Denti.AI
Step-by-step implementation guide — pre-implementation checklist, onboarding, staff training, go-live runbook, and ROI tracking.
Denti.AI — Implementation Playbook (DSO)
Denti.AI Implementation Playbook
Diagnostic Imaging AI for Dental Support Organizations
1. Executive Summary
What Denti.AI Does
Denti.AI is an FDA-cleared diagnostic imaging AI platform that automatically analyzes dental radiographs (periapical, bitewing, panoramic, and CBCT) to detect pathologies including caries, periapical lesions, bone loss, calculus, and other clinical findings. The system overlays AI-detected findings directly onto images within your existing workflow, providing dentists with a consistent second read that enhances diagnostic accuracy and documentation.
Why DSOs Specifically Benefit from Diagnostic Imaging AI
Scale Advantages:
- A 30-location DSO processes approximately 500,000+ radiographs annually. AI-assisted diagnosis at this volume transforms from a convenience into a competitive moat—standardizing care quality across providers of varying experience levels while reducing diagnostic variability that creates liability exposure.
Standardization:
- Diagnostic imaging AI eliminates the "provider lottery" where patient outcomes depend on which dentist happens to be scheduled that day. Your newest associate and your most experienced clinician now operate with the same AI-augmented baseline, creating predictable clinical quality that supports your brand promise.
Data Aggregation:
- At scale, Denti.AI generates enterprise-level insights impossible for single practices to capture: detection rates by location, provider calibration metrics, pathology prevalence trends, and treatment acceptance correlation data. This intelligence informs clinical protocols, CE priorities, and operational decisions.
Expected Timeline: Decision to Full Deployment
| Phase | Timeline | Milestone |
|---|---|---|
| Pre-Implementation | Weeks 1–2 | Technical readiness, baseline metrics, stakeholder alignment |
| Pilot Wave (2–3 locations) | Weeks 3–6 | Validated workflows, champion certification |
| Wave 2 (5–8 locations) | Weeks 7–12 | Scalable rollout process confirmed |
| Wave 3+ (remaining locations) | Weeks 13–20 | Full deployment |
| Optimization | Weeks 21–28 | ROI validation, workflow refinement |
For a 30-location DSO: 6–7 months from contract signature to full deployment with optimization.
2. Pre-Implementation Checklist (Weeks 1–2)
Technical Requirements
Hardware
☐ Verify all imaging sensors/equipment are on Denti.AI's compatibility list 🔵 ☐ Confirm workstation specifications meet minimum requirements:
- Windows 10/11 (64-bit) or macOS 12+
- 8GB RAM minimum (16GB recommended)
- 50GB available storage for local processing cache
- Display resolution: 1920x1080 minimum ☐ Document imaging modalities by location (periapical, bitewing, pano, CBCT) Estimated time: 3–4 hours across portfolio
Software
☐ Verify PMS versions across all locations (see integration compatibility matrix below) ☐ Confirm imaging software versions (Dexis, Schick, Carestream, etc.) ☐ Document browser versions on clinical workstations ☐ Identify any locations running legacy systems requiring upgrade ⚠️ Estimated time: 2–3 hours with centralized IT
Network
☐ Test upload/download speeds at each location (minimum 25 Mbps up/down) ☐ Verify firewall rules allow connection to Denti.AI endpoints 🔵 ☐ Confirm VPN configurations if applicable ☐ Test latency to Denti.AI servers (acceptable: <200ms) Estimated time: 1–2 hours per location
Integrations
| PMS | Integration Method | Compatibility Notes |
|---|---|---|
| Dentrix | Direct API | Version 16.5+ required |
| Eaglesoft | DICOM bridge | Version 21+ required |
| Open Dental | Direct API | Version 22.1+ required |
| Denticon | Cloud-native | Full compatibility |
| Curve | Cloud-native | Full compatibility |
Vendor Onboarding Steps
☐ 🔵 Schedule kickoff call with Denti.AI enterprise team (Day 1) ☐ 🔵 Receive dedicated Customer Success Manager assignment ☐ 🔵 Establish direct escalation contacts:
- Technical Support: enterprise-support@denti.ai
