Post-Treatment Support Automation
12-month patient retention improved from 52% to 68%; patient-initiated complication reports reduced by 40% as proactive check-ins caught issues earlier.
10-week build across all four locations. Clinical team involved throughout content development. Full deployment in week 8, calibration review at 60 and 120 days.
The challenge
Patients were disengaging after treatment not because results failed but because the post-treatment experience created uncertainty without structured support.
The group understood, at least in principle, that patient lifetime value was their primary commercial lever. What they hadn't solved was the operational reality of maintaining meaningful contact with a growing patient base at a frequency and personalisation level that made a difference. The clinical team was fully utilised delivering treatments. Front-of-house staff were managing new patient enquiries and scheduling. No one owned the post-treatment relationship in a structured way. The result was a consistent pattern: a patient treated for the first time, a satisfactory immediate outcome, then a drift — no proactive contact, a missed rebook window, and ultimately a patient who never returned. The margin cost of this pattern was calculable and significant.
What we did
The approach
We designed a post-treatment patient support automation system built around treatment-specific communication sequences, AI-assisted Q&A for common post-treatment concerns, and intelligent rebook timing. The system was built to feel like attentive clinical aftercare — not marketing automation — which required close involvement of the clinical team in content development.
Key findings & actions
Treatment-specific aftercare sequences
personalised communication journeys triggered by treatment type, practitioner, and patient profile — covering day 1, day 3, day 7, and day 28 touchpoints with clinically accurate content
AI-assisted post-treatment Q&A
trained on the group's clinical protocols and common patient concern taxonomy, able to triage questions and escalate to clinical review when responses fell outside defined parameters
Rebook timing intelligence
model trained on historical rebook patterns by treatment category to identify the optimal outreach window for re-engagement, replacing uniform 3-month reminders with individually timed prompts
Concern escalation workflow
structured triage logic routing patient-reported concerns to the appropriate response — self-service information, front-of-house callback, or clinical review — with SLA targets for each tier
Retention analytics
monthly cohort analysis of 30/60/90/180-day rebook rates by treatment category, location, and practitioner to identify where the post-treatment journey was breaking down
How we worked
Scope
Post-treatment communication sequence design, AI Q&A configuration and escalation logic, rebook timing modelling, and retention analytics.
Timeline
10-week build across all four locations. Clinical team involved throughout content development. Full deployment in week 8, calibration review at 60 and 120 days.
Operating model
Clinical lead owned content approval. Operations manager owned workflow configuration. Joint review cadence at monthly intervals with clear escalation protocol if AI Q&A accuracy fell below defined thresholds.
Outcomes
What changed
12-month patient retention improved from 52% to 68%; patient-initiated complication reports reduced by 40% as proactive check-ins caught issues earlier.
12-month patient retention improved from 52% to 68%
a 31% relative improvement — within the first year of operation
Average rebook time for injectable treatments reduced from 118 days to 89 days, recovering approximately one additional treatment cycle per patient per year
Patient-initiated contact to report post-treatment complications reduced by 40%, as proactive check-ins identified issues earlier and delivered reassurance before anxiety escalated to a call
Clinical team reported a reduction in reactive concern management, with AI triage handling 73% of post-treatment questions without clinical escalation
Governance
Trust, collaboration & governance
All clinical content reviewed and approved by the clinical lead before deployment — no AI-generated clinical advice entered the system without human sign-off
Escalation logic designed with explicit conservative thresholds: when in doubt, route to human review rather than attempt an automated response
Patient consent for automated communications obtained at booking, with straightforward opt-out mechanism throughout
Monthly accuracy audit of AI Q&A responses against clinical standards, with retrain trigger if divergence was detected
Reframe
Patients weren't disloyal — they were un-followed-up. The automation maintained relationships at a scale the team alone couldn't sustain.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Next steps
Related services
Start a discovery
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We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.