6-Month Nursing Vacancy Forecasting Without a Crystal Ball

Why Reactive Staffing Is the Default — and What It Costs

A resignation letter arrives on a Tuesday. By Thursday, the scheduler is calling the float pool. By the following Monday, a travel-nurse agency is sending a rate card. Within three weeks, a position that was quietly underfilled for months has become an emergency procurement, and the cost of that emergency shows up on a finance report two months later — long after anyone remembers the Tuesday.

This is the standard operating mode at most facilities that manage nursing staffing in spreadsheets. It is not the result of poor judgment. It is the result of a measurement gap: the data needed to see a vacancy coming simply isn't assembled in a way that surfaces the signal before the resignation.

Nursing vacancy forecasting is the practice of closing that gap. Not with machine learning, not with a vendor's proprietary black box, but with a transparent, deterministic formula built on three inputs your facility already generates: how many FTE positions you have budgeted, how many are filled right now, and at what rate nurses have been leaving over the past 12 months.

This article walks through the mechanics of a 6-month vacancy forecast — what goes into it, how to build it without specialist software, and why the 78-day average time-to-fill for an experienced RN makes a six-month horizon the minimum actionable window for proactive nurse recruiting.

The Case for a Six-Month Forecast Window

Before building a model, it helps to understand why six months is the right unit of time to plan around — not 30 days, not a year.

Thirty days is too short. Once a nurse has given notice, the clock is already running against you. The average time-to-fill an experienced RN position is 78 days (NSI 2026 National Health Care Retention & RN Staffing Report, via Kahuna Workforce, 2026). That means a vacancy that opens today will, on average, remain open for more than two months even under an active recruitment effort. An agency placement can compress that timeline, but at meaningfully higher cost — direct-employ coverage is substantially less expensive than agency rates, and the cost difference compounds across the duration of an unfilled position.

A year is too long for most facility planners. Annual projections are useful for budget modeling and strategic workforce planning, but they carry too much uncertainty to drive near-term recruiting decisions. Months 7–12 are real enough to plan for in aggregate; they are too speculative to act on at the individual-position level.

Six months is actionable. It encompasses the full recruitment and onboarding cycle for most RN positions, captures the near-term departure signals visible in your own trailing data, and is short enough that the forecast can be refreshed monthly without becoming a major analytical undertaking.

The national context reinforces this urgency. HRSA projected a 78,610 FTE RN shortage in 2025 and 63,720 FTE RN shortage by 2030 (HRSA Nursing Workforce Projections Factsheet, November 2022). A more recent HRSA analysis projects an 8% RN shortage by 2028, narrowing to approximately 3% — roughly 108,960 FTE — by 2038, with rural and nonmetropolitan areas facing an estimated 11% shortage by 2038 (HRSA Nurse Workforce Projections 2023–2038 Factsheet, 2024). In that labor market, reactive recruiting increasingly means competing for a smaller available pool under time pressure. A 6-month nursing vacancy forecasting horizon gives your recruiting function a meaningful runway before the field narrows.

The Three Inputs Your Forecast Needs

A deterministic vacancy forecast doesn't require historical modeling of complex variables. It requires three clean inputs, consistently defined.

1. Current budgeted FTE by unit and role

This is your authorized headcount: the number of RN, LPN/LVN, and nursing-support FTE positions your facility has budgeted and approved. It is not a census count of hours worked last pay period. It is the structural baseline against which you measure the gap.

If your budgeted FTE figures live in an HR system or finance system but are not visible alongside your staffing data, that's the first integration point to address. A vacancy forecast that uses estimated or rounded FTE counts produces a forecast with the same imprecision baked in.

Understanding FTE-weighted headcount — how part-time, per-diem, and full-time employees are converted to a common FTE unit — is foundational here. A unit that carries 14 nurses but only 9.4 budgeted FTE has a very different vacancy picture than a unit with 14 nurses and 14.0 FTE.

2. Currently open vacancies

Count the positions that are unfilled today: positions that are budgeted, not filled, and actively open or on hold. Express them in FTE, not headcount. This is your starting gap — the vacancy floor your forecast builds forward from.

Tracking vacancy days open for each open position gives you a richer signal. A position open for 12 days behaves differently in your forecast than a position that has been open for 94 days. Aging vacancies may indicate sourcing difficulty, compensation misalignment, or scope-of-practice issues in that role — each of which requires a different response. For the baseline forecast, count them all.

