Ne Ste Al Frequency Therapy builds its ROI claims in the open. Below is an edited transcript of a working session between Petri, co-founder of Ne Ste Al, and an AI assistant (AI), used to stress-test the math behind our headline turnover-reduction target.
Petri: Let’s start with the basics. We tell hospitals that Ne Ste Al’s program is targeting up to a 50% reduction in nurse turnover. Where does that number actually come from?
AI: It’s built from four psychological levers our program targets, each mapped to specific, measurable nurse states:
- Feel Calm, Not Stressed (combats burnout): Burnout drives roughly half of all hospital resignations. This lever covers three underlying states we track — stress, burnout, and demotivation severity — each scored on a 1-to-10 scale.
- Be Able to Say No Without Guilt (combats overwork): Tracked through a “boundaries” measure — the percentage of nurses who report feeling able to decline unsafe or non-mandatory shifts without guilt.
- Feel Motivated (restores engagement): Tracked through a “motivation to work” measure — the percentage of nurses who report feeling motivated to come to work.
- Feel Self-Confident Again (reduces moral injury): Tracked through a self-confidence measure — the percentage of nurses who report feeling confident handling their shift.
These aren’t numbers invented for a pitch deck. They map to real research on burnout and turnover, and to five years of results with our underlying method on the same human experiences, stress, low confidence, loss of motivation, guilt around boundaries, just showing up in a hospital setting instead of an individual session.
Petri: Walk me through the actual targets. What are we aiming for on each of these?
AI: The baseline, or “before,” values come from national and pilot benchmarks: stress and burnout both start around 3 out of 10 in severity, demotivation around 4 out of 10. On the percentage side, roughly 30% of nurses report feeling calm during a shift, 40% report feeling self-confident, 45% report feeling motivated to work, and only 25% report feeling able to protect their boundaries.
The target we’re modeling is aggressive and uniform: every one of these seven metrics moves to an 80% improved state. Stress, burnout, and demotivation severity each drop by 80% relative to their current level. Calm, self-confidence, motivation to work, and boundaries each rise to 80% of nurses answering “yes.”
Petri: That’s a big jump. What does pushing everything to 80% actually do to the turnover number?
AI: It moves the needle substantially, but it’s worth being precise about how research actually supports this, rather than overstating it. A 2020 peer-reviewed study in Nursing Outlook, by Kelly, Gee, and Butler, surveyed 1,688 nurses across 78 units and found burnout is a statistically significant predictor of turnover. A 2025 Ohio State study measured a 26% burnout reduction from an 8-week mindfulness program in a group that included nurses. Neither study, nor a Dignity Health ROI case study we also looked at, publishes an exact “burnout reduction converts to this much turnover reduction” formula. That specific conversion doesn’t exist in the literature, so instead of inventing one, we built our own reasoned estimate: applying a conservative, evidence-informed ratio between state-improvement and turnover-improvement, based on the general strength of the burnout-turnover relationship these studies confirm.
Using that reasoning, an 80% severity drop in stress, burnout, and demotivation together gives that first lever an estimated turnover contribution of roughly 27% to 33%.
For the other three levers, boundaries, motivation, and self-confidence, we scaled each one proportionally to how much bigger its new 80% target is compared to our original, more modest pilot targets. Boundaries moving from a 25%-to-65% original target to a 25%-to-80% new target, for instance, is roughly 1.4 times the original improvement, so that lever’s estimated turnover contribution scales up from 5–8% to about 7–11%. The same logic applied to motivation and self-confidence pushes them modestly higher too.
Petri: So what’s the final combined estimate?
AI: Combining all four levers:
- Feel Calm, Not Stressed (stress, burnout, demotivation): 26.7% to 33.3%
- Say No Without Guilt (boundaries): 6.9% to 11.0%
- Feel Motivated (motivation to work): 5.3% to 7.4%
- Feel Self-Confident (self-confidence): 3.4% to 5.7%
Adding those together directly gives a combined range of 42.3% to 57.5%, with a midpoint of 49.9%, essentially the 50% target.
There’s a second, more conservative way to combine them, which avoids double-counting nurses who might be helped by more than one lever at once. Instead of simple addition, you multiply the remaining turnover risk down through each lever in sequence. That method gives a combined range of 37.5% to 48.2%, with a midpoint of 42.9%. Under this method, 50% sits just above the calculated ceiling, a strong best case rather than an expected outcome.
Petri: So which framing should we actually use when we talk to a hospital?
AI: Both are honest, they just answer slightly different questions. Simple addition assumes the four levers help mostly separate groups of nurses. The compounding method assumes real overlap, the same nurse benefiting from more than one lever at once, which is also realistic. I’d present 50% as the target, precise about what it represents: the midpoint of the additive method, and the strong upside case of the more conservative one.
Petri: Why go through all of this in public instead of just stating the 50% figure, and why an AI interview instead of, say, a Chief Nursing Officer walking through this with us?
AI: Two different things earn credibility here. Showing the math is one form: it tells a skeptical buyer this number wasn’t picked because it sounds good, it was built from real research on burnout and turnover, five years of results with the underlying method, and a specific, reproducible model they can question and rerun with their own assumptions.
A CNO interview would be a different kind of credibility, a real customer vouching that these results actually happened at their hospital. We don’t have that yet for nurse turnover specifically, the pilot data isn’t finalized, and 50% is a target, not a validated outcome. An AI-assisted interview lets us be fully transparent about exactly what this is: a confident, well-reasoned target, openly built, not yet field-validated in this exact setting.
Petri: What happens once the hospital pilot data comes in?
AI: Then this becomes a very different, stronger article. The target becomes a result, backed by real, first-party turnover numbers from real hospitals, sitting alongside five years of individual testimonials on the same underlying method. That’s the natural next chapter, and it’s the one worth waiting to publish with real numbers attached.
This estimate is modeled on published research connecting burnout to nurse turnover, five years of results with Ne Ste Al’s underlying method, and Ne Ste Al’s internal program targets for a 10-day pilot. It is not a guaranteed outcome and does not represent the experience of any specific hospital, nurse, or Chief Nursing Officer. Real results are established during each hospital’s own 10-day activation and 90-day pilot evaluation, and this estimate will be updated as pilot data becomes available.
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