Every long-term care and senior-living community has a rhythm. Hospital discharges ebb and flow, families make housing decisions around holidays and the school calendar, and weather affects both admissions and illness. Yet many operators plan staffing and marketing as if every month will look like the last one.
Your own historical census data already contains most of what you need to see these patterns. This post explains how to use monthly numbers to plan, without needing a data science team.
Pull monthly figures for as many years as you can reliably get, ideally at least three. For each building, gather:
Average daily census or occupancy by month
Admissions and discharges by month
Inquiries, tours or referrals received, if you track them
Admission sources, such as hospitals or home, if recorded
Length of stay or discharge reasons, if available
Check definitions first. If the way occupancy was counted changed partway through, note it so you do not mistake a change in method for a change in demand.
Plot census by month for each year on a single chart, with months on the horizontal axis. Look for months where the lines rise or fall together. A consistent dip in one season suggests a seasonal effect. A single odd year is likely an event, such as a local closure or an illness outbreak that restricted admissions.
For each month, calculate how far it sits above or below that year's average, and then average those differences across years. This gives a rough "seasonal index" for each month. It is simple, and you can do it in a spreadsheet.
If your census has been climbing over several years, the months at year end will look higher partly because of growth. Compare each month to the same month a year earlier, or to a moving average, to avoid confusing growth with seasonality.
Without assuming anything about your market, common influences to investigate include:
Hospital discharge volumes, which can be heavier in certain seasons
Illness seasons, which affect both admissions and temporary holds on admissions
Holidays, when families may delay or accelerate decisions
Weather, which can affect travel, tours and discharges
Local events, such as a nearby community closing or opening
Ask your admissions and clinical staff what they notice. Their experience often explains what the numbers show.
Once you know which months tend to run higher or lower, plan ahead:
Hiring and onboarding: Start recruiting early enough that new staff finish orientation before busier months.
Agency and overtime: Budget for the months when census typically peaks or when sick calls tend to rise.
Vacation and time off: Spread approvals so coverage is adequate in expected high-demand periods.
Supplies and dietary: Adjust ordering to expected census.
Keep a margin of safety. A forecast is an informed guess, not a promise.
Marketing works best when it arrives before demand, not after.
Increase outreach to referral sources, such as hospital discharge planners and physicians, ahead of historically slower months.
Schedule community events, open houses and digital campaigns to build tour traffic before seasonal peaks.
Track inquiry-to-admission conversion by month, since the lag between interest and move-in matters.
Review which sources bring the most admissions in each season, and shift effort accordingly.
Small numbers mislead. In a small building, one admission or discharge changes the percentage noticeably.
Do not overfit. Three data points per month do not prove a rule. Treat patterns as hypotheses and check them as new months arrive.
Update regularly. Compare actual census to your forecast every month and adjust.
Account for known changes. A renovation, new competitor or change in payer mix can break past patterns.
Census and admissions data is operational, but if you drill into resident-level detail it may contain protected health information. Report in aggregate where you can, and limit access to resident-level data to those who need it.
Add a seasonal view to your leadership dashboard and review it quarterly with administrators and marketing leads. UnityCare IT helps operators pull census, admissions and referral data from their systems into one reporting view, so patterns like these are easier to see and act on.
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