3. Trailing 12-month departure rate

This is your most important forward signal. Calculate it as: total RN departures in the past 12 months ÷ average filled RN FTE over that period. This gives you a monthly departure rate when divided by 12.

Use rolling 12 months, not calendar year — a calendar-year figure truncates recent trend and introduces artificial seasonality. If you manage a unit where turnover spiked in a specific quarter due to an identifiable event (a manager change, a significant policy shift, a competing facility opening), consider whether the trailing 12 months accurately reflects ongoing departure risk or whether a shorter trailing period — six months, doubled — better captures the current reality.

The national staff RN turnover rate was 17.6% in 2025, up 1.2 percentage points from 2024 (NSI 2026 National Health Care Retention & RN Staffing Report, via Becker's Hospital Review, 2026). That's a starting benchmark if you don't yet have your own figure, but your unit-level rate is the number that drives your forecast. The NSI 2026 dataset — covering 527 hospitals and 262,405 RNs — shows RN turnover ranging from 5.6% to 40.0% by hospital bed count (NSI 2026, via Becker's, 2026). Your facility's rate is almost certainly not the national average, and your units are almost certainly not uniform.

Building the Forecast: Step by Step

Once you have the three inputs, the forecast is arithmetic.

Step 1: Calculate your expected monthly departure rate

Trailing 12-month departure rate ÷ 12 = expected monthly departure rate

Step 2: Project cumulative departures over six months

Expected monthly departure rate × 6 months × current filled FTE = anticipated departures in the next six months

Step 3: Add currently open vacancies

Projected FTE gap = currently open vacancies + anticipated departures over six months

This is your 6-month vacancy projection. It is not a prediction of exactly which nurses will leave or precisely when. It is a probabilistic estimate of the staffing gap your unit will face, expressed in FTE, based on the departure rate your facility has actually experienced.

Worked example (model only — illustrative inputs)

Suppose a 40-bed medical-surgical unit carries 38 filled RN FTE against a 40.0 FTE budget, with 2.0 FTE currently open. Over the trailing 12 months, 7 RN departures were recorded against an average of 37 filled FTE — a departure rate of approximately 18.9%, close to the NSI 2026 national rate of 17.6%.

• Monthly departure rate: 18.9% ÷ 12 = 1.58% per month
• Filled FTE × monthly rate × 6 months: 38 × 1.58% × 6 = approximately 3.6 FTE anticipated to depart
• Current open vacancies: 2.0 FTE
• Projected 6-month FTE gap: 5.6 FTE

That 5.6 FTE gap, against a 40 FTE budget, represents a 14% projected vacancy rate six months from today — before accounting for any improvement in retention or recruiting pace. With a 78-day average time-to-fill, beginning recruitment for those 5–6 positions today means the last new hire, under an optimistic scenario, completes onboarding close to the six-month mark. Starting in month three means the unit is understaffed through month eight at minimum.

Layering in Retirement Flags

The arithmetic above captures departures driven by the turnover rate you have experienced. It does not capture anticipated departures you can see coming — primarily nurses approaching retirement age or who have signaled retirement intent.

The nursing retirement cliff is a meaningful force in this forecast. NCSBN's 2022 National Nursing Workforce Study found that 610,388 RNs — nearly one in five — intend to leave the nursing workforce by 2027, with approximately 200,000 of those under age 40 (NCSBN 2022 National Nursing Workforce Study, 2023). The nurses approaching retirement represent departures that are, in many cases, visible in HR records: age band, years of service, expressed intent captured in stay interviews or exit surveys.

Retirement flags are a qualitative layer on top of the quantitative formula. If your HR data shows that three RNs on a unit are aged 60 or older with 25+ years of tenure, and one has informally indicated retirement within the year, that single piece of information should shift your recruiting urgency for that unit regardless of what the trailing departure rate says.

The practical integration: flag nurses who meet your facility's retirement-risk criteria (age threshold + tenure threshold, or expressed intent), convert them to FTE, and add them to your 6-month gap projection as a supplement to the formula-driven figure. Treat them as a separate line — not because they should be managed differently, but because they represent a qualitatively different signal (anticipated vs. probabilistic) and you may want to recruit specifically for their clinical specialty or experience level.

What a Vacancy Forecast Doesn't Tell You

A 6-month FTE gap projection is necessary for proactive recruiting. It is not sufficient for workforce planning by itself. Here is what it leaves out.

