Key takeaways
- Over 90 days, employees in three departments at a health system in Illinois completed 361 shifts, the equivalent of 3,700 hours of patient care that did not go to overtime or outside labor.
- Annualized, that is 15,200 hours, equal to 7.3 full-time positions of coverage, produced by people already on the payroll.
- Nobody was hired, trained, or contracted to make it happen. Sixty-four percent of active users logged in without training, and every employee who logged in once came back, again and again.
The series so far has been about rates: what share of shifts filled, how fast, at what incentive, in which roles. This piece is about volume. Rates show performance. Volume shows impact. Here’s what that translated to over a single quarter.
The numbers
From mid-May to mid-August, three departments at a health system in Illinois posted 1,389 open shifts for their own staff to pick up. Employees accepted 361 of them. Those 361 shifts added up to 3,748 hours of care coverage.
Every one of those hours was a shift that would otherwise have been covered by overtime, incentive pay layered on overtime, an outside order, or not covered at all. None of them required a new hire, a contract, or a recruiter.
Annualized, those 90-day results equal about 15,200 hours of internal coverage across the three departments. At 2,080 hours per full-time employee, that is 7.3 FTEs of coverage the hospital already had on staff and simply could not access before.
For context, the 2026 NSI National Health Care Retention and RN Staffing Report puts the average hospital at 43 unfilled RN positions and 56 to 102 days to recruit an experienced nurse. Three departments closed the equivalent of seven of those positions’ worth of hours in a quarter, without a single hire.
What it took to get there
The instinct is to assume a result like that needs an extensive rollout: training sessions, a change management plan, an IT project. It doesn’t.
The system was live in 72 hours. Sixty-four percent of users got started without any training. Every employee who logged in once came back. Sixty-eight percent of returning users took an action, and more than half explored the full platform functionality on their own. At the end of the pilot, 78% of surveyed staff said they were more satisfied with how they managed shifts using Gigly.
The workflow itself was not complicated. An open shift that used to sit on a schedule board or travel by phone tree now showed up on the employee’s phone. Picking it up took a simple tap. The 361 shifts were not the result of a new staffing program. They were the result of making posting shifts less time consuming for managers, and making open shifts easier for the right employees to see and act on.
What the volume does not tell you
Three things are worth keeping in mind when interpreting the numbers.
It is three departments, not an entire hospital. The 7.3 FTEs are what three units produced. Scaling it to a building is arithmetic, but it is arithmetic on an assumption: that other departments behave like these three. The role data says they will not all behave the same way, which is the argument of The Urgent-Shift Gap Is a Role Problem, Not a Hospital Problem.
It is coverage, not headcount. The 7.3 FTE figure simply converts the hours covered into a familiar unit of measure: the equivalent of 7.3 full-time positions’ worth of hours. It does not mean the hospital avoided hiring 7.3 people. Those hours still needed to be covered; Gigly helped source them from existing staff rather than relying on overtime or outside labor.
The exciting part is that this is the floor, not the ceiling. Most of the 1,389 posted shifts did not fill, and 309 that did not fill were posted inside 24 hours, where the fill rate was lowest. The lead-time data says that posting known gaps earlier moves shifts into the part of the curve where they fill at 38% instead of 11%. That means the 361 represent what these departments achieved without yet optimizing when shifts get posted.
What to do with it
Put your own number against it. Start with three departments and look at the last 90 days of shifts covered through overtime or outside labor. Add up those hours, then estimate how many could realistically have been picked up by existing staff. Annualize that number by multiplying by four, then divide by 2,080 to express the opportunity in FTE-equivalent coverage.
That gives your finance team a clearer way to see how much premium-priced coverage may already be available within the workforce you have.
To translate those hours into dollars, use the cost comparison here.
Where we come in
Gigly is how the Illinois system reached its own staff. Employees see open shifts on their phones and pick them up, before those shifts turn into overtime or outside labor. The 361 shifts and 3,748 hours above are the first 90 days across three departments, with no hire, no contract, and no IT project. This is the last of six pieces built on that dataset. The first, Hospitals Have Two Open Shift Problems. They Manage Them Like One., is the place to start if you have not read the others.
If you want to see what your three departments could produce, pull the overtime and outside-labor hours for any three units over the last 90 days. We are glad to walk through it with you against those numbers.
Frequently Asked Questions
How much coverage did the pilot produce?
Over 90 days, three departments at a health system in Illinois covered 361 open shifts and about 3,700 hours of care with their own employees. Annualized, that is roughly 15,200 hours, or 7.3 full-time positions of coverage.
Does 7.3 FTEs mean the hospital avoided hiring seven people?
No. It is hours of coverage expressed in full-time equivalents, so a finance team can compare it to what those hours would have cost at overtime or outside rates. It is not a headcount reduction.
What did it take to roll out?
The system was live in 72 hours. Sixty-four percent of active users got started without training, and every employee who logged in once came back. No new hires, contracts, or IT integration were required.