CostCtrl Demo: Healthcare Profitability Dashboard

One dataset. Three answers about where you make money.

Everything below is computed live from the embedded operational data of a fictional EUR 28M multi-specialty clinic group: care episodes, visits, exams, minutes and capacity. Change the filters above and every engine recalculates.

Margin cascade: revenue to EBIT (TDABC view)

Where 100 cents of revenue go. Unused capacity, including every no-show slot, is shown as its own block, not buried in episode costs.

Practical capacity set at 85% of theoretical, following Kaplan and Anderson's TDABC convention, so episode costs are not inflated by the cost of idle resources.

The method-divergence problem

Same clinics, same ledger, different truths.

Monthly trend: revenue, TDABC profit and physician utilisation

Seasonality is real: an August holiday trough and a winter push. Capacity cost, though, is fixed every month.

Traditional vs ABC vs TDABC: who is lying to you?

All three engines allocate the same operating cost pool. They just disagree, sometimes by 30 to 46%, about which episode caused it. Research on costing-system distortion reports errors of this magnitude on the extremes (IJISR).

How each engine thinks

The plain-language version.

Traditional: one blended cost per completed visit absorbs all operating cost; SG&A is spread as a flat % of revenue. Complexity is invisible: a 10-minute repeat consultation "costs" the same as a day-surgery work-up with imaging, pre-authorization and recovery monitoring.

ABC: cost pools with drivers (per booking, per exam, per claim, per nursing minute). Better, but every driver is an average: an MRI counts the same as an X-ray, an Insurance B claim the same as a self-pay receipt, and all capacity cost is pushed onto episodes, so idle time silently inflates every rate.

TDABC: each resource group gets a capacity cost rate in EUR per minute; time equations estimate the minutes each visit, exam, procedure and claim actually consumes. Unused capacity, including every no-show slot, is isolated instead of allocated.

Two real time equations from this model

This is what replaces allocation keys. Every coefficient is minutes.

consultation_min = 4..25 (base, by specialty)
+ 10 if first_visit
+ 15 if complex_case
+ setup + duration if procedure (12..25 + 30..55 by class)

episode_admin_min = 2.5 × bookings
+ 10 if pre_authorization
+ 2..9 × claims (billing, by payer)
+ 6 per no-show rebooked
+ 20 per denied claim

Rates used here: Physicians 1.91 EUR/min, Diagnostics & Imaging 1.39, Nursing 0.80, Facilities 0.64, Front office 0.50 (resource group cost / practical capacity minutes).

Cost and margin by engine

Cost per episode and margin under each engine. Arrows show rank moves from Traditional to TDABC profitability.

Traditional margin vs TDABC margin

On the diagonal, the methods agree. Off it, your tariffs were negotiated on fiction. Points below the line are subsidised by the ones above it.

Why they differ: a blended cost per visit punishes visit-heavy care (consultations, physiotherapy, chronic programmes) and flatters procedure-heavy care (day surgery, cardiology). ABC catches exams and claims but averages away payer behaviour and no-shows. TDABC prices the minutes.

Under the hood: the full model card

Every parameter behind the three engines. In an engagement these come from your GL, payroll, scheduling system and billing extracts.

Capacity cost rate = resource group cost / practical minutes (85% of theoretical, Kaplan & Anderson). No-shows: a missed appointment still consumes the booked physician slot plus 6 minutes of rebooking, which is why empty chairs are expensive and invisible to a blended cost per visit. Pathway costing follows the peer-reviewed approach we published.

The whale curve: a few pathways carry everyone else.

Sort payer-line pathways from most to least profitable (TDABC) and accumulate. The curve climbs far above 100%, then the tail gives it back. Kaplan's Kanthal case found 225% at the peak; HBS research typically finds the top 20% generate 150 to 300% of profits while the bottom tail destroys 50 to 200%.

Cumulative profit curve (TDABC)

Hover a point for its P&L. Click to pin the card and see the three Kaplan levers for the tail.

Detail: select a point on the curve

Full TDABC P&L of the hovered or pinned item.

Hover or click any point on the whale curve.

Fixing the tail: the three levers

Kaplan's playbook for loss-making pathways. Walking away from a contract is the last resort: its volume still absorbs capacity cost.

1. Process lever

Reduce the minutes: SMS reminders and digital intake, pooled scheduling and group sessions for therapy, direct-access imaging slots, batched pre-authorizations instead of one call per episode.

2. Relationship lever

Renegotiate with evidence: take the pathway P&L to the payer. Below-cost tariffs, denial rates and pre-auth burden are contract clauses, not weather. Most payers move when shown episode-level cost.

3. Menu-based pricing lever

Price the menu, not the average: no-show fee or booking deposit, out-of-hours premium, admin surcharge for heavy-touch payers, self-pay packages priced on TDABC cost. Behaviour self-selects and the cross-subsidy stops.

Unused capacity: the cost line almost nobody measures.

An IMA field study found only 3 of 63 companies measured the cost of unused capacity. TDABC makes it a standing line item per resource group, following the CAM-I capacity model: theoretical, practical, used, idle.

CAM-I capacity bridge by resource group

Theoretical minutes, the 15% practical haircut (breaks, training, cleaning, maintenance), productive use, and paid-for idle time.

Only 3 of 63 companies in the IMA capacity study measured the cost of unused capacity. Everyone else pays for it and then hides it inside episode costs.

Utilisation of practical capacity by month

Physicians are the constraint in the winter push; imaging and support carry structural slack all year.

EUR of idle capacity, by resource group and month

Resource cost is fixed monthly; idle EUR = (practical minutes - used minutes) x capacity cost rate. Darker cells burn more cash.

Decision menu for idle capacity: sell it (evening imaging sessions, occupational health contracts), shift training and maintenance into it, delay the next scanner, or accept it as growth headroom, but price care episodes as if it did not exist.

What a consultant would circle in red.

Nine findings, ranked by annual EUR impact, each computed live from this model. Expand any card for the calculation trace, the lever and the first action.

Identified in this model

EUR 0

Sum of the nine sized opportunities below. Not all are additive and not all are fully capturable; even the conservative half typically funds the costing programme many times over.

Ask the model. It computes, it does not improvise.

Type a question about pathway profitability, payer economics, methods, capacity or what-ifs. Answers are calculated live from the demo dataset on this page.

Try these

How this works: this assistant computes answers from the demo model embedded in this page. It is deterministic, not a live AI. In an engagement, the same questions run against your data.

Overview

One dataset. Three answers about where you make money.

Margin cascade: revenue to EBIT (TDABC view)

The method-divergence problem

Monthly trend: revenue, TDABC profit and physician utilisation

Traditional vs ABC vs TDABC: who is lying to you?

How each engine thinks

Two real time equations from this model

Cost and margin by engine

Traditional margin vs TDABC margin

Under the hood: the full model card

The whale curve: a few pathways carry everyone else.

Cumulative profit curve (TDABC)

Detail: select a point on the curve

Fixing the tail: the three levers

1. Process lever

2. Relationship lever

3. Menu-based pricing lever

Unused capacity: the cost line almost nobody measures.

CAM-I capacity bridge by resource group

Utilisation of practical capacity by month

EUR of idle capacity, by resource group and month

What a consultant would circle in red.

Ask the model. It computes, it does not improvise.

Try these

See this with your data in 3 to 6 weeks