guess and becomes data – the workforce infrastructure a system can manage, not just hope is working.

14 hrs

Memorial Hermann – a 17-hospital system

A section of nurse residency, rebuilt around active clinical decision-making. Sixteen hours of e-learning became two, the outcomes held, satisfaction rose to 89%, and 14 hours per nurse went back to the bedside.

See the full Memorial Hermann case →

Common questions

Answered, plainly.

Independent research tracked new-graduate competency from 23% across 2011–2015 (Kavanagh & Szweda) to 9% in 2020 (Kavanagh & Sharpnack). The decline is measured, not anecdotal – and it’s what preceptors report feeling on the floor.

The ability to make sound clinical decisions under real conditions – not modules completed. OMS measures the decision: what a nurse assesses, how they prioritize, where they hesitate, and what they miss.

Education exists everywhere; visibility doesn’t. The gap isn’t a lack of education – it’s that no system could see whether education produced readiness or just completion. OMS makes that visible.

More than one in five newly hired nurses leave within their first year, at about $60,090 each to replace (NSI, 2026) – plus the preceptor time lost with them. Readiness is a retention and safety issue, not only an education one.

OMS is an AI-driven clinical simulation platform where nurses make real clinical decisions in immersive scenarios, capturing how they reason as data – the workforce infrastructure a health system can measure and manage.

Readiness Evidence

See the readiness evidence for a system like yours.

We’ll send the full readiness evidence pack – the research, the methodology, and the named outcomes – and walk through what making readiness visible would look like across your facilities.