Charge integrity · charted but never billed · Riverbend Veterinary Group

The industry's survey number

35%

Instinct's State of ER & Specialty Veterinary Care 2024 (n=545) reports completed services left off the final bill fell from 43% to 35%.[1] It is self-reported, single-system, and holds only for the practices running Instinct.

Measured across your PMSes

10.0%

Network-wide missed-charge rate, set-difference per visit. Worst where you see least: legacy AVImark 33.6%, the Instinct ER 14.3%. A single-PIMS tool cannot see the seam between the systems you run.

Completed care that got charted but never billed, across all four systems: $1.69M net recoverable.

$1.99M of charted line value never made it onto an invoice in the window (10.0% of expected), $1.69M of it net of what would not have collected. The gap is concentrated where you can least see it: your Riverbend Specialty Akron (34% missed) — the legacy AVImark site with no native charge capture. Your modern PMS sites sit near 5%. Each dollar is a real subtraction over two columns you can read, not a multiplier. We measure it and hand you the number; the re-bill happens inside your own PMS.

charted (expected)

$19.82M

Σ every charted line · 240d

billed (captured)

$17.83M

Σ what hit the invoice

missed-charge rate

10.0%

gross / expected · network

net recoverable

$1.69M

× 0.85 realization (drawer)

Industry baseline

90%

Shepherd cites a ~90% industry charge-capture estimate.[3]

Shepherd, single platform

96.5%

Shepherd reports 96.5% capture on its own platform across ~90 GP practices.[3] The same study reports 18.1% ACT lift and 17.3% revenue lift for automated-capture practices versus legacy.

Measured, across your PMSes

90.0%

Network capture measured as set-difference per visit across all four PMSes, with the per-doctor and per-site variance Shepherd's single mean hides. Audit trail per visit.

Charge capture by clinician

Clinicians with ≥ 50 visits in the window, lowest capture first. The long-tail outlier is where the recoverable dollar concentrates, not the mean.

Dewey Kuhlman

59.8%found money

Carmine Hane

65.3%

Uriel Braun

66.6%

Elvira Mohr

67.1%

Reid Labadie

68.1%

Roscoe McCullough

70.6%

Jasmine Reynolds

84.5%

Lester Mills

84.6%

Madeline Wunsch

85.4%

Mario Wunsch

85.5%

Scottie Reichert

85.6%

Leif Cummings

87.1%

Lorenzo Harris

87.4%

Sarah Mendez

92.0%

Hobart Koch

92.3%

James Park

92.6%

Calvin Schowalter

92.8%

Nathanial Waters

93.0%

Everett Gutkowski

93.0%

Willis Reichert

93.4%

Angie Bogan

94.7%

Casper Johnston

94.7%

Allan O'Hara

95.1%

Della Satterfield

95.3%

Shepherd's published 96.5% is a single all-practice mean across ~90 GP practices. Central Uplift surfaces per-doctor variance so the operator acts on the long-tail outlier, not the mean.

Charge capture by PMS

One bar per system the operator runs. Capture = 1 − measured missed-charge rate.

cornerstone Riverbend Urgent Care Columbus

95.0%

ezyvet Riverbend Specialty Pittsburgh

92.8%

instinct Riverbend Emergency Cleveland

85.7%

avimark Riverbend Specialty Akron

66.4%

This is the metric Shepherd cannot produce. Single-PIMS practices on Shepherd see one bar. This operator sees four.

Per-site charge integrity · sorted by missed $ desc

Click any column header to re-sort. The spread by PMS is the point: legacy and high-throughput sites catch the least.

Hospital	PMS	Completed visits	Charted $	Billed $	Missed $	Missed %	Net recoverable
Riverbend Emergency Cleveland	`instinct` modern PMS · high-throughput ER	5,958	$5.75M	$4.93M	$820.0K	14.3%	$697.0K
Riverbend Specialty Pittsburgh	`ezyvet` modern PMS · native charge capture	7,081	$8.79M	$8.16M	$637.4K	7.2%	$541.8K
Riverbend Specialty Akron no native capture	`avimark` legacy · no native charge capture	979	$934.1K	$619.9K	$314.3K	33.6%	$267.1K
Riverbend Urgent Care Columbus	`cornerstone` modern PMS · native charge capture	8,972	$4.35M	$4.13M	$219.1K	5.0%	$186.2K

Per-visit evidence (top 50 by gap)

Filtered to: Surgical Services. Clear filter

Click any row to expand the charted-vs-billed split, the false-positive guards, and the SQL behind the gap.

Instinct's 35% figure is survey-reported. The numbers above are measured set-difference per visit with named false-positive guards. Audit trail per row.