- Account Executive (commercial issues)
- Implementation Engineer (technical issues)
- Clinical Liaison (workflow questions) ☐ 🔵 Confirm SLA terms for enterprise support (target: 1-hour response for P1 issues) ☐ Sign BAA and complete vendor security assessment 🔵 ☐ Receive sandbox/test environment credentials 🔵 Estimated time: 4–5 hours total
Data/Access Prerequisites
☐ Create master API credential with enterprise scope 🔵 ☐ Establish SSO integration (SAML 2.0 or OAuth 2.0) if available ☐ Document imaging archive access method per location:
- Direct DICOM pull
- Bridge software integration
- Manual export/import ☐ Compile location-level admin credentials for PMS systems ☐ Map imaging storage locations (local server, cloud, hybrid) ☐ Identify historical imaging retention needs (typically 2–5 years for AI training value) Estimated time: 6–8 hours
Internal Stakeholder Alignment
Stakeholder Alignment Map 🟣
| Stakeholder | Role in Implementation | Communication Frequency | Key Concerns to Address |
|---|---|---|---|
| Board/Investors | Approve budget, track ROI | Monthly updates | ROI timeline, competitive positioning, liability protection |
| CEO/CDO | Executive sponsor, remove blockers | Weekly during rollout | Clinical quality lift, provider adoption, brand differentiation |
| VP of Operations | Overall implementation owner | Daily during active waves | Timeline adherence, resource allocation, location readiness |
| CFO | Budget approval, ROI validation | Monthly + quarterly | Cost per location, payback period, efficiency gains |
| VP of IT/CTO | Technical architecture decisions | Daily during integration | Security, infrastructure, maintenance burden |
| Regional Managers | Cascade to locations, manage resistance | 2x weekly during active waves | Staff workload, training burden, operational disruption |
| Office Managers | Day-to-day execution at location | Daily during their wave | Workflow changes, patient communication, team morale |
| Clinical Directors | Protocol approval, provider buy-in | Weekly | AI accuracy, clinical override protocols, liability |
| Providers | End users, clinical adoption | Training + go-live + check-ins | "Does this help me or slow me down?" |
Approval Gates Required 🟣
☐ Budget approval from CFO/CEO ☐ Security/compliance approval from IT/Compliance ☐ Clinical protocol approval from CDO/Clinical Director ☐ Communication plan approval from Marketing/Communications Estimated time: 2–3 weeks of alignment conversations before kickoff
Baseline Metrics to Capture BEFORE Go-Live ⚠️
Critical: Standardize measurement methodology across all locations before capturing baselines.
| Metric | Definition | Measurement Source | Frequency | Notes |
|---|---|---|---|---|
| Case Acceptance Rate | % of diagnosed treatment accepted by patients | PMS reports | Monthly | Segment by procedure type |
| Radiographic Diagnosis Time | Minutes from image capture to documented findings | Time study (sample) | Spot check | Sample 20 patients/location |
| Pathology Detection Rate | # of pathologies documented per 100 BWX | Chart audit | Monthly | Requires manual chart review |
| Treatment Plan Value per Patient | Average $ of proposed treatment | PMS reports | Monthly | New patient vs. existing |
| Claim Denial Rate (diagnosis-related) | % of claims denied for documentation issues | RCM reports | Monthly | Isolate imaging-related denials |
| Patient Cycle Time | Chair time per imaging appointment | Schedule analysis | Weekly | Establishes efficiency baseline |
| Provider Diagnostic Confidence | Self-reported confidence score | Survey (1–10 scale) | Pre-launch | Qualitative, not quantitative |
Standardization Protocol for Baseline Metrics 🟣
☐ Create unified data dictionary defining each metric precisely ☐ Assign central analyst to pull metrics (avoid location-level variation in interpretation) ☐ Capture 3 months of pre-implementation data where possible ☐ Document any locations with data quality issues (flag for interpretation) ☐ Store baseline data in central repository for post-implementation comparison Estimated time: 2–3 weeks to capture comprehensive baselines
