Key takeaways
- At a health system in Illinois, a shift posted seven or more days ahead was filled by the hospital’s own staff 38.1% of the time. The same kind of shift posted less than four hours ahead was filled 1.9% of the time. Same units, same people, same pay scale. Twenty times more likely to be filled.
- Across three departments over the 90-day pilot, 349 shifts were posted inside the 24-hour window. Forty filled. The other 309 defaulted to overtime, incentive pay, or agency.
- The urgent shifts already carried the highest incentive in the dataset, $15 an hour, and still converted at the lowest rate. Money alone may not be the lever in the last 24 hours.
- When an urgent shift did fill, the answer came fast: a median of under two hours from posting. The willingness is there. The way of asking is often what fails.
- A planned open shift and an urgent open shift are different problems with different fixes. Most hospitals run one process for both, and it is the urgent one that ends up outside the building.
Every hospital has a process for filling an open shift, and most of the time, it works. A gap is posted and the department moves on. But that same process is also expected to solve an 0540 call-out for an 0700 start. That is a fundamentally different problem, and most staffing workflows were never designed to solve it. The data below shows why coverage gaps with hours or days of lead time cannot be treated the same as urgent coverage gaps.
The open shift is not one problem
Over 90 days of a deployment across three departments at a health system in Illinois, 1,389 shifts were posted for internal staff to pick up. The fill rate was tracked against one variable: how far ahead of the start time each shift was posted.
The curve is not subtle. Shifts posted seven or more days out filled 38.1% of the time. Three to seven days out, 30.3%. One to three days, 29.5%. Then the decline accelerates. Inside 24 hours, between 13% and 17%. Less than four hours, 1.9%.
Read it as two problems and the pattern makes sense. Give a shift a few days of runway and it fills at a one-in-three rate, which is a coverage problem a manager can plan around. Move inside 24 hours and the fill rate drops to about 11%, more than three times lower than a week-out shift. Inside four hours, it is twenty times lower.
At that point, the problem has changed. It is no longer just about whether someone is available to work. It is about whether the right person can be reached, decide, and respond before the window closes.
Most hospitals treat both with the same tool: a manager, a phone, and a list. With enough lead time, that process can work. On the urgent gap, it breaks down quickly, and the result is exactly what the data shows: the internal fill rate collapses, and the shift moves outside.
What the urgent window is costing
The reason this matters to both an operator and a scheduler is what happens to the nearly 90% of urgent shifts that do not fill internally. They still get covered. They get covered by overtime, incentive pay layered on top of overtime, or by an agency order placed because the manager ran out of time. In our own data, the shift that gets sent outside is typically the most expensive, and often the most avoidable.
Nationally, that is not a small line. Kaufman Hall’s National Hospital Flash Report found hospital contract labor expense rose 257.9% between 2019 and 2022, with the median wage rate paid to staffing firms up 56.8% over the same period. It has come down from the peak, but the reflex it built has not. HFMA reports that during the pandemic, premium pay contracts and overtime grew to nearly 10% of direct labor costs, and most systems are still working that number back down.
Turnover compounds it. The 2026 NSI National Health Care Retention and RN Staffing Report puts the average cost of replacing one staff RN at $60,090, with each percentage point of turnover worth roughly $295,000 a year to the average hospital. The same report puts the average hospital at 43 unfilled RN positions and 56 to 102 days to recruit an experienced nurse. Every one of those unfilled positions produces open shifts, and the ones that surface late are the ones that go to premium labor. We have written before about what turnover does to a hospital’s margin; the urgent shift is where that cost shows up week to week.
Now put the Illinois numbers against that. Over 13 weeks, across just three departments, 309 urgent shifts went unfilled by internal staff. That is about 24 shifts a week at risk of rolling into overtime or outside labor. That is three departments of one hospital. Scale that across the rest of the building, then across the full year, and the cost compounds quickly.
Why the usual fixes do not work inside 24 hours
The first instinct when a shift is still open at the last minute is to raise the incentive. Sometimes that works. But the data suggests that incentive alone is not always enough, especially when the window to reach the right person has already narrowed.
In the Illinois dataset, the average incentive paid on shifts posted less than four hours ahead was $15 an hour, the highest of any group. Those shifts filled at 1.9%. The average incentive on shifts posted a week or more ahead was $9.67 an hour, the lowest of any group. Those filled at 38.1%. The pattern suggests that as urgency increased, hospitals were already paying more to solve the problem, but higher incentives alone were not enough to overcome the shrinking response window.
That matters because it complicates the easy answer. More money can help, but it is only one part of the equation. In the urgent window, success depends on reaching the right person quickly, with an offer strong enough to move them.
