Overtime is one of the first numbers a CFO or administrator looks at when labor costs rise. It is also one of the easiest to misread. A high overtime figure does not tell you why it is high, and responses that ignore the cause, such as simply asking managers to cut hours, tend to backfire.
Better decisions begin with a breakdown. This post outlines how to use payroll and scheduling data to understand where overtime comes from and what to do about it, in a way that supports both staff and the budget.
Overtime has many causes
Overtime shows up for very different reasons:
- Vacancies: open positions covered by existing staff.
- Call-offs and absences: unplanned gaps filled at the last minute.
- Scheduling design: shifts that create predictable overruns, such as long handoffs.
- Census changes: higher acuity or higher occupancy than the schedule anticipated.
- Timekeeping issues: missed punches, late clock-outs or unreviewed edits.
- Orientation and training: time needed for new staff that was not planned.
Each calls for a different solution. Treating them all the same wastes effort and can frustrate people.
Break it down
By department and role
Look at overtime hours by nursing, dietary, housekeeping, therapy and maintenance. Within nursing, separate aides, LPNs and RNs. Concentrations often reveal where vacancies or workload are greatest.
By shift and day of week
Overtime clustered on weekends or nights suggests coverage patterns. Overtime spread evenly may point to general understaffing or scheduling design.
By individual employee patterns
Be thoughtful here. A small number of employees working most of the overtime can indicate burnout risk and fairness concerns. The question is whether the workload is sustainable, not who to blame.
By cause code, where possible
If your system allows tagging overtime by reason, such as call-off coverage or vacancy, the picture sharpens. Even a lightweight tagging practice helps over a few months.
Metrics to track together
Overtime alone is not enough. Pair it with:
- Hours per resident day, so you know whether total staffing is aligned with census.
- Agency or contract labor use, since it can substitute for overtime and carries its own cost.
- Turnover and open positions, which drive coverage needs.
- Call-off rate, by shift and department.
- Schedule versus actual hours, to see where plans diverge from reality.
From insight to action
Once you know the causes, responses can be targeted:
- If vacancies drive overtime, prioritize recruiting and retention for those roles, and consider whether schedule flexibility could help attract candidates.
- If call-offs cluster on certain shifts, explore reasons with staff, from transportation to scheduling preferences.
- If scheduling design creates predictable overruns, adjust shift start times, break coverage or handoff practices.
- If timekeeping errors are a factor, tighten review routines and provide clear guidance.
Keep staff in the conversation
Frontline employees often understand why overtime happens better than any report does. Sharing the data with unit managers and inviting their interpretation builds trust. It also helps ensure that cost goals do not come at the expense of resident care or staff wellbeing, since persistent overtime can contribute to fatigue and turnover.
A hypothetical example
Picture a hypothetical 110-bed building where overtime is concentrated among night-shift aides on weekends. Reviewing the data, the team sees that two open positions and a pattern of weekend call-offs are the main drivers. Rather than pressuring managers to reduce hours, they offer a weekend-focused schedule option to attract applicants and add a modest on-call incentive. Over time, the overtime pattern eases because the cause was addressed.
Make it a regular review
Overtime deserves a recurring slot in operations meetings, ideally weekly, with simple trend views by department and shift. Short, regular reviews catch changes early and build habits of looking for causes.
CarePulse Analytics combines payroll, scheduling and census data into labor dashboards that update automatically. If you would like to see how your overtime breaks down, a demo using your own data is a practical place to start.