Enterprise-Level Requirements
Network Standards Across Locations
☐ 🟣 Decide: Centralized cloud hosting vs. location-level edge processing
- Recommended for most DSOs: Cloud-hosted with edge caching for performance ☐ Document standard firewall rules to deploy across all locations ☐ Establish VPN configuration standards if locations require private connectivity ☐ Create network readiness checklist for new location onboarding (future-proofing)
SSO and Access Management
☐ 🔵 Configure SSO integration with identity provider (Okta, Azure AD, etc.) ☐ 🟣 Define role-based access control structure:
- Admin: Central IT, implementation team
- Clinical: Providers (full access to AI findings)
- View-only: Regional managers (reporting only)
- Support: Office managers (limited admin functions) ☐ Establish user provisioning/deprovisioning workflow ☐ Document process for credentialing new providers within system
Centralized Credentialing
☐ Create master provider roster with NPI, state licenses, location assignments ☐ Configure provider-level access permissions in Denti.AI ☐ Establish update process when providers transfer between locations Estimated time: 8–10 hours for enterprise setup
3. Location Readiness Assessment
Scoring Framework
Score each location on the following factors (1 = lowest readiness, 5 = highest readiness):
Factor 1: IT Infrastructure Maturity
| Score | Criteria |
|---|---|
| 5 | Fiber internet (100+ Mbps), hardware <3 years old, current PMS version, cloud-based systems |
| 4 | Cable internet (50+ Mbps), hardware <5 years old, PMS within 1 version of current |
| 3 | DSL or cable (25+ Mbps), hardware <7 years old, PMS within 2 versions of current |
| 2 | Slow internet (<25 Mbps) OR hardware >7 years old OR outdated PMS |
| 1 | Multiple infrastructure deficiencies, significant upgrades required before implementation |
Factor 2: Staff Tenure and Adaptability
| Score | Criteria |
|---|---|
| 5 | Turnover <15%, history of successful tech adoption, staff expresses enthusiasm for new tools |
| 4 | Turnover 15–25%, at least one successful tech adoption in past 2 years |
| 3 | Turnover 25–35%, mixed history with technology changes |
| 2 | Turnover 35–50% OR documented resistance to recent technology changes |
| 1 | Turnover >50% OR active staff dissatisfaction that would complicate any change initiative |
Factor 3: Patient Volume
| Score | Criteria |
|---|---|
| 5 | 150–250 patients/week (optimal: high impact, not overwhelming) |
| 4 | 100–150 patients/week (good volume, slightly less data) OR 250–300 (slightly higher risk) |
| 3 | 75–100 patients/week OR 300–350 patients/week |
| 2 | <75 patients/week (limited ROI) OR 350–450 patients/week (capacity concerns) |
| 1 | <50 patients/week (insufficient volume) OR >450 patients/week (extreme change risk) |
Factor 4: Existing Tech Stack Compatibility
| Score | Criteria |
|---|---|
| 5 | PMS and imaging software on Denti.AI compatibility list, no custom integrations that could conflict |
| 4 | Primary systems compatible, minor integrations may need adjustment |
| 3 | Systems compatible but on older versions requiring potential updates |
| 2 | One primary system (PMS or imaging) requires upgrade before implementation |
| 1 | Multiple systems incompatible, significant technical work required |
Factor 5: Local Champion Availability
| Score | Criteria |
|---|---|
| 5 | Tech-forward provider AND engaged office manager, both have expressed interest in AI |
| 4 | Either a tech-forward provider OR an engaged office manager with tech aptitude |
| 3 | Staff members willing to champion but haven't led tech initiatives before |
| 2 | No obvious champion, but no active resistance identified |
| 1 | No champion candidate identified OR key staff actively resistant to new technology |
Composite Readiness Score Calculation
Simple Weighted Average:
| Factor | Weight |
|---|---|
| IT Infrastructure | 25% |
| Staff Tenure/Adaptability | 20% |
| Patient Volume | 15% |
| Tech Stack Compatibility | 25% |
| Local Champion | 15% |
Composite Score = (IT × 0.25) + (Staff × 0.20) + (Volume × 0.15) + (Compatibility × 0.25) + (Champion × 0.15)