The second instinct is to work the phone harder. That is what most units already do: the charge nurse or manager starts calling, texting, and checking who is off. It is slow, it reaches a handful of people, and it consumes the one person on the unit whose attention is worth the most at that moment. By the time the list is exhausted, the agency order is the only option left, and it is placed not because it was chosen but because the clock ran out.
The demand is there. It is fast.
Here is the part of the dataset that changes the conversation. When an urgent shift did fill, it filled quickly. The median time from posting to acceptance on last-minute shifts was under two hours. Across all filled shifts, one in six was accepted within an hour of going up.
In other words, the staff were not refusing. When the shift reached them, a meaningful share said yes, and they said it fast. The willingness was never the missing piece. The reach was.
The same pattern shows up by department. In the first seven weeks of the pilot, respiratory therapy at this system filled 40% of its urgent shifts with a median time to fill of 48 minutes. Surgical acute filled fast once it filled, a 5.6-hour median, but three in four postings never found a taker. Intermediate care posted the most shifts and took the longest, a 93-hour median. The urgent-shift problem looks different from one unit to the next, which means the policy and the response should too. The unit is the level where this has to be measured.
And when reach was solved, the numbers moved. By 90 days, internal staff at this system had picked up 360 open shifts across the three departments, about 3,700 hours of patient care that did not go to overtime or agency.
Managing two problems as two
If a gap with days of lead time and a gap with only hours to solve are different problems, they need different handling. The Illinois data points to four things hospitals need to start thinking about.
Post known gaps a week out. The single cheapest lever in the dataset is time. Approved PTO, standing vacancies, and predictable census swings are knowable well before they become an urgent post. Every day of lead time added to a shift moves it up the fill curve.
Stop treating incentive as the only fix for urgency. Incentive can absolutely help move a hard-to-fill shift, but urgency introduces a different constraint: time. If a shift is posted at 0540 for an 0700 start, the right incentive may improve the odds, but it still has to reach the right person fast enough to matter. We break down the incentive data in You Are Already Paying the Most for the Shifts That Fill the Least.
Treat the urgent shift as a reach problem. In the final 24 hours, the question is not only “who can we pay to take this” but “how many qualified people saw it?” A shift that reaches every eligible employee at once, on the device already in their pocket, is a different shift from one that reaches the six people the charge nurse had to call. The staff were always there; what was missing was visibility, and once they can see the shift, the ones that fill tend to fill within the hour.
Measure by unit, then adjust. Fill rate at this system swung from under 2% to 38%, and time to fill from under an hour to 93 hours, depending on when and where a shift happened to be posted. None of that variance is visible without tracking fill rate, time to fill, and incentive spend by department, weekly. Once it is visible, every change to posting policy or pay becomes a test with a result instead of a guess.
Where we come in
Gigly is how the Illinois system solved the reach problem. Your own employees see open shifts on their phones and pick them up, before those shifts turn into overtime, incentive pay, or agency. There is no new hire, no contract, and no IT project; the system above was live in about 72 hours. Open shifts get covered one way or another. The question is whether they get covered by your people at your rates, or by somebody else’s at theirs.
This is the first of six pieces from this dataset. The next five each take one finding and go deeper, starting with the incentive numbers. As they publish, they will be linked here.
If you want to see your own two curves, pull the last 90 days of open shifts, sort them by how far ahead they were posted, and look at the fill rate on either side of the 24-hour line. Then look at what the unfilled side cost. We are happy to walk through it with you against your actual numbers.
Frequently Asked Questions
What counts as an urgent shift?
In this data, any open shift posted less than 24 hours before it starts. Most come from same-day call-outs or census jumps. Across the 90 days, one in four postings fell inside that window.
Why does fill rate drop so sharply inside 24 hours?
Time, more than willingness. A shift posted a week out can be seen and claimed by anyone who is off that day. A shift posted at 0540 has to reach a qualified person who is awake, free, and close enough to arrive by 0700. Fewer people ever see it, and fewer of those can act.
Does a higher incentive fix it?
It helps, and the right amount matters. In this dataset the urgent shifts already carried the highest incentive and still filled least, which says money alone is not enough once the window has narrowed. The incentive has to reach enough of the right people to matter.
Is the answer just to post shifts earlier?
It is the cheapest first step. Approved PTO, known vacancies, and predictable census swings can be posted days ahead, and every day of lead time moves a shift up the fill curve. Same-day gaps will still happen, which is why the urgent window needs its own approach.
How is this different from a staffing agency or float pool?
Both cover a shift after the internal option has failed, at outside rates. This approach puts the open shift in front of the hospital’s own qualified employees first, on their phones, so the shift is covered by people already on payroll before it goes outside.