Why a scenario and not a case study?
Because we would rather show the arithmetic than invent a testimonial. The numbers below come from three published studies applied to a typical practice; the practice is a composite, and nothing here is a claim about a client’s results. Where a figure is an assumption, it says so.
The practice
Four chairs in Sandton, open 08:00 to 17:00 on weekdays and Saturday mornings, about 56 booked slots a day. One receptionist, who also does the billing. A website with a contact form and a phone number. Enquiries arrive by phone during hours, by form at all hours, and by search at night from people with a toothache.
What does the no-show research say about its week?
The 2018 systematic review of 105 studies puts the average no-show rate at about 23% across specialties, with long booking lead times and a prior no-show as the strongest predictors 1. Applied to 56 slots a day, that is about 13 missed slots a day, roughly one chair standing idle for the full day, five days a week. The chair, the room and the staff are paid for either way.
| Today (assumed) | With reminders and deposits (assumed) | |
|---|---|---|
| Booked slots a day | 56 | 56 |
| No-show rate | 23% 1 | 10% (a target, not a measured result) |
| Missed slots a day | 13 | 6 |
| Slots recovered a month (22 days) | about 160 |
The 10% is deliberately not a promise. The Cochrane review of eight randomised trials found that text-message reminders increase attendance compared with no reminders and with letters, match phone calls, and cost less per attended appointment 2; it measured an improvement, not a fixed floor. A practice already phoning every patient will see less change than one that does not remind at all.
What does the response-time research say about its evenings?
Harvard Business Review’s audit of 2,241 companies found that firms contacting a web lead within an hour were nearly seven times as likely to qualify it as firms that waited even an hour longer, and more than sixty times as likely as firms that waited a day; 23% never replied 3. The practice’s form is read at 08:00 the next morning, so every evening and weekend enquiry is in the sixty-times bucket. The person with the cracked tooth at 21:40 has booked the practice whose site answered.
What would the chatbot change?
The evening enquiry is answered within seconds: the emergency number for a real emergency, otherwise the first available slot, booked into the diary, with a reminder set. The medical-aid question is answered from the practice’s own list. The reminder goes out the evening before with one-tap confirm or cancel, and a cancelled slot is offered to the waiting list. The receptionist arrives to a diary that filled itself overnight and a morning without forty repeated questions.
What would it not change?
Anything clinical. The chatbot does not triage, does not advise and does not discuss results; it hands those to a person. It collects a name, a number and a reason category, and nothing else, because health information is special personal information under POPIA and the Regulator’s fines run to ten million rand 4.
What is the honest conclusion?
That the arithmetic is favourable and the evidence is real, and that a practice should measure its own no-show rate and its own after-hours enquiry count before believing either number above. The booking system and the website chatbot are month to month, so the measurement can be done on the practice’s own data in the first month.
Questions people ask
Is this a real practice?
No. It is a composite built to show how the published research applies to a practice of that size. We do not publish client results, and we have not invented any.
Where does the 23% come from?
A 2018 systematic review of 105 studies of appointment no-shows across specialties; the average across them was about 23% 1.
Can the chatbot book into our practice software?
It books into a shared calendar or connects to your system through its integrations; ask us about the software you run.
Sources
- Dantas, Fleck, Cyrino Oliveira & Hamacher, “No-shows in appointment scheduling – a systematic literature review”, Health Policy 122(4), 2018 (105 studies)
- Gurol-Urganci et al., “Mobile phone messaging reminders for attendance at healthcare appointments”, Cochrane Database of Systematic Reviews, 2013 (8 trials, 6,615 participants)
- Oldroyd, McElheran & Elkington, “The Short Life of Online Sales Leads”, Harvard Business Review, March 2011 (audit of 2,241 US companies)
- Protection of Personal Information Act 4 of 2013, section 109 (administrative fines) — popia.co.za