Walk-in patients arrive when they decide to, so any single hour at an urgent care is hard to call. The aggregate is different. Across months of visits, walk-in volume follows patterns by hour, day and season that are regular enough to schedule against.
That data is already in your EMR or practice management system, timestamped by arrival. Walk-in volume forecasting turns it into a model of expected demand that's detailed enough to make staffing decisions shift by shift. This guide explains how to build the model and how to use it to size your locum coverage.
Why Experience Alone Isn't Enough for Walk-In Volume Forecasting
A medical director who's spent years at a location usually has an accurate sense of which shifts run hot and which run light. The limits are resolution and consistency.
Memory holds the typical Tuesday afternoon and the usual Saturday rush. It anchors on memorable shifts, like the night the waiting room was full at 8 PM on a Wednesday, and it underweights the slow ones. It also doesn't transfer: a feel for one location's volume may not apply to a second site with a different catchment population.
A forecast built from data can be handed to a new operations manager, applied across sites, and checked when a staffing decision has to be justified. It also shows the specific hours, days and seasons where coverage is reliably out of line with demand.
The Visit Data You Already Have
A visit log export usually includes these fields at minimum:
- Arrival timestamp. The date and time the patient arrived or registered.
- Visit count. Aggregated from individual records.
- Chief complaint or acuity category. If it's captured consistently at registration.
- Discharge timestamp. This lets you calculate time in the clinic.
If your system also captures door-to-provider times, include them. Provider hours from payroll or scheduling records let you normalize volume by actual coverage, which gives you patients per provider hour (PPH).
Pull at least 90 days of data. Twelve months is better. Twenty-four months lets you check that seasonal patterns repeat from one year to the next.
Building the Walk-In Volume Curve
The goal is a demand heatmap: a grid of expected visit volume for each combination of hour of day and day of week, with seasonal adjustments on top.
Step 1: Hour-of-Day Profile
Aggregate all visits by arrival hour across your data window. Calculate the average visits and the standard deviation for each hour.
The standard deviation tells you as much as the average. An hour with high average volume and low deviation is predictable, and you can staff to it with confidence. An hour with moderate volume and high deviation is variable, and it calls for flex coverage or a deliberate buffer.
Step 2: Day-of-Week Adjustment
Calculate the average daily visit count for each day of the week. Express each day as an index against your overall daily average, where 100 is average and 130 is 30% above it. The pattern depends on the market. A location near office parks may peak early in the week, and a location in a residential area may peak on Saturday morning.
Multiply your hour-of-day baseline by the day-of-week index. That two-dimensional grid is the core of the forecast.
Step 3: Seasonal Indexing
Group your data by month and calculate a monthly index: each month's average daily visits against your annual average. Flu activity in the United States usually peaks between December and February, so expect respiratory illness to raise your winter indices. Summer can bring more injuries.
If your data covers more than one year, compare the monthly indices across years. An index that holds steady is reliable enough to forecast with. One that swings from year to year may reflect something other than season, such as a new competitor, a demographic shift in your catchment area, or a new occupational health contract.
Step 4: Local Event Calendar
Some spikes follow the calendar and not the season. Back-to-school deadlines for physicals and vaccinations can raise volume at locations that serve families. Local events like races and festivals can raise injury visits.
These show up in your data as outliers from the day-of-week model. Match them against actual event dates to confirm the cause, then add them to your forward forecast as adjustments.
Turning the Volume Forecast Into Staffing Decisions
The forecast tells you how many patients to expect in each window. To turn that into a staffing decision you need one more number: your target PPH. Urgent care staffing ratios covers how to set it. The core formula is:
Providers needed = Expected hourly visits ÷ Target patients per provider hour (PPH)
A center targeting 2.5 PPH that forecasts 10 patients between 9 and 10 AM needs 4 providers in that hour. A forecast of 4 patients between 6 and 7 AM works out to 1.6. Round up to 2 or down to 1, depending on your opening protocol and minimum coverage requirements.
Applied to every hour in the heatmap, this gives you a demand-adjusted staffing template: how many providers each shift needs to meet expected demand at your target throughput. When your actual schedule sits above the template, you're paying for overstaffing. When it sits below, you have a coverage gap you can measure. The same template works for RN and MA coverage, using the throughput measure you track for those roles.
Finding the Locum Gaps in the Forecast
The scheduling question is where your permanent coverage falls short of the template, and for how long. The shortfall concentrates in a few kinds of windows.
Peak windows where permanent staff can't flex further. A provider who's already working a full schedule can't absorb the extra Saturday morning demand the forecast shows. That's a locum gap, and asking more of permanent staff doesn't close it.
Seasonal surges. Respiratory illness season is the most visible one. Summer injuries, back-to-school illness and year-end PTO among permanent staff create others. Your seasonal index shows these windows months ahead.
Unplanned absences. Look at your historical call-outs by day of week. If they cluster on particular days, treat those days as a coverage risk in the model.
Visit-type gaps. If your data includes chief complaint categories, look for visit types that concentrate at certain times, such as pediatric visits after school or occupational health visits on weekday mornings. Those windows may need coverage with a particular skill set.
Evenings and weekends deserve a close look. They're hours when permanent staff are more likely to have personal obligations, so a sustained demand peak there is harder to cover from your own roster.
Building a Forward-Looking Locum Schedule
With a staffing template and a map of the gaps, you can decide how many locum shifts you need, in which windows, and how far ahead to post them.
Work backward from the time it takes you to fill a shift. Run the forecast on a rolling 30-day forward window so that gaps show up while there's still time to fill them. Post shifts in known high-demand periods earliest.
Separate structural gaps from variable ones. A structural gap shows up in the template every week, because your permanent roster is sized below peak demand. It's best filled by a locum clinician who wants recurring shifts at your facility. A variable gap appears when call-outs, vacations or a volume spike push demand above your capacity, and it needs clinicians who are already credentialed with you and can cover on short notice.
Track forecast accuracy and adjust. Your first forecast won't be exact. Compare actual visits with the model's predictions each month, and update an index when it's wrong in the same direction more than once.
The Cost Case for a Precise Forecast
Understaffing is the visible cost. Overstaffing is easier to miss.
Take a center that schedules one extra provider on every shift as a buffer against unexpected volume. The forecast shows which shifts use that buffer and which don't. Every buffer shift that goes unused is cost you can remove without touching the shifts that need it.
The opposite error is a coverage gap: longer door-to-provider times, and patients who leave before they're seen. A forecast doesn't remove gaps. It shows which windows justify coverage in advance and which are already covered.
A Starting Point if You Don't Have a Model Yet
You don't need special software. A 90-day visit log from your EMR and a spreadsheet with pivot tables will produce a usable heatmap.
- Export visit records with arrival timestamps for the trailing 90 days.
- Build a pivot table with day of week as rows, hour of day as columns, and average visit count as values.
- Calculate the standard deviation for each cell to find the high-variability windows.
- Overlay actual provider hours from your scheduling records on the same grid.
- Flag every cell where scheduled coverage differs from the calculated need by more than 0.5 providers.
The flags are your locum scheduling list for the next quarter. Repeat the analysis as new data comes in. Before a locum provider covers one of those shifts, urgent care locum orientation covers what to send them.
Rediworks is a self-service marketplace for urgent care locum shifts. Once your forecast shows which shifts need coverage, you can post the clinician type, dates and shift details yourself. Sign up to post shifts.