Unit-level mix. A projected 5.6 FTE vacancy on a single unit may mask a composition problem — if two of those departing nurses are charge nurses with ten years of experience and the replacement pipeline is new graduates, the staffing gap in FTE closes while an experience gap opens. Track seniority and specialty certification alongside FTE count where those distinctions affect care model.

Fill mode and cost. A vacancy forecast tells you a gap is coming; it doesn't tell you how you'll fill it. Agency coverage is substantially more expensive than direct employment — a point covered in depth in travel nurse vs. staff cost analysis. The forecast should prompt a fill-mode decision, not just a recruiting action: will this position be filled by a direct hire, an internal transfer, a float-pool expansion, or short-term agency coverage while recruiting continues? Each carries a different cost and lead time.

The forecast is only as good as the inputs. If your FTE budget figures haven't been reconciled since the last fiscal year, or your departure records have gaps, the arithmetic above produces a number with false precision. Before treating the output as an operational commitment, sanity-check the inputs: do the filled-FTE numbers match a recent payroll census? Are all departures, including mid-contract and per-diem separations, captured?

From Spreadsheet to Structured Tracking

The method described here can be implemented in a spreadsheet — and doing so is far better than no forecast at all. For a single unit with a stable headcount, a well-maintained Excel or Google Sheets workbook gives you most of the value.

The challenges emerge at scale. A Director of Nursing managing three to four units is tracking multiple departure-rate histories, multiple FTE budgets, and multiple retirement-flag cohorts, each refreshed monthly. Keeping those tables consistent, applying the formula uniformly, and generating a summary view of aggregate facility gap requires more spreadsheet discipline than most staffing environments can sustain alongside clinical operations.

Our Nursing Vacancy & FTE Forecasting Workbook is a structured Excel workbook built around this exact method: budgeted FTE by unit and role, current open vacancies, trailing departure rate, retirement flags, and a six-month projected gap — with the formula transparent and the inputs clearly separated from the outputs. It's designed for nurse managers and Directors of Nursing who want the structure without building it from scratch.

If you're looking for ongoing, automated calculation alongside BLS wage benchmarking, retention risk scoring, and rolling turnover tracking, Nursing Workforce Planner's Professional tier includes 6-month vacancy forecasting built directly into the dashboard — alongside the departure tracking that feeds it.

The Cost of Getting This Wrong

Before moving on to implementation, it is worth grounding the stakes in sourced figures, calmly stated.

The NSI 2026 report puts the average cost of a single RN departure at $60,090 (NSI 2026, via Becker's Hospital Review, 2026). Across a hospital, the average annual RN-turnover cost is $5.19 million, ranging from $4.2M to $6.2M (NSI 2026, via Becker's, 2026). Each percentage point of RN turnover costs the average hospital approximately $295,000 per year (NSI 2026, via Becker's, 2026).

The connection to vacancy forecasting is direct. A gap projection that arrives six months early gives recruiting a full cycle to source a direct hire. A gap projection that arrives the day a resignation is received — which is what happens when the spreadsheet only shows current vacancies — compresses that cycle to the point where agency coverage is often the only option, and agency rates are substantially higher than directly employed staff. The NSI 2026 data notes that replacing 20 travel nurses with employed staff can save $1.32M (NSI 2026 / Kahuna Workforce, 2026).

These are not arguments for alarm. They are the arithmetic of a planning decision: the same staffing gap, filled proactively vs. reactively, has a different cost. A vacancy forecast is the mechanism that creates the proactive option.

A Practical Starting Point

Nursing vacancy forecasting does not require a machine-learning model, a statistical consultant, or a six-month implementation project. It requires clean inputs, a consistent formula, and a monthly refresh discipline.

Start with one unit. Pull the trailing 12-month departure count, the current filled FTE and budgeted FTE, and count the open vacancies. Apply the formula. That number — your projected 6-month FTE gap — is the basis for a recruiting conversation that starts today instead of in month five.

If the formula shows a gap, the next question is sourcing: who is your recruitment partner, what is your current offer relative to the BLS May 2024 median RN wage of $93,600 (BLS Occupational Outlook Handbook, May 2024), and does your internal pay band sit at, above, or below the regional median for the role?

That's a conversation worth having with six months on the clock. It is a different conversation entirely when the clock has already run out.

For a ready-made structure to begin this work, download the Nursing Vacancy & FTE Forecasting Workbook — a unit-by-unit Excel template built on the method described here, with the formula transparent and the inputs clearly labeled.