Dental clinic: how to verify reported reductions in overdue payments

In this article we explain what vendors typically report about improvements in overdue payments after implementing automated dunning and AI‑driven reminders, what you should check before making decisions, how to measure the impact in your own dental clinic and the key legal requirements (GDPR/LOPDGDD).

Throughout you will find: a summary of the evidence and its verification level; a past/present/future table with the cut‑off date; a technical and operational description of commonly automated features; a practical methodology to measure reduction in overdue payments; a legal and operational checklist; an illustrative numerical example; and concrete steps to pilot this in a dental clinic.

Executive summary and evidence verification level

What has been published and what it means

• In the AR/dunning sector it is common for vendors to publish case studies and marketing materials claiming improvements in on‑time payments after deploying automated sequences and AI scoring. These figures frequently appear in commercial collateral; however, treat them as ‘vendor‑reported’ until primary verifiable evidence is supplied.
• Verification status: in our review as of reference date 2026‑09‑09 we did not find public, independent primary documentation that confirms, for a named dental clinic case, a specific reduction in overdue payments verifiable by an external audit. Therefore, this article preserves any concrete percentages as vendor/marketing reports and explains what to request to verify them before relying on them for purchasing decisions.

What to ask for when you want to verify a specific claim

• Accounting report detailing the compared periods and the exact KPI definition (for example: the numeric definition of “overdue payments” used).
• The compared periods and sample size (number of invoices, total invoiced amount in each period).
• Evidence that there were no simultaneous changes (discounts, commercial policies, external collection campaigns) that could explain the difference.
• External audit or certification of the outcome, if available.

If you do not have primary documentation for a claim, use a mitigated headline like this article: “reported by vendors” and make it clear these figures come from commercial materials and must be verified with primary documentation.

Verification box (summary)

• Primary documentation available: NO (we have not located public, independent primary documentation that verifies a specific dental clinic case with a concrete figure as of publication date 2026‑09‑09). Request primary documentation from the vendor or clinic before quoting precise figures.
• Vendor sources (examples): there are numerous vendor whitepapers and marketing materials in the AR/debt recovery sector; interpret those figures cautiously: ask for period, KPI definition, sample size and external audit if you require it.

Table: past / present / future (context and trends)

Reference date: 2026‑09‑09

Period	Summary
PAST	Progressive adoption over the last decade of automated reminders (SMS/email) and AR solutions. Relevant legal framework: Regulation (EU) 2016/679 (GDPR) and the LOPDGDD (2018). Facturae format is mandatory for invoicing public administrations. Sources: GDPR, BOE – LOPDGDD, Facturae.
PRESENT	As of publication date 2026‑09‑09 no public independent study was located that documents a dental clinic case with a concrete figure verifiable publicly. There are multiple vendor cases and whitepapers with approximate ranges reported in commercial materials. GDPR and LOPDGDD remain applicable; the AEPD is the competent authority.
FUTURE	Expected trend: more AI to personalise dunning sequences and predictive models for non‑payment. Regulatory risk: increased supervision over automated decisions and electronic communications; monitor AEPD updates after the publication date.

What is typically automated: functional and technical components

Common AR automation implementations for clinics include:

• Automatic capture and registration of invoices from your ERP or clinical management system.
• Automatic issuance and sending of invoices/receipts (PDF, email) and scheduled reminders.
• Multichannel dunning: email, SMS, WhatsApp Business (with caution), automated or manual calls, and postal escalation.
• Integrated payment gateways (virtual POS, payment links in email/SMS) to facilitate immediate payment.
• Scoring and prioritisation with models using payment history, amount, ageing and patient behaviour to choose channel and tone.

Suggested diagram: flow from “invoice generated” → “timing & scoring rules” → “channel selected” → “send” → “payment or escalate to human”.

