This report draws together publicly available data on the operational and commercial performance of dental practices, with a focus on the areas where practices most commonly lose patients, appointments, and revenue without always realising it.
It is designed for practice owners and managers who want a structured way to assess their own performance against industry figures and to understand which improvement areas carry the most financial weight.
The following organisations and publications are cited in this report:
This report is for general information and benchmarking purposes. It does not constitute clinical, legal, or financial advice. Practice results will vary.
Six operational areas where dental practices lose patients and revenue, often gradually and without a single obvious cause. Each tile shows the headline figure for that area.
The Planet DDS 2025 Dental Industry Report, which surveyed approximately 3,400 dental practices internationally, found that no-shows average 7.4% of confirmed appointments and late cancellations average 15.5%. Combined, roughly one in four appointments either does not proceed or is not filled in time to recover the chair time.
These figures are drawn from an international (predominantly US) sample. Australian-specific published benchmarks are limited, but anecdotal evidence from practice management consultants suggests Australian practices sit in a similar range, with some urban markets experiencing higher no-show rates among new patients.
The research consensus, drawn from dental-sector vendor and clinical sources, is that structured automated reminders meaningfully reduce no-show rates. SMS reminders show particularly strong results, largely due to open-rate and response-time characteristics: SMS open rates are broadly cited at approximately 98%, with most messages read within 90 seconds of delivery.
Bars show estimated reduction in no-show rate relative to no reminders. Figures are indicative ranges drawn from dental-sector vendor and clinical literature. Results vary by practice type, patient demographic, and timing of reminders.
An AI agent handles the full reminder workflow without front-desk involvement: sending reminders at configured intervals (e.g. 72 hours, 24 hours, 2 hours before appointment), receiving and logging confirmation replies, flagging non-responses for human follow-up, and filling cancelled slots from a short-notice waitlist. The agent does not make clinical decisions. It manages the coordination layer that front-desk staff typically handle manually, freeing staff for higher-value patient interactions.
Recall (the process of bringing existing patients back for routine preventive appointments) is one of the most reliable revenue streams in a dental practice and one of the most commonly underleveraged. According to Cast Hub benchmarks, dental practices retain approximately 55 to 65% of their patient base through active recall. The remaining 35 to 45% lapse at varying intervals.
Patients who have lapsed are not necessarily lost permanently. The challenge is that reactivation success drops significantly the longer a patient has been inactive.
Indicative ranges based on Cast Hub dental recall and reactivation benchmarks. Rates reflect multi-touch outreach campaigns. Single-contact outreach typically yields materially lower rates. AU-specific published data is limited; international data applied directionally.
The consistent finding is that multi-touch campaigns (a sequence of contacts across SMS, email, and where appropriate phone) materially outperform single reminders. The aggregate reactivation range across all overdue segments, with structured campaigns, is approximately 15 to 35% of the contacted inactive list.
An AI agent can run a continuous recall programme without manual list management. It identifies overdue patients in the practice management system, initiates timed outreach sequences, handles booking replies conversationally (including preferred times and any updated contact details), and escalates unresponsive or complex cases to front-desk staff. The agent prioritises recently lapsed patients, where reactivation rates are highest, automatically.
Workforce availability is now one of the primary constraints on practice revenue in Australia. According to BDO's Australian healthcare benchmarks and the Gorilla Jobs 2025 Dental Recruitment and Salary Survey (both Australian sources), approximately 3 in 4 dental practices report being unable to operate at their intended clinical capacity due to staff shortages, across both clinical and administrative roles.
The front desk is a particular pressure point. Administrative staff handle a high volume of transactional, repeatable tasks alongside genuinely complex patient interactions. This combination leads to task overload, higher error rates during busy periods, and, over time, elevated turnover.
Staffing pressure means the tasks that require human judgement (complex patient concerns, insurance queries, clinical questions directed to a dentist) compete with entirely automatable tasks (appointment confirmation replies, after-hours FAQ, recall reminders). When both land on the same overloaded front-desk team, the lower-complexity tasks consume time that should be reserved for patient-facing value.
The result is not a replacement of front-desk staff. It is the removal of a category of repeatable tasks that currently prevent staff from focusing on the interactions that genuinely require a person.
According to mConsent, which analyses patient-facing consent and communication data across dental practices, cost is the number one barrier to treatment acceptance. This applies to both elective and recommended treatment. Secondary barriers include lack of urgency ("it doesn't hurt yet"), anxiety, and insufficient understanding of the treatment plan.
