Estimate telehealth visit wait time using operational inputs you can adjust for your own telehealth visit service. The calculator shows the main planning result alongside capacity, time, or workload details so you can spot the assumption driving the answer. Use recent local data and compare more than one scenario before changing schedules, rooms, staffing, or booking rules.
The demand-pressure adjustment is a transparent planning approximation, not a full queueing simulation.
The estimate combines the visible queue with a utilization adjustment. It becomes unreliable near full capacity, where small changes and random variation cause large delays.
Use observed arrival and service-time distributions for formal queueing or simulation work. Triage, priority cases, scheduled batches, interruptions, and abandoned waits are not modeled.
With 18 arrivals per hour, 5 servers, 14-minute service, and 4 patients ahead, capacity is 21.43 patients per hour. Base wait is 14 minutes; after the demand-pressure adjustment, the estimate is 88 minutes.
When arrivals nearly equal service capacity, there is little spare time to absorb random delays, so queues can persist and grow.
Average arrivals meet or exceed average hourly capacity. Under sustained conditions, no finite steady wait can be estimated with this model.
Enter only patients who must be completed before the new arrival can start. Include active patients if they occupy every relevant server.
The arithmetic average is more appropriate for capacity. A median can understate the effect of unusually long visits.
No. It is an operational planning estimate and does not account for triage, emergencies, priority rules, or individual care needs.
| Input | Meaning |
|---|---|
| Planning period | Keep every workload and time input aligned. |
| Average time | Use a weighted mean when case types differ. |
| Productive time | Exclude breaks and unavailable time consistently. |
| Scenario check | Test typical and peak conditions separately. |