Examples of recommended messages and tone (do not copy vendor templates verbatim):

• Pre‑due email (3 days before): friendly reminder tone, state amount, due date and payment link.
• SMS on due date: brief, clear call to action with link.
• WhatsApp (if used): obtain consent and avoid mentioning treatments; use a generic message about outstanding balance and payment options.

Common multichannel dunning flow (typical practice)

1. Reminder 3 days before due (email, informational tone).
2. Notice on due date (SMS with payment link).
3. 3 days post‑due: second email with payment options and an offer to agree instalments if appropriate.
4. 14 days post‑due: more formal communication and an invitation to contact administration.
5. 30–45 days: human escalation (collections team call or notice of further measures).

Variants: for patients avoid mentioning any clinical details in messages; for B2B invoices you can be more specific. A/B test intervals and tone for your patient base (each clinic and demographic responds differently).

If you would like hands‑on support with implementation at this stage, the architecture phase of the Momentum Process covers the definition of flows and scoring adapted to the clinic.

How to measure reduction in overdue payments: definition, metrics and methodology

Operational definition of “overdue payments” (suggested for comparison):

• Overdue payments (%) = (Amount overdue and unpaid in period X) / (Amount invoiced in period X) * 100.

Alternative and complementary metrics:

• DSO (Days Sales Outstanding): average days to collect from invoice date.
• % on‑time payments: percentage of invoices paid on or before the due date.
• Recovery rate: amount recovered after dunning actions vs amount initially overdue.

Recommended methodology to attribute effect to automation:

1. Define the KPI clearly (e.g. Overdue payments using the formula above).
2. Comparable period: minimum 3–6 months before vs 3–6 months after; 6 months is more robust to remove seasonality.
3. Control variables: do not change relevant commercial terms (e.g. discounts, financing) or collection policies during the pilot period.
4. Preferable: design an A/B pilot (control group using the current process vs treated group with automation) to isolate the effect.

Practical before/after calculation example (illustrative)

Assumptions (illustrative example):

• Monthly invoicing: EUR 50,000.
• Compared period: 6 months.
• Total overdue amount before (6 months): EUR 6,000 (overdue 2.0% using the suggested definition).
• Total overdue amount after (6 months with automation): EUR 3,600 (illustrative).

Calculation (illustrative):

• Overdue before = EUR 6,000 / (EUR 50,000 * 6) = EUR 6,000 / EUR 300,000 = 2.0%.
• Relative reduction (illustrative) = (EUR 6,000 – EUR 3,600) / EUR 6,000 = 40% less overdue amount.

Warning: this example is illustrative and assumes there were no other changes. If variables are not controlled, attribution to the automated system is uncertain.

Legal compliance and data protection (critical for dental clinics)

Key obligations:

• Applicable framework: GDPR (Regulation (EU) 2016/679) and LOPDGDD (Ley Orgánica 3/2018). In Spain the AEPD publishes practical guidance on electronic communications and vendors.
• Health data: if a message reveals information about treatments it is a special category (art. 9 GDPR) and requires stronger safeguards and, generally, a more secure legal basis.
• Possible legal bases for collection communications: performance of a contract (when a contractual relationship exists), legitimate interest (with a necessary balancing test) or consent (where applicable). Document the chosen legal basis and the corresponding analysis.
• DPIA (data protection impact assessment): recommended when the system includes automated profiling with significant effects on individuals (for example, scoring that leads to adverse decisions). Practical recommendation: consult AEPD guidance and document the DPIA.
• Contracts with vendors: include a data processor clause, details on international transfers, technical measures (encryption, restricted access), retention periods and subcontractors.

Messaging good practices:

• Avoid any reference to treatments or clinical details in collection messages.
• Offer channel options and communication preferences/consent.
• Keep logs and communication histories and document the legal basis.
• Limit frequency and ensure a respectful, proportionate tone.

Risks by channel (WhatsApp, SMS, email)

• WhatsApp Business: commonly used but relies on a third party that may process metadata and, in some cases, transfers outside the EU; review processor clauses and guarantees.
• SMS: cost per message and delivery variability; less control over content security.
• Email: deliverability and phishing risks; require authentication measures (SPF/DKIM/DMARC) and clear templates.