Treatment acceptance rates vary significantly by practice, patient demographic, and how treatment plans are presented. Practices that provide written treatment plan summaries and payment plan options report materially higher case acceptance than those that discuss treatment verbally only.
The Australian Dental Association's Dental Fees Survey provides the most reliable public benchmark for Australian dental fees. For a standard check-up and clean (items 011 + 114), the national average is approximately $233, with a range of roughly $162 to $309 across practices and states.
Source: Australian Dental Association Dental Fees Survey (AU). Item codes 011 (periodic oral examination) and 114 (scale and clean) combined. Figures represent a national range; individual practice fees vary.
On treatment acceptance, an AI agent can deliver post-consultation follow-up: sending the patient a written summary of the treatment plan, a link to payment options, and a prompt to book. This closes the gap between verbal discussion and decision. On patient value, the agent improves retention by ensuring the next appointment is always booked before the patient leaves and that recall contacts are timely.
Benchmarks are only useful if you know where you sit relative to them. The six metrics below are the ones that matter most for the areas covered in this report. Most practice management systems (PMS) capture the raw data; extracting it in a useful format is the common gap.
| Metric | Benchmark range | Where to find it in your PMS | What to do with it |
|---|---|---|---|
| No-show rate % of confirmed appointments not attended |
Target: below 5% Industry average: ~7.4% (international) |
Appointment reports, filtered by "did not attend" or "no-show" status over a rolling 90 days. | If above 7%, review reminder workflow. Check whether reminders are going to current mobile numbers. |
| Recall retention rate % of active patients who returned within 13 months |
Strong: above 65% Average: 55-65% Concern: below 50% |
Recall reports, patients with last visit date within 13 months as a share of total active patient list. | Segment by how long patients have been with the practice. Recent lapsers (under 6 months) are the priority to contact first. |
| After-hours enquiry capture % of after-hours contacts that result in a booking or response |
Most practices: near 0% With automation: 30-60% |
Phone system missed-call logs plus website contact form or chat logs. Often not tracked at all. | If not tracked, start. The gap between after-hours contacts and bookings is often the single easiest revenue gain to identify. |
| Call answer rate % of inbound calls answered during business hours |
Target: above 85% Common actual: 60-75% |
Phone system or VoIP dashboard. Some PMS systems integrate call logs. | New patient calls that go to voicemail are almost always lost. Monitor Monday mornings and Friday afternoons specifically. |
| Case acceptance rate % of treatment plans accepted and scheduled |
Strong: above 85% Average: 65-80% (varies by treatment type) |
Treatment planning module. Track accepted vs. presented by treatment category if possible. | If acceptance is low, examine whether written summaries and payment options are being offered consistently. |
| Reactivation rate % of lapsed patients who rebook after contact |
With structured outreach: 15-35% Without: typically 5-10% |
Run a report of patients with no appointment in 14+ months. Track how many respond to an outreach campaign. | Compare response rates by contact method (SMS vs email vs phone). Prioritise the channel that works for your patient base. |
The six areas in this report are not isolated problems. They are connected. A patient who receives a timely recall reminder is less likely to become a reactivation case. A new patient whose after-hours enquiry is answered is less likely to call a competitor. A patient who receives a written treatment plan summary after a consultation is more likely to accept and book.
Small percentage improvements across each area do not simply add. They compound. Consider a practice with the following starting points (all figures illustrative, based on benchmarks in this report):
None of these individual changes is large. None requires a capital investment in new equipment. All of them are primarily coordination and communication improvements. This is where AI agents are a practical match: they handle the systematic, repeatable, high-volume communication tasks that underpin each of these gains.
The most common sequence for practices implementing AI agents is:
Each step builds on the previous. The data in this report suggests that getting the first two right consistently is enough to produce a meaningful, measurable improvement in practice revenue within the first three months.
The following sources are cited throughout this report. Where data is drawn from international studies, this is noted. Where figures are illustrative calculations, they are labelled as such in the relevant section.
This report was compiled by FriendlyBots as a general-purpose benchmarking resource for Australian dental practice owners and managers. It does not constitute clinical, financial, or legal advice. Data accuracy is provided on a best-efforts basis from publicly available sources. Where data is international, it has been applied directionally to the Australian context. Practices should verify figures against their own data and consult appropriate advisers for specific operational decisions.
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