Dental Clinic · Netherlands
Reducing appointment friction across a multi-location patient journey
How we redesigned appointment reminders, confirmation and recall workflows for a three-location healthcare provider.
The problem
Patients received inconsistent appointment communication while front-desk staff managed confirmations, reminders and recall journeys across fragmented channels.
The solution
A WhatsApp-based reminder and recall workflow connected to appointment records, with clear patient options and human escalation for ambiguous situations.
Measurement context
Baseline, measurement period and source should be verified before republication.
Technologies
Context
The organisation operated three locations with twelve staff members and relied heavily on manual calls, emails and personal WhatsApp messages to manage appointments. Patients had limited options to respond and staff lacked a consistent view of appointment status.
Discovery and evidence
- Appointment no-show rate close to 18%.
- More than 15 staff hours per week spent on reminder calls.
- Inconsistent recall communication for overdue check-ups.
- Limited visibility into whether a patient confirmed, requested help or ignored a reminder.
Product opportunity
How might we help patients confirm, reschedule or request assistance without creating another communication channel for the front-desk team to manage?
Key product decisions
- Use WhatsApp because it was already familiar to patients and required no additional application or account.
- Keep humans in ambiguous situations instead of generating unreliable automated responses.
- Start with appointment reminders and overdue recall journeys before expanding scope.
- Connect communication to the practice management platform as the source of truth.
Journey before and after
Before: appointment scheduled -> manual call -> voicemail -> email -> manual follow-up -> spreadsheet or system update.
After: appointment scheduled -> automated reminder -> patient confirms, reschedules or requests support -> record updated or exception routed to staff.
What we learned
The greatest value did not come from automating every patient interaction. It came from separating predictable interactions from situations where human judgement remained necessary.
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