Minimum checklist to assess vendors:

• Do they provide a data processor clause and a subcontractor model?
• Where are the data stored (EU/EEA or third countries)?
• What technical (encryption at rest/in transit, data segregation) and organisational measures do they apply?
• Do they allow audits or provide security reports?
• Do they facilitate deletion and portability under GDPR?

Operational, reputational and ethical risks and how to mitigate them

Key risks and practical mitigations are described above: segmentation, empathetic tone, human escalation, periodic audits, and templates validated by legal counsel. Maintaining these practices reduces litigation and complaints.

Cost, expected ROI and commercial considerations

Costs to consider:

• SaaS subscription (monthly or annual).
• Per‑message costs (SMS/WhatsApp) and payment gateway fees.
• Technical integration (hours/consultancy fees) with your management system.
• Team training and process adjustments.

Simple ROI template (illustrative):

• Initial investment (setup + integration): EUR 3,000 (illustrative).
• Annual recurring cost (SaaS + messages): EUR 2,400 (illustrative).
• Estimated saving from reduced overdue amounts (illustrative): if the clinic currently has EUR 6,000 overdue in 6 months and reduces by 40% → saving of EUR 2,400 in 6 months (illustrative).
• Approximate payback: investment recovered in ~8–12 months in this scenario (illustrative).

Recommendation: ask the vendor for period‑by‑period breakdowns, KPI definitions and sample sizes before accepting percentage claims.

Practical step‑by‑step guide to implement in your dental clinic

Implementation checklist (8 steps):

1. Data audit: contact quality, duplicates, valid emails/SMS numbers.
2. KPI definition: agree how you will measure “overdue payments” and the comparison period.
3. Vendor selection: verify technical and legal criteria (see checklist above).
4. Pilot design (A/B) and control group definition.
5. Integration with billing/ERP systems and payment gateway.
6. Configure sequences, scoring and templates (avoid clinical content).
7. Monitor (DSO, % on‑time payments, recovery rate) and adjust weekly during the pilot.
8. Legal documentation: contracts, legal basis, DPIA if applicable, retention policies.

Recommendations for the pilot: sufficient sample size for statistical signal (for example 200–500 invoices in 3 months) and minimum duration 3 months; 6 months is preferable for stability. These figures are indicative.

If you want practical help with the pilot or technical integration, Momentum Logic AI can support you from the initial audit through pilot deployment.

Note: for a broader view on automating recurring billing and bank reconciliation with AI —tasks complementary to collections— consult our pillar guide on invoice automation and bank reconciliation with AI on the Momentum Logic AI blog.

Comparison of solutions: specialised AR SaaS vs ERP modules vs bespoke development

Practical summary: specialised AR SaaS usually offers speed and multichannel dunning; ERP modules give more data control if your ERP supports it; bespoke development provides customisation but higher cost and time. Choose according to volume and desired control.

Sources, vendor cases and how to interpret them

Primary legal sources cited in this article:

• GDPR
• LOPDGDD (BOE)
• AEPD
• Facturae

Vendor cases and materials (verification note): there are many vendor marketing materials in the sector. Before reproducing percentages or concrete figures, request primary documentation from the vendor or clinic.

Conclusion and next steps for the reader

Practical conclusion: automation can improve collections for many clinics, but any concrete figure in vendor materials must be verified with primary documentation. Prioritise proper measurement (define KPI and period), design an A/B pilot and ensure GDPR compliance, particularly if there is a risk of revealing health data.

Recommended next step: perform a data audit and a controlled pilot before scaling. If you would like us to evaluate your specific case, request a Free Assessment and we will cross‑check KPI definitions and pilot design.

This article was drafted with the support of artificial intelligence and reviewed by qualified Momentum Logic AI staff before publication.

— Momentum Logic AI Team . momentumlogicai.es . LinkedIn

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