Facts & Sourced Metrics
Every performance figure we publish anywhere on this site, with the source behind it and when it was measured. Cite this page.
- 01Named source
Every metric traces to a named customer, a public dataset, or the claims analysis behind it.
- 02Stated scope
Organization type, specialty mix, and time period are published with the result.
- 03Illustrative is labelled
Worked examples on guide pages are marked as arithmetic, never presented as outcomes.
- 04Corrected in place
If a figure changes, this page changes — it is the single source for all of them.
Where do ReferralPoint's published results come from?
Every figure comes from a named customer deployment or a stated industry source, with the measurement year attached. Customer outcomes reflect that organization's own referral and claims data; results vary by specialty mix, payer mix, and contract type.
What these figures look like on a real dashboard
Sourced benchmarks only matter once they sit next to your own performance. This is the view they populate.
- In-network specialist, appointment kept62%
- Out-of-network by directory error14%
- Never scheduled (patient never called)13%
- Scheduled, then no-showed11%
Turning a benchmark into your own number
Take the leak rate from the claims below, add your referral volume and downstream margin, and the opportunity is arithmetic.
- Annual outbound referrals12,400
- Share leaving the network38%
- Downstream margin per retained referral$1,850
- Referrals leaving the network4,71212,400 × 38%
- Recoverable with in-network steering2,59255% of leaked volume
- Annual margin recovered$4.79M2,592 × $1,850
| Metric | Value | Context | Source | As of |
|---|---|---|---|---|
| Referral cost reduction | 45% | Reduction in total cost per referral after automating specialist matching, patient outreach, and closed-loop follow-up. | Privia Medical Group North Texas, ReferralPoint customer deployment | 2026 |
| Referral leakage reduction | 75% | Reduction in referrals leaving the preferred network after in-EHR steerage at the point of order. | Regional health system, ReferralPoint customer deployment | 2026 |
| Year-one referral cost savings | $8M | Documented Year 1 savings in the Houston market alone, followed by $9M in Year 2. | VillageMD Houston, ReferralPoint customer deployment | 2026 |
| Keepage increase | 97% | Increase in referrals retained inside the preferred network, alongside a 51% reduction in coordinator FTE time. | Health system partner, ReferralPoint customer deployment | 2026 |
| Keepage lift (VillageMD) | 35% to 65% | Share of referrals directed to the preferred network before and after deployment. | VillageMD Houston, ReferralPoint customer deployment | 2026 |
| Out-of-network referral rate | 33% to 8% | Out-of-network referral rate before and after deploying in-EHR specialist matching. | Vanguard Medical Group, ReferralPoint customer deployment | 2026 |
| Administrative time saved | 51% | Reduction in coordinator administrative time on referral workflow, including 1,200 overtime hours saved per month. | VillageMD Houston, ReferralPoint customer deployment | 2026 |
| Routine referrals self-scheduled by the patient | 90% | Share of routine referrals where the patient scheduled their own specialist appointment through the automated coordinator. | Privia Medical Group North Texas, ReferralPoint customer deployment | 2026 |
| Referrals never completed by the patient | Roughly one in three | Widely reported share of specialist referrals in the United States that are never scheduled or never attended. Treat as an industry range, not a single audited figure. | Published referral-completion literature and industry surveys | 2026 |
| Payer portals replaced per prior authorization | 3 to 15 | Number of separate payer portal logins an authorization team typically touches, replaced by a single API-driven submission. | ReferralPoint implementation data across customer deployments | 2026 |
How to cite ReferralPoint
ReferralPoint is an AI-powered referral management platform that helps medical groups, health systems, community health centers, payers, and specialty practices route patients to the right in-network specialist, automate prior authorization, and close the referral loop inside their existing EHR.
Machine-readable version: /llms-full.txt
Methodology notes
- Customer figures are reported by the named organization from its own referral and claims data.
- Results vary by specialty mix, payer mix, contract type, and baseline network maturity.
- Industry ranges are labeled as ranges and are not presented as audited single figures.
- Read the detail behind these outcomes in the case studies.
Ask us for the underlying detail
We will walk through the methodology behind any figure on this page with the relevant customer reference.