Same chart-of-accounts vocabulary, per-visit evidence. Vetsource Data and Insights normalizes data from more than 6,500 practices to the AAHA/VMG Chart of Accounts for industry benchmarking.[a][b] Central Uplift uses the same chart-of-accounts vocabulary and produces per-visit forensic evidence rather than aggregate trendlines.

What this looks like in your stack today. Your PMS catches the charges your team enters into it — Instinct flows charges from the treatment sheet to the invoice inside its own walls, and Cornerstone and ezyVet do the same for the sites that run them. What none of them does is reconcile charted care against what billed across all the systems you run, and your legacy sites have no native charge capture to begin with. A single-PMS audit tool (VetXbill, the in-workflow capture in Shepherd/ezyVet) sees one system; a CPA's annual missed-charge review samples a slice. The query in the drawer subtracts SUM(revenue_cents) from SUM(expected_revenue_cents) per site over the normalized layer — the recoverable dollar is the query output. Copy it, run it on your own warehouse, see which of your sites catches the least.

What we do with it. We measure the gap and hand you a written read: the sites, visit types, and codes with the highest unbilled rate, and the checkout-step change that closes each. The re-bill happens inside your own PMS — we contact no pet owner, build no list, and write nothing back to any system.

Cited context (real, verifiable). Instinct's State of Emergency and Specialty Veterinary Care 2024 (545 professionals) found ~35% of ER/specialty practices leave completed services off the bill, down from 43%; the AVMA puts the industry baseline at ~5–10% of all charges missed. Those frame whether a measured rate is high or low — they are never substituted for the operator's own measured number. The per-line miss distribution in this synthetic dataset is a Central Uplift construct, labeled in the methodology. Press Q for the query this page ran.

SQL · this page ran 3 queries Q to toggle

Per-site charted-but-never-billed (expected − billed)

SELECT h.id, h.slug, h.name, h.pms_source,
      COUNT(v.id) AS completed_visits,
      COALESCE(SUM(v.expected_revenue_cents), 0) AS expected_cents,
      COALESCE(SUM(v.revenue_cents), 0) AS billed_cents,
      COALESCE(SUM(MAX(v.expected_revenue_cents - v.revenue_cents, 0)), 0) AS gross_missed_cents
    FROM hospitals h
    LEFT JOIN visits v
      ON v.hospital_id = h.id
      AND v.visit_date >= ?
      AND v.status NOT IN ('walkout', 'left_ama')
    GROUP BY h.id
    ORDER BY gross_missed_cents DESC

-- Business constant (NOT query output): CHARGE_REALIZATION_FACTOR = 0.85
--   Fraction of charted-but-unbilled $ that would realistically have collected.
--   Central Uplift estimate; in a paid diagnostic this is derived from the operator's
--   own write-off/decline rate and disclosed here.
-- Net recoverable $ = (expected - billed, from the query above) × 0.85.

Per-visit forensic evidence (top 50 by gap)

SELECT * FROM (
  SELECT
    v.id AS visit_id,
    v.pms_source_record_id AS pms_record_id,
    v.visit_date AS visit_date,
    h.slug AS hospital_slug,
    h.name AS hospital_name,
    h.pms_source AS pms_source,
    p.external_ids_json AS external_ids_json,
    p.owner_external_id AS owner_external_id,
    v.expected_revenue_cents AS charted_cents,
    v.revenue_cents AS billed_cents,
    (v.expected_revenue_cents - v.revenue_cents) AS gap_cents,
    (SELECT pr.aaha_vmg_category FROM procedures pr WHERE pr.visit_id = v.id
       GROUP BY pr.aaha_vmg_category ORDER BY SUM(pr.revenue_cents) DESC LIMIT 1) AS aaha_vmg_category
  FROM visits v
  JOIN hospitals h ON h.id = v.hospital_id
  JOIN patients p ON p.id = v.patient_id
  WHERE v.visit_date >= 1759600007115
    AND v.status NOT IN ('walkout','left_ama')
    AND v.expected_revenue_cents > v.revenue_cents
    
) t
WHERE t.aaha_vmg_category = 'surgical_services'
ORDER BY t.gap_cents DESC
LIMIT 50

Charge capture by clinician (per-doctor variance)

SELECT c.id AS clinician_id, c.name_first || ' ' || c.name_last AS clinician_name, h.slug AS hospital_slug,
  COUNT(v.id) AS visit_count,
  CAST(SUM(v.expected_revenue_cents) AS REAL) AS exp_sum,
  CAST(SUM(v.revenue_cents) AS REAL) AS bill_sum
FROM clinicians c
JOIN hospitals h ON h.id = c.hospital_id
JOIN visits v ON v.primary_clinician_id = c.id AND v.visit_date >= 1759600007180 AND v.status NOT IN ('walkout','left_ama')
GROUP BY c.id
HAVING COUNT(v.id) >= 50 AND SUM(v.expected_revenue_cents) > 0

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