Readiness Tiers
| Composite Score | Readiness Tier | Recommended Wave |
|---|---|---|
| 4.0–5.0 | High Readiness | Wave 1 candidates |
| 3.0–3.9 | Moderate Readiness | Wave 2 |
| 2.0–2.9 | Low Readiness | Wave 3 (with remediation) |
| <2.0 | Not Ready | Defer until remediation complete |
Sample Location Assessment Matrix
| Location | IT Infra | Staff | Volume | Tech Stack | Champion | Composite | Tier |
|---|---|---|---|---|---|---|---|
| Denver Main | 5 | 4 | 4 | 5 | 5 | 4.60 | High |
| Phoenix East | 4 | 5 | 4 | 4 | 4 | 4.20 | High |
| Tucson West | 3 | 4 | 3 | 4 | 4 | 3.55 | Moderate |
| Austin Central | 4 | 3 | 5 | 3 | 3 | 3.55 | Moderate |
| El Paso | 2 | 2 | 3 | 3 | 2 | 2.40 | Low |
Recommended Rollout Sequence Logic 🟣
Wave 1 Selection Criteria (2–3 locations):
- Composite score 4.0+ (high readiness)
- Geographic diversity (test different regional contexts)
- Mix of urban/suburban if your portfolio varies
- At least one location with your most common PMS
- Avoid your flagship location (too much visibility if issues arise) ⚠️
- Avoid your lowest-performing location (don't conflate tool performance with location challenges)
Wave 2 Expansion (5–8 locations):
- All moderate readiness locations (3.0–3.9)
- Include locations with your second-most-common PMS
- Test any specialty mix variations (ortho, pedo, GP) if applicable
Wave 3 Completion:
- Remaining locations with remediation of identified barriers
- New location openings should follow standard implementation playbook
4. Rollout Strategy
Wave Structure Overview
| Wave | Locations | Duration | Primary Objective |
|---|---|---|---|
| Wave 1 (Pilot) | 2–3 | 4 weeks | Validate workflows, identify failure points, certify champions |
| Buffer | — | 2 weeks | Capture lessons learned, refine processes, update training |
| Wave 2 | 5–8 | 4 weeks | Scale validated process, stress-test support model |
| Buffer | — | 1 week | Minor refinements only |
| Wave 3 | Remaining | 4–6 weeks | Full deployment at production pace |
| Wave 4 (if needed) | Remediated locations | As needed | Address locations that were deferred |
Wave 1 Pilot Location Selection 🟣
Selection Matrix Checklist:
☐ Composite readiness score ≥4.0 ☐ Office manager has capacity to provide daily feedback ☐ At least one provider willing to participate in clinical validation ☐ Located within reasonable travel distance from central team (or regional manager) ☐ Representative of broader portfolio (don't pick only your most unique locations) ☐ Not currently undergoing other major changes (renovation, leadership transition, etc.) ⚠️ ☐ PMS is your most common platform (to validate primary integration path)
Recommended Pilot Configuration for 30-Location DSO:
- Pilot Location A: Highest readiness score, closest to HQ, general practice
- Pilot Location B: Second-highest readiness, different region, represents common workflow
- Pilot Location C (optional): Specialty practice if applicable (pediatric, ortho)
Wave Timeline Detail
Wave 1: Weeks 3–6
| Week | Activities |
|---|---|
| Week 3 | Integration go-live, champion training, parallel workflow begins |
| Week 4 | First patient images through system, daily troubleshooting calls |
| Week 5 | Provider training complete, all providers using system |
| Week 6 | Parallel workflow ends, AI-assisted workflow becomes standard |
Daily Cadence During Wave 1:
- AM: Champion submits overnight issues via Slack/Teams channel
- 10 AM: 15-minute standup with central implementation team
- PM: Champion documents workflow observations
- 4 PM: Quick check-in call if issues flagged
- EOD: Central team logs issues/resolutions in implementation tracker
Inter-Wave Buffer: Weeks 7–8
☐ Compile all issues encountered into "lessons learned" document ☐ Update training materials based on pilot feedback ☐ Refine go-live day checklist based on actual experience ⚠️ ☐ Adjust workflow recommendations if needed ☐ Certify Wave 1 champions (they may support Wave 2 sites) ☐ 🟣 Present pilot results to executive sponsor Estimated time: 20–30 hours of analysis and refinement
