Cube vs. Mosaic for a Multi-Hospital Veterinary Group

When DVMs are paid a percentage of their own production, that comp cost scales differently at a hospital with a heavy specialty or surgical caseload than at a general-practice location, because case mix drives both production and cost per case differently. A model using one blended comp ratio across the group will misstate margin at any hospital whose case mix differs meaningfully from the group average. This shows up most clearly when comparing a hospital with an emergency or specialty service line against a general-practice hospital nearby, since the two can look identical on revenue alone while carrying very different comp obligations.

Veterinary pharmacy and surgical supply costs vary by case mix in ways a flat percentage of revenue assumption misses; a hospital doing more orthopedic surgery or emergency stabilization work carries meaningfully different supply cost than one doing mostly wellness visits and routine vaccinations. Track inventory cost against case mix at the hospital level rather than applying a group-wide average, so a hospital's actual margin profile is visible rather than smoothed over. A hospital carrying a large controlled-substance or specialty pharmaceutical inventory also ties up more working capital than a wellness-focused location, which matters for cash planning even before margin comes into it.

If hospitals joined the group at different times, possibly on different practice management systems before being migrated, historical data consistency across locations can be uneven. Before building a consolidated forecast, confirm each hospital's production, revenue, and inventory data are being pulled from a comparable source and time period, since inconsistent historical data will make any forecast, in Cube, Mosaic, or a spreadsheet, unreliable from the start. Budget real time for this normalization step during onboarding rather than assuming either platform's import process will quietly reconcile mismatched historical formats on its own.

If your team already consolidates DVM production, comp, and inventory cost by hospital in a spreadsheet, Cube's approach of syncing that model against practice management and payroll data keeps the calculation where it's understood, with less manual pulling from each hospital's system every month.

Once you're running enough hospitals that a spreadsheet roll-up becomes unwieldy, a dashboard consolidating production, comp, and margin by hospital can save real time for group leadership or an investor. Confirm in a demo that Mosaic can carry different comp formulas and case-mix cost profiles by hospital rather than assuming one group-wide structure applies everywhere.

Jirav's driver-based approach is useful when a group is acquiring additional hospitals and needs the forecast to model each new location's ramp-up and integration timeline separately, rather than assuming an acquired hospital immediately performs at the group's existing average production and margin levels. That ramp period typically runs longer than most acquisition models initially assume, especially when a hospital is also transitioning onto group-standard systems, pricing, and reporting cadence at the same time.

A hospital that takes emergency walk-ins or receives specialty referrals from other clinics in the group will show a different revenue and cost pattern than a pure wellness-and-preventive-care location, and averaging those two hospital types together in one forecast obscures both. Track emergency and referral volume separately by hospital so the model can distinguish a hospital that's genuinely underperforming from one whose case mix simply looks different by design.

Export a quarter of production, comp, and inventory data from two or three hospitals on different vintages within the group and see how cleanly it maps into either tool, since a hospital acquired more recently may still be running on its prior practice management system during a transition period. Ask specifically how each platform handles a hospital mid-migration between systems, since that's a real, recurring scenario for a growing group rather than a rare edge case worth designing around only after it happens.

Test these points before consolidating hospital data:

• Export a quarter of production, comp, and inventory data from two or three hospitals of different vintages within the group.
• Ask how each platform handles a hospital that is mid-migration between practice management systems.
• Confirm each hospital's data comes from a comparable source and time period before you consolidate.
• Check that different comp formulas and case-mix cost profiles can be carried by hospital.
• Verify that emergency and referral volume can be tracked separately by hospital.