A referral is only potential revenue until the patient is evaluated and started on a plan of care. Between those points sit scheduling, eligibility, and a window in which the patient can choose a faster clinic or simply not call back. In outpatient rehab, where referral relationships are the lifeblood of volume, letting referrals leak is letting growth leak. This dashboard turns intake from a black box into a managed pipeline.
What this report answers
The Intake Funnel page counts the four stages — referrals received, scheduled, evals completed, and converted to plan of care — shows the drop-off as a funnel, breaks the stage counts by referral source (ortho surgeon, primary care, self-referral, specialist, hospital discharge, workers' comp/attorney), and puts the conversion rates on the chart labels. The Speed & Leakage page focuses on time and loss: referrals lost, average days from referral to eval, days-to-eval by site, lost referrals by source, and a weekly referral-volume trend. Together they answer: how many referrals turn into patients, where do they drop, and how fast do we get them in?
The metrics that matter
The shape of the funnel tells you where to act — a sharp narrowing at one stage (many scheduled but few evals completed, say) points to a specific bottleneck like eval no-shows. Average days from referral to eval is the speed metric that decides whether you win the patient: every day a referral sits unscheduled is a day a competitor or a cold lead can take it. Lost referrals by source shows which referral relationships are leaking, which is exactly where outreach and process fixes pay off.
Why the data is trapped across the systems
Referrals, scheduling, eval completion, and plan-of-care start are recorded at different moments across the intake and clinical modules of Prompt, Raintree, Net Health / Clinicient, WebPT, TheraOffice, Jane, or Fusion. The canned intake report — where one exists — rarely expresses a full received-to-started funnel with days-to-eval and loss by source, and never across multiple sites. Reconstructing the funnel means exporting and modeling the stage flags, which is what this template does.
How to read it
Read the funnel shape first to find the narrowest stage, then read days-to-eval to see whether speed is costing you referrals, and finish on lost referrals by source to know which relationships to shore up. Rising days-to-eval is an early sign demand is outrunning eval capacity. The sample uses fully synthetic, anonymized referral data — no PHI — so you can see the finished layout before bringing your own.
Metrics it tracks
| Metric | What it means |
|---|---|
| Referrals Received | Count of inbound referrals in the period — the top of the funnel. |
| Referrals Scheduled | Referrals that booked an evaluation, via a 1/0 flag. |
| Evals Completed | Referrals whose evaluation was actually completed, via a 1/0 flag. |
| Converted to Plan of Care | Referrals that started a plan of care, via a 1/0 flag — the bottom of the funnel. |
| Referrals Lost | Referrals that never scheduled or no-showed the eval, via a 1/0 flag. |
| Avg Days Referral to Eval | Mean days from referral to eval, computed only over scheduled referrals (0 when not scheduled, keeping the ratio clean). |
Used by: Director of operations and intake coordinator
Frequently asked questions
Why do outpatient rehab referrals get lost before the evaluation?
Referrals leak when they aren't scheduled promptly, when eligibility or authorization stalls, or when the patient no-shows the eval. Every day a referral sits unscheduled raises the odds the patient chooses a faster clinic or disengages — which is why average days-to-eval and lost-referrals-by-source are the metrics intake teams watch most.
What is a good referral-to-eval conversion rate?
It varies by market and referral source, but the value of the funnel isn't a single benchmark — it's seeing the drop-off at each stage (received → scheduled → evaluated → started) so you can fix the narrowest one. A funnel that loses most referrals between received and scheduled points to a front-desk speed problem, not a clinical one.
Why track days from referral to evaluation?
Speed wins referrals. The longer a referral waits to be scheduled and evaluated, the more likely the patient goes elsewhere or doesn't start at all. Rising days-to-eval is also an early signal that demand is outpacing evaluation capacity at a site, before it shows up in lost volume.
Build this report on your own data
Clone this Rehab therapy 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.
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