Wave 2: Weeks 9–12
| Week | Activities |
|---|---|
| Week 9 | Integration go-live at all Wave 2 locations simultaneously (or staggered by 2–3 days) |
| Week 10 | Champion-led training execution, provider onboarding |
| Week 11 | Parallel workflow, intensive troubleshooting |
| Week 12 | Transition to standard workflow, stabilization |
Wave 3: Weeks 14–20
- Deploy in cohorts of 5–8 locations per week
- By this phase, process should be production-ready
- Central team transitions from hands-on to oversight role
- Champions from earlier waves can serve as peer mentors
Go/No-Go Criteria ⚠️
Criteria to Advance from Wave 1 to Wave 2
| Criterion | Threshold | Measurement |
|---|---|---|
| Technical stability | <3 P1 incidents in final 2 weeks | Incident log |
| User adoption | >80% of images reviewed with AI | System analytics |
| Provider satisfaction | Net Promoter Score >0 | Quick survey |
| Training completion | 100% of staff trained | Training tracker |
| Workflow efficiency | No net increase in patient cycle time | Time study |
| Champion confidence | Champion rates readiness to scale at 4+ (1–5 scale) | Champion feedback |
Decision Protocol:
- 🟣 If 5 of 6 criteria met: Proceed to Wave 2
- 🟣 If 3–4 criteria met: Extend buffer, address gaps, reassess in 1 week
- 🟣 If <3 criteria met: Pause, conduct root cause analysis, escalate to executive sponsor
Criteria to Advance from Wave 2 to Wave 3
| Criterion | Threshold | Measurement |
|---|---|---|
| Technical stability | <2 P1 incidents per location in final 2 weeks | Incident log |
| Support scalability | Central team handling volume without backlog | Support metrics |
| Champion model validation | Champions leading training independently | Observation |
| Cross-location consistency | <15% variance in key workflow metrics | Analytics |
| Provider satisfaction | Net Promoter Score >+10 | Survey |
Rollback Plan ⚠️
Rollback Triggers
- Critical system outage lasting >4 hours during business hours
- Data integrity issue affecting patient records
- Widespread provider refusal to use system (>50% of providers)
- Patient safety concern identified
Rollback Procedure by Scenario
Scenario A: Single Location Technical Failure
- Disable Denti.AI integration at affected location (30 minutes)
- Revert to pre-implementation workflow
- Notify affected location staff
- Escalate to vendor for root cause analysis 🔵
- Other locations continue unaffected
Scenario B: Integration-Level Failure (affects multiple locations)
- 🟣 Decision maker authorizes temporary enterprise-wide disable
- Push communication to all affected locations within 1 hour
- Document patient appointments requiring re-review
- Vendor escalation to P0 status 🔵
- Daily status updates until resolution
Scenario C: Wave Failure (pattern of failures across Wave locations)
- Complete current wave but do not advance
- Extend buffer period indefinitely
- 🟣 Conduct executive review of implementation approach
- 🔵 Engage vendor executive sponsor
- Revise implementation plan before proceeding
Isolation Protocol: Each location's Denti.AI instance operates independently. A failure at one location does not technically impact other locations. This allows surgical rollback without portfolio-wide disruption.
5. Configuration & Integration (Weeks 2–3)
Step-by-Step PMS Integration
Dentrix Integration
☐ Step 1: Verify Dentrix version 16.5 or higher ☐ Step 2: Contact Denti.AI to obtain Dentrix API bridge installer 🔵 ☐ Step 3: Install bridge application on Dentrix server (requires admin rights) ☐ Step 4: Configure API credentials in bridge application ☐ Step 5: Map provider IDs between Dentrix and Denti.AI ⚠️ ☐ Step 6: Test patient lookup from Denti.AI → Dentrix ☐ Step 7: Test treatment plan push from Denti.AI → Dentrix ☐ Step 8: Configure auto-attachment of AI reports to patient chart ☐ Step 9: Document any Dentrix customizations that may affect integration Estimated time: 2–3 hours per location
Eaglesoft Integration
