MVZ Dental Group
A Munich group practice with 52 dentists and 38 treatment units. Dampsoft, Doctolib, and DATEV integrated through a single definitional layer.
“Our data was everywhere. In Dampsoft, in spreadsheets, in the heads of our senior dentists. Stathon was the first system that did not merely connect those sources — it defined what they meant in relation to each other. That changed everything.”
Managing Director, MVZ Dental Group, Munich
The client name has been anonymized at the practice's request. Operational metrics reflect the actual deployment environment.
A Munich group practice operating 52 dentists across 38 treatment units and three sites. Four definitional gaps identified across three isolated systems. Stathon deployed at Forge Tier with all four modules in read-only integration mode. Within nine months: treatment unit utilization rose from 59–63% to 73–78%, no-show rates fell from 13–15% to ~7.9%, internal referral conversion improved from ~36% to 52–57%, and billing corrections recovered €47–55K — with an annualized projection of €230–290K.
The practice.
The client is one of Munich's largest group practices, operating under the MVZ (Medizinisches Versorgungszentrum) organizational model. The practice delivers dental care across three Munich sites, spanning seven specialties from general dentistry and surgical implantology to periodontology, pediatric dentistry, and orthodontics.
The organization operates 52 licensed dentists and 38 treatment units, employing approximately 95 personnel including clinical and support staff. The active patient base approaches 18,000 individuals, with annual patient visits ranging between 60,000 and 65,000.
The practice operates in a mixed funding environment: approximately 59% GKV (statutory health insurance), 27% PKV (private insurance), with the remainder comprising self-pay patients.
Operational fragmentation.
2.1 Operational fragmentation
The core practice management system. Patient records, clinical documentation, and GKV/PKV billing all reside here — but it does not communicate with either of the other two systems.
Appointment scheduling platform. Patients view and book available slots — but Doctolib availability logic has no awareness of Dampsoft treatment workflows.
Accounting and payroll system. Revenue data arrives via manual export-import — with no definitional context.
Treatment unit bookings were managed in separate Excel spreadsheets at each site. Not synchronized with any system, and there was no consistent concept of what counted as a utilized unit.
The three systems and Excel-based coordination together formed an organization whose data was present everywhere — but nowhere was there a definitional link between them. There was no established record of what the same concepts meant in relation to one another.
Dampsoft, Doctolib, and DATEV each applied different criteria to identify someone as a patient. As a result, patient counts, active patient ratios, and revenue attribution differed across every system.
Treatment unit utilization was calculated from manually maintained, site-level Excel spreadsheets. There was no consistent definition of what constituted a "booked", "available", or "reserved" state.
Internal referrals within the practice were not tracked systematically. There was no definition of what counted as a converted referral, what remained open, and what was lost.
The accounting logic for PKV and GKV invoices, self-pay cases, and partial reimbursements differed by system, producing distorted revenue reports.
2.2 Pre-deployment diagnostic findings
Documented utilization was running at 59–63%. The industry benchmark for group practices of comparable profile and scale is 82–88%. The gap did not stem from capacity shortage — it arose from coordination and definitional fragmentation.
The confirmed no-show rate ranged between 13.4% and 15.1%, against a benchmark of 5–7%. Recall and reminder workflows differed by system, and there was no consistent definition of what constituted a confirmed, cancelled, or no-show appointment.
Fewer than 36% of internal referrals converted to confirmed appointments within 90 days. Referral recording was ad hoc — documented by individual dentists or handed over verbally. No tracking system existed.
Average reimbursement cycle for PKV invoices was 45–50 days. An audit of coding inconsistencies indicated an estimated €200–350K in under-billed or delayed revenue, though precise measurement was not possible before deployment.
40–50% of coordinator work time was spent on manual data reconciliation: Dampsoft exports, manual Doctolib updates, and populating Excel utilization tables. This burden was not a structural necessity — it was a consequence of definitional absence.
Three phases.
The practice engaged at Forge Tier with all four modules active. Integration connects to Dampsoft DS-Win, Doctolib, and DATEV in read-only mode — no source system was modified. Deployment proceeded in three sequential phases.
Arché — Definitional layer
The Arché phase established the definitive description of the practice's complete operational logic — before any automation or intelligence was built on top. This was not data modelling. This was ontological decision-making: what does a “patient”, an “appointment”, a “referral”, a “revenue event” mean to this practice.
“During the definitional phase, nothing changed in our systems. The same data, the same screens. What changed: for the first time there was a layer between them that defined what that data meant in relation to each other. That gave us something we had never had before: a single source of truth.”
Managing Director — following Phase 1 completion
The Arché phase took approximately 10 weeks. The majority of the work was not technical implementation but structured decision-making: the board, senior dentists, and administrative leadership jointly defined the practice's canonical conceptual framework.
Core & Athena — Scheduling and intelligence
Following Arché layer completion, Core and Athena activated in parallel, grounded in the entity definitions established by Arché.
Aegis — Compliance and data sovereignty
The Aegis phase established the GDPR/DSGVO compliance architecture and data sovereignty infrastructure — grounded in the Arché layer entity definitions and the data flow inventory generated by Core.
After 9 months.
The following results are based on 9 months of operational data. A full review is scheduled for June 2026, at which point all metrics will be validated retrospectively.
Coordinators previously spent 40–50% of their working hours on manual data reconciliation. That time has been reclaimed. No staff were displaced — the capacity was redirected to patient communication and clinical coordination.
In the first 9 months, Athena contributed to €47–55K in billing corrections — primarily PKV under-billing and GKV coding discrepancy cases. Annualized, this signals a €230–290K potential.
What we learned.
The value of definitional work precedes automation
Perhaps the most significant finding from the MVZ deployment: the practice's greatest gains came not from automation, but from what preceded it. Once the practice had clear definitions for the first time — what an available treatment unit is, what a lost referral is, what a failed billing event is — the organization immediately became capable of asking questions it had previously been unable to formulate. Coordinators began to see referral losses. Senior dentists saw capacity data in real time. The board identified revenue anomalies weeks before they surfaced in the accounts.
Clinical adaptation and organizational integration
The concepts established by Arché — particularly the patient state taxonomy and the referral state machine — initially met resistance from some clinical staff who considered their own working methods well-established protocols. The measurable shift came when senior dentists began visualizing their own referral conversion rates by specialty. The data was not a directive — it was feedback from a system they had themselves defined. Nine months in, 57–62% of clinical staff regularly use the Athena dashboard for independent decisions.
Next iterations.
Athena clinical outcome modelling
Athena's second iteration will expand to include clinical outcome modelling: treatment line effectiveness by specialty, risk stratification with return-based predictive logic, and structured decision recommendations for therapeutic protocol selection.
Multi-site expansion
If the June 2026 full review confirms results to date, the practice plans to integrate two new Munich sites. The Arché layer architecture supports this natively: entity definitions are site-agnostic, and expansion requires no reconfiguration.
This Field Report is based on documented operational data. The client name and identifying details have been anonymized at the practice's request.
The full review is scheduled for June 2026. Final metrics will be updated in this document following its completion.