How was dual-PMS complexity across Dentally and SOE Exact managed?

Integrated via API into a unified orchestration layer normalising both data formats into a consistent internal schema.

How does the predictive no-show model identify high risk appointments?

Trained on 18 months of historical PMS data, outputting risk scores 72 hours in advance to trigger tailored outreach.

What oversight do practice teams have over recall outreach?

Managers receive daily exception reports for human follow-up. All messages use pre-approved templates.

How long did no-show impact take to appear after go-live?

Measurable impact appeared within 4 weeks, stabilizing at 7% by week 12.

Production Case Study · Healthcare🔒 NDA Protected

AI Dental Appointment Intelligence System

A dental service organisation across 32 practices was losing £2.1M annually to appointment no-shows and 4,200 uncaptured recall patients. Mirlo Systems deployed a predictive confirmation system, automated schedule recovery pipeline, and recall campaign platform, reducing the group no-show rate from 18% to 7% within 90 days.

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✦Enterprise-Grade Security✦AES-256 Encrypted Pipelines✦Zero-Downtime Migrations✦Sub-500ms Response Latency✦Agile Delivery Sprints✦CI/CD Infrastructure✦HIPAA & SOC2 Ready✦NDA-Protected Engagements✦Enterprise-Grade Security✦AES-256 Encrypted Pipelines✦Zero-Downtime Migrations✦Sub-500ms Response Latency✦Agile Delivery Sprints✦CI/CD Infrastructure✦HIPAA & SOC2 Ready✦NDA-Protected Engagements✦Enterprise-Grade Security✦AES-256 Encrypted Pipelines✦Zero-Downtime Migrations✦Sub-500ms Response Latency✦Agile Delivery Sprints✦CI/CD Infrastructure✦HIPAA & SOC2 Ready✦NDA-Protected Engagements

7%Group No-Show Rate

£2.1MAnnual Production Recovered

1,840Recall Patients Converted in 90 Days

AI Predictive Confirmation Engine — Mirlo Systems

TL;DR

Executive Summary

01 · Business & Technical Challenge

The Challenge

Operational Friction

The organisation's clinical operations across 32 practices were running well below addressable revenue capacity. Appointment no-shows ran at 18% across the group, losing between £14,700 and £17,640 in clinical production daily (£2.1M annually). Overdue recall lists held 4,200 patients uncontacted for >12 months. Front-desk staff spent 60% of working hours on manual scheduling administration.

Business Risks at Stake

Risk of continuing to lose £2.1M annually in recoverable production while treating no-shows as normal
Risk of front-desk burnout as scheduling admin volume grows across 32 practices
Risk of deploying non-compliant AI tools handling patient data without HIPAA-conscious architecture

02 · Solution Architecture & Technical Execution

The Solution

Mirlo Systems built a dual-PMS integration across Dentally and SOE Exact. The Predictive Appointment Confirmation System identified high no-show risk slots 72 hours out and applied personalized AI voice confirmations 48 and 4 hours before appointments, dropping no-shows to 7%. The Schedule Recovery Pipeline auto-filled cancelled slots from waiting lists in 22 minutes.

Technology Stack

Dentally PMSSOE ExactAnthropic ClaudeTwilion8n Workflow AutomationSupabase

Engineering Capabilities Delivered

Predictive no-show model scoring each appointment 72 hours in advance using 18 months of PMS data
AI voice confirmation replacing static SMS with personalised outreach 48 and 4 hours prior
Automated slot recovery pipeline filling cancelled appointments from waiting lists within 22 minutes
Dual PMS integration across Dentally and SOE Exact with normalised data schema
Recall campaign automation running 3-touch outreach for 4,200 overdue patients without front-desk involvement

Execution Approach

Discovery Across 5 PracticesDual PMS Integration BuildPilot at 8 PracticesFull Group RolloutCompliance Sign-Off

03 · Business Value & Tangible Results

Measurable Outcomes

Verified Performance Results

Group no-show rate: Reduced from 18% to 7% within 90 days — 90-day post-deployment PMS audit across all 32 practices
Annual production recovered: £2.1M in first year of operation — Finance team annual production comparison
Overdue recall patients converted: 1,840 of 4,200 converted in 90 days — Recall campaign tracking, PMS booking records
Cancelled slot fill time: Reduced from 4.2 hours to 22 minutes — Schedule recovery pipeline event log
Front-desk insurance admin time: Reduced by 68% in integrated practices — Staff time-tracking comparative audit

Before vs. After

Group no-show rate

Before18%

After7%

-61%

Cancelled slot fill time

Before4.2 hours average

After22 minutes average

-91%

Overdue recall patients booked

Before0 of 4,200

After1,840 of 4,200

44% converted in 90 days

Front-desk insurance admin burden

BeforeManual 100%

AfterAutomated 74%

-68% manual workload

Annual production recovered

Before£0

After£2.1M

Full recovery

Qualitative Gains

Zero manual administrative friction in routine processing pipelines.
Enhanced staff operational capacity redirected to high-value client interactions.
Full compliance and security audit trail maintained across all automated pipelines.

Stage 01
"We haven't started with AI yet"
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"We tried AI and it didn't stick"
A chatbot or off-the-shelf tool didn't hold up in real operations. We rebuild it properly - integrated with your actual systems and tested against real workload, not a controlled test.
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Stage 03
"AI is working, we want more of it"
One system is proving out. Now scale it - more coverage, more automation, better reporting - without breaking what's already working.
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Built with HIPAA-conscious architecture. Patient data is processed in isolated environment with AES-256 encryption. Zero-data-retention contract with LLM provider.