☐ Step 1: Verify Eaglesoft version 21 or higher ☐ Step 2: Obtain DICOM bridge credentials from Denti.AI 🔵 ☐ Step 3: Configure Eaglesoft DICOM export settings ☐ Step 4: Install Denti.AI DICOM listener service ☐ Step 5: Test image routing from Eaglesoft → Denti.AI ☐ Step 6: Configure results return pathway (typically embedded link in chart) ☐ Step 7: Test end-to-end workflow with sample patient Estimated time: 3–4 hours per location
Open Dental Integration
☐ Step 1: Verify Open Dental version 22.1 or higher ☐ Step 2: Enable API access in Open Dental preferences ☐ Step 3: Generate API key for Denti.AI 🔵 ☐ Step 4: Configure Denti.AI with Open Dental API endpoint ☐ Step 5: Test patient synchronization ☐ Step 6: Verify image acquisition workflow integration ☐ Step 7: Test clinical notes auto-population (if configured) Estimated time: 2 hours per location
Step-by-Step Imaging System Integration
Dexis Integration
☐ Step 1: Confirm Dexis version and TWAIN/DICOM capability ☐ Step 2: Configure Dexis DICOM export destination to Denti.AI bridge ☐ Step 3: Set automatic export on image capture (recommended) or manual trigger ☐ Step 4: Test image quality thresholds (Denti.AI may reject low-quality images) ⚠️ ☐ Step 5: Configure return display pathway (Denti.AI overlay viewer vs. results in Dexis) Estimated time: 1–2 hours per location
Schick Integration
☐ Step 1: Confirm Schick sensor compatibility ☐ Step 2: Obtain Schick-specific bridge configuration from Denti.AI 🔵 ☐ Step 3: Configure CDR DICOM export settings ☐ Step 4: Test bidirectional image flow ☐ Step 5: Verify sensor calibration doesn't affect AI analysis Estimated time: 2 hours per location
Carestream Integration
☐ Step 1: Document Carestream software version and modules in use ☐ Step 2: Configure CS DICOM network settings ☐ Step 3: Set up Denti.AI as a DICOM destination node ☐ Step 4: Test with all imaging modalities in use (PA, BW, Pano, CBCT) ☐ Step 5: Configure per-modality analysis settings (some may have different AI models) 🔵 Estimated time: 2–3 hours per location
CBCT-Specific Configuration (if applicable)
☐ Step 1: Confirm CBCT manufacturer compatibility (i-CAT, Carestream, Planmeca, etc.) ☐ Step 2: 🔵 Verify Denti.AI CBCT module is licensed (separate from 2D analysis) ☐ Step 3: Configure DICOM transfer for 3D datasets (file size considerations) ⚠️ ☐ Step 4: Set analysis parameters (slice thickness, region of interest) ☐ Step 5: Test with sample CBCT scan (allow 10–15 minutes for analysis) ☐ Step 6: Configure findings display in CBCT viewer vs. separate report Estimated time: 3–4 hours per location
Test Environment Setup and Validation Checklist
Centralized Test Environment (Recommended for DSOs)
☐ 🔵 Request dedicated test/sandbox tenant from Denti.AI ☐ Populate with synthetic patient data (never use real PHI in test) ☐ Configure test environment to mirror production settings ☐ Grant test environment access to implementation team and champions ☐ Establish clear naming convention to avoid test/production confusion ⚠️
Validation Checklist per Location
| Test Case | Expected Result | Pass/Fail | Notes |
|---|---|---|---|
| Image capture triggers upload | Image appears in Denti.AI within 60 seconds | ||
| AI analysis completes | Findings overlay appears within 90 seconds | ||
| Provider can view findings | Findings display in clinical workflow | ||
| Provider can accept/modify findings | Changes saved and documented | ||
| Treatment plan exports | Procedures appear in PMS | ||
| Patient chart attachment | AI report attached to patient record | ||
| Provider notes integration | Clinical notes include AI findings reference | ||
| Multi-image analysis | FMX analyzed as complete series | ||
| Comparison to prior images | Year-over-year comparison functions | ||
| Audit trail | All interactions logged |
Estimated validation time: 2 hours per location
Data Migration / Historical Image Ingestion
☐ 🟣 Decide scope of historical image ingestion:
- Option A: No historical ingestion (AI only on new images going forward)
- Option B: Limited ingestion (past 12 months for active patients)
- Option C: Full ingestion (all available historical images)
Recommendation for most DSOs: Option B provides comparison value without excessive cost/complexity.
If Ingesting Historical Images:
☐ Identify imaging archive storage per location ☐ Document total image count and storage size ☐ Estimate ingestion timeline (typical: 1,000 images/hour per location) ☐ Schedule ingestion during off-hours to avoid bandwidth impact ⚠️ ☐ Plan for initial AI analysis batch processing (may take 24–48 hours per location) ☐ Verify historical analysis results are correctly associated with patient records ☐ 🔵 Confirm pricing model for historical analysis (may differ from ongoing analysis) Estimated time: 4–8 hours per location depending on archive size
Security and HIPAA Compliance Verification
Enterprise-Level HIPAA Checklist
☐ BAA Execution
- 🔵 Obtain executed BAA from Denti.AI
- Legal review of BAA terms
- Document BAA in compliance files
- Set BAA review reminder (annually)
☐ Data Governance
- Document data flow: image capture → Denti.AI processing → results return
- Confirm data residency (US-based servers for HIPAA compliance)
- Verify data retention policies align with your requirements
- Document data deletion procedures if contract ends 🔵
☐ Access Controls
- Role-based access configured per security policy
- Minimum necessary access principle applied
- User provisioning/deprovisioning workflow documented
- Access logging enabled and reviewed
☐ Encryption
- Confirm encryption in transit (TLS 1.2+)
- Confirm encryption at rest (AES-256)
- Verify key management practices 🔵
☐ Audit Controls
- System audit logging enabled
- Log retention period confirmed (minimum 6 years for HIPAA)
- Establish log review process
☐ Vendor Security Assessment
- Request SOC 2 Type II report from Denti.AI 🔵
- Review security questionnaire responses
- Document any risk acceptances 🟣
- Schedule annual security review
☐ Breach Notification
- Confirm vendor breach notification timeline (≤24 hours)
- Document breach response contact at Denti.AI 🔵
- Test breach notification workflow
Configuration Standards (DSO-Specific)
Standardized Configuration Template
The following settings should be IDENTICAL across all locations:
| Setting | Standard Value | Rationale |
|---|---|---|
| Analysis sensitivity | Medium (vendor default) | Consistent detection rates |
| Finding categories enabled | All FDA-cleared pathologies | Complete diagnostic support |
| Confidence threshold display | Show all ≥70% confidence | Balances noise vs. completeness |
| Auto-treatment plan suggestions | Enabled | Consistent workflow |
| Provider override tracking | Enabled, required | Liability protection |
| Report format | DSO custom template | Brand consistency |
| Retention period | 10 years | Beyond HIPAA minimum |
| Audit logging | Verbose | Compliance protection |
Location-Specific Configuration Allowed
| Setting | Can Vary By | Notes |
|---|---|---|
| Provider preferences | Individual provider | Order of findings, default views |
| Specialty modules | Location type | Ortho-specific AI for ortho locations |
| Integration settings | Local systems | PMS/imaging software specific |
| Display language | Demographics | If multilingual providers |
| Notification preferences | Office manager | Alert thresholds |
6. Team Training Plan
Train-the-Trainer Model
Champion Selection Criteria
The local champion is the linchpin of successful implementation. Each location needs ONE primary champion.
Ideal Champion Profile:
- Office manager OR lead clinical coordinator (not provider)
- Minimum 1 year tenure at location
- Demonstrated technology aptitude (comfortable with PMS, imaging software)
- Respected by clinical staff (providers will take their cues)
- Available for initial 4-hour certification + ongoing 2 hours/week during rollout
- Willing to be accountable for location's training completion
Champion Responsibilities:
- Complete certification training (4 hours)
- Deliver all role-specific training at their location
- Serve as first point of contact for staff questions
- Track training completion for all staff
- Report issues to central implementation team
- Participate in daily/weekly check-ins during rollout
- Provide feedback on training effectiveness
Champion Certification Program 🔵
☐ Complete Denti.AI administrator training (2 hours online) ☐ Complete clinical workflow training (1 hour online) ☐ Shadow a certified champion at pilot location (if Wave 2+) (2 hours) ☐ Demonstrate training delivery capability (deliver sample module to peer) ☐ Pass certification assessment (80% minimum) ☐ Receive champion certification badge and materials
Total certification time: 4–6 hours per champion
Standardized Training Materials
Centrally Created Materials (Do Not Modify)
Provider Training Module (45 minutes)
- AI fundamentals for clinicians
- Clinical workflow demonstration
- Interpreting AI confidence scores
- Override documentation requirements
- Liability and malpractice considerations
Clinical Staff Training Module (30 minutes)
- Image acquisition best practices for AI
- When to alert provider to AI findings
- Patient communication basics
Front Desk Training Module (20 minutes)
- Patient FAQ responses
- Scheduling considerations
- Administrative reports
Billing Training Module (30 minutes)
- Documentation requirements for AI-assisted diagnosis
- Coding implications
- Claim attachment procedures
Day 1 Cheat Sheets (one per role)
- Single-page quick reference
- Printed, laminated, posted at workstation
Champion-Customizable Elements
- Training schedule (adapt to location's patient flow)
- Practice patient cases (use familiar scenarios)
- Local workflow nuances (specific to their PMS configuration)
- Q&A responses (address location-specific concerns)
Role-Specific Training Outlines
Dentists/Providers Training (45 minutes)
Learning Objectives:
- Understand AI detection capabilities and limitations
- Integrate AI findings into diagnostic workflow
- Document AI-assisted diagnoses appropriately
- Know when and how to override AI suggestions
Module Content:
| Topic | Time | Method | Notes |
|---|---|---|---|
| AI in dentistry overview | 5 min | Video | Demystify the technology |
| Denti.AI detection capabilities | 10 min | Presentation | What it catches, what it misses |
| Live workflow demonstration | 15 min | Screen share | Show actual workflow |
| Interpreting confidence scores | 5 min | Discussion | Clinical significance of percentages |
| Override documentation | 5 min | Hands-on | Practice documenting disagreement |
| Liability and documentation | 5 min | Presentation | Address malpractice concerns ⚠️ |
Common Resistance Points:
- "AI will replace me" → AI is a diagnostic aid, not a replacement. You remain the licensed decision-maker.
- "This will slow me down" → Initial learning curve exists; show time savings data from pilot.
- "What if AI is wrong?" → AI provides suggestions; your clinical judgment prevails. Documentation protects you.
- "I don't trust black box technology" → Denti.AI shows reasoning; you can see why it flagged something.
Day 1 Cheat Sheet - Providers:
DENTI.AI QUICK REFERENCE - PROVIDERS
1. AI analysis appears automatically after image capture (30–60 sec)
2. Review findings in overlay view (yellow = AI detected)
3. Click any finding to see confidence score and evidence
4. ACCEPT findings you agree with (auto-documents)
5. MODIFY findings where AI partially correct
6. DISMISS findings you disagree with (MUST document why)
7. Treatment plan suggestions appear after findings review
8. Questions? Ask [Champion Name] or Slack #denti-ai-help
Hygienists Training (30 minutes)
Learning Objectives:
- Optimize image acquisition for AI analysis
- Understand AI findings display
- Know when to flag findings for provider attention
Module Content:
| Topic | Time | Method |
|---|---|---|
| Image quality for AI | 10 min | Demonstration |
| Reading AI overlays | 10 min | Hands-on practice |
| Hygienist workflow integration | 5 min | Discussion |
| Common scenarios | 5 min | Case examples |
Key Workflow Changes:
- Image acquisition technique affects AI accuracy (angulation, positioning)
- AI findings visible during hygiene exam
- New protocol: alert provider if AI detects findings beyond routine calculus
Day 1 Cheat Sheet - Hygienists:
DENTI.AI QUICK REFERENCE - HYGIENISTS
IMAGE QUALITY TIPS:
☐ Proper sensor positioning (AI needs full tooth)
☐ Consistent angulation (reduces false positives)
☐ Avoid cone cuts (AI can't analyze what's not captured)
DURING EXAM:
☐ AI findings auto-appear after images captured
☐ Yellow overlay = AI detection (review with provider)
☐ Flag anything unexpected to provider before they enter room
REMEMBER: You don't diagnose AI findings—that's provider responsibility
Front Desk / Office Manager Training (20 minutes)
Learning Objectives:
- Answer basic patient questions about AI
- Access reporting functions
- Manage scheduling considerations
Module Content:
| Topic | Time | Method |
|---|---|---|
| Patient FAQ responses | 10 min | Role play |
| Reporting dashboard | 5 min | Demo |
| Scheduling notes | 5 min | Discussion |
Patient FAQ Talking Points:
- "Is AI analyzing my X-rays?" → "Yes, we use advanced technology that helps our doctