Online check-in is how a growing share of urgent care patients arrive — and it's the cheapest demand you'll ever get, because the patient came to you. But a booking isn't a visit. This dashboard follows the full digital funnel from session started, to reservation confirmed, to arrived on-site, to a completed visit, so you can see where online arrivals leak before they reach the exam room.
What this dashboard answers
The funnel page answers it in one picture: of everyone who started an online booking, how many confirmed, how many showed up, and how many completed a visit — with the stage-to-stage conversion rates that show exactly where the drop happens. The channel-and-center page asks where conversion is strong and where it isn't: which centers turn bookings into visits, which channels (the Solv app, your own site, Google) convert best, and how confirmed reservations are trending as patients shift to digital.
The metrics that matter
Booking-to-visit conversion is the bottom line — the share of online starts that became real, billable visits. The no-show / drop count is its mirror: confirmed reservations where the patient never arrived, which is both lost revenue and a held slot you couldn't give to a walk-in. Watching the funnel stage-by-stage tells you whether you're losing people at confirmation (a friction problem) or after confirmation (a wait-time or reminder problem).
Why the data is trapped
The booking funnel lives almost entirely in Solv or your scheduling tool, while the completed-visit record lives in Experity, eClinicalWorks, or athenahealth. Tying a booking ID to the visit it became — the join that lets you compute true booking-to-visit conversion — is exactly the cross-system match the standard reports don't do. Most operators see confirmations in one dashboard and visits in another and never connect the two.
How to read it
Read the funnel first to find the leaking stage, then use the by-channel view to see whether one channel converts far worse than the others (often a sign the channel over-promises or under-confirms). Conversion by center surfaces local execution — reminders, wait times, room readiness. The weekly confirmed-reservations line is your digital-adoption trend; a steady climb means the funnel is becoming your most important arrival path.
The sample on this page uses entirely synthetic booking data — anonymized centers, no real patients or PHI.
Metrics it tracks
| Metric | What it means |
|---|---|
| Online Sessions Started | Count of online check-in / reservation attempts — one row per booking attempt. |
| Reservations Confirmed | Count of booking attempts that became a confirmed reservation. |
| Arrived On-Site | Count of confirmed reservations where the patient actually arrived. |
| Completed Visits | Count of arrivals that became a completed, billable visit. |
| Booking-to-Visit Conversion | Completed visits ÷ online sessions started — the share of digital starts that turned into a visit. |
| No-Show / Drop Count | Count of reservations that were confirmed but the patient never arrived. |
Used by: Marketing-minded owners and ops directors measuring the digital funnel
Frequently asked questions
What is a good booking-to-visit conversion rate for urgent care?
It varies by channel and how aggressively you confirm and remind, but the useful discipline is watching the funnel stage-by-stage rather than chasing a single number. A big drop at confirmation points to booking friction; a drop after confirmation points to no-shows, long waits, or weak reminders.
Does this work with Solv data?
Yes. The funnel is built for the kind of session, reservation, arrival, and visit events a tool like Solv produces, joined to the completed-visit record in your EMR. Export both and use this as a template; the sample here uses synthetic data so you can see the finished shape first.
Why track no-shows on online bookings?
A confirmed reservation that never arrives is lost revenue and a slot you couldn't offer a walk-in. Tracking the no-show / drop count by channel and center shows where reminders and confirmation flows need work, and how much capacity online no-shows are quietly costing you.
Build this report on your own data
Clone this Urgent care template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
Use this as a template →