Healthcare is one of the sectors where the gap between perceived profitability and actual profitability is widest. Dental clinics are no exception. Revenue per treatment is visible. Costs per treatment are, in most cases, a mystery.

This case study describes how a dental clinic group applied Time-Driven Activity-Based Costing to gain visibility into treatment-level profitability, with results that reshaped their pricing, scheduling, and operational strategy.

The Challenge

The clinic group operated multiple locations with a range of dental treatments, from routine check-ups and cleanings to complex procedures like implants, orthodontics, and oral surgery. Revenue was growing steadily, but margins were under pressure. Management suspected that some treatments were underpriced, but without detailed cost data, they could not confirm which ones or by how much.

Their existing financial reporting was structured around traditional accounting categories: staff costs, materials, rent, equipment depreciation, and administrative overhead. This told them their total costs per clinic per month, but nothing about costs per treatment, per dentist, or per patient type.

The core question was straightforward: which treatments are actually profitable, and which are consuming resources without generating adequate returns?

The Approach

The team applied a TDABC methodology to build a cost model that traced every euro of expenditure to specific treatments. The process followed a structured path:

Resource pool identification: All clinic resources were grouped into pools. Dentists, hygienists, dental assistants, reception staff, treatment rooms, equipment, and shared facilities each formed a distinct resource pool with a calculated cost per minute.

Time equation development: For each treatment type, time equations were built to capture how long each resource was engaged. A routine cleaning, for example, might require 20 minutes of hygienist time, 10 minutes of assistant time, 5 minutes of reception time, and 30 minutes of treatment room occupancy. A complex implant procedure would have an entirely different resource profile.

Material cost assignment: Direct materials (dental supplies, prosthetic components, consumables) were assigned to treatments based on actual usage data from the clinic’s inventory system.

Model calculation: With resource rates and time equations in place, the model calculated the full cost of each treatment type, incorporating all direct and indirect costs.

Results: Iteration 1

The first iteration of the model produced results that challenged several long-held assumptions.

Routine treatments were more profitable than expected. Standard check-ups and cleanings, often seen as low-value services, turned out to have strong margins. They used lower-cost resources efficiently, had predictable time profiles, and generated consistent throughput.

Some complex procedures were barely breaking even. Certain high-revenue treatments, including some orthodontic cases, had much thinner margins than anticipated. The combination of specialist dentist time, extended treatment room occupancy, multiple appointments, and higher material costs eroded the apparently attractive revenue.

Overhead allocation revealed hidden subsidies. When shared costs (reception, administration, facility costs) were properly allocated based on resource consumption, it became clear that some treatment categories were being subsidised by others. The clinic’s most popular treatments were effectively funding underpriced complex procedures.

Refinement: Iteration 2

The first iteration raised new questions. The team refined the model with additional granularity:

Patient type segmentation: Not all patients consuming the same treatment have the same cost profile. First-time patients require more administrative time. Patients with complex medical histories require longer consultations. The model was adjusted to capture these variations.

Appointment efficiency analysis: The team analysed gaps between appointments, no-show rates, and turnaround time between patients. These “hidden” capacity costs were significant, particularly at certain locations where scheduling practices led to 15-20% unused treatment room time.

Multi-visit treatment costing: Treatments spanning multiple appointments (orthodontics, phased implant procedures) were costed across the full treatment cycle rather than per individual visit, giving a true picture of total cost and profitability per case.

Outcomes

The detailed profitability data enabled several concrete actions:

Pricing adjustments: Treatment prices were revised based on actual cost data. Some procedures received meaningful price increases that had been overdue for years. Others, particularly high-margin routine treatments, were kept competitive to maintain patient volume.

Schedule optimisation: Clinics adjusted scheduling templates to reduce dead time between appointments and ensure high-cost resources (specialist dentists, surgical rooms) were utilised more efficiently.

Treatment mix strategy: Marketing and patient communication were adjusted to promote treatments with the best margin profiles, while underperforming procedures were reviewed for operational improvements or price corrections.

Cross-location benchmarking: With consistent cost data across locations, management could compare operational efficiency between clinics and identify best practices for wider adoption.

The overall impact was a measurable improvement in profitability without a corresponding increase in patient volume. The revenue was already there. The clinic group simply needed the cost visibility to manage it properly.

Lessons for Healthcare Providers

This case illustrates a pattern common across healthcare: revenue is visible but costs are opaque. Whether you run a dental practice, a physiotherapy clinic, or a medical imaging centre, the same dynamics apply. Some services subsidise others. Some patient types are far more expensive to serve than others. And without treatment-level cost data, pricing and operational decisions are based on intuition rather than evidence.

TDABC is particularly well-suited to healthcare because clinical processes are structured and repeatable. Time equations can accurately capture the resource profile of each service, and the results are immediately actionable.

By Miguel Guimaraes, Partner at Cost and Profitability Consulting and Co-Founder of CostCTRL

Running a healthcare practice and want to understand your true treatment profitability? Get in touch for a confidential discussion, or visit our events page to learn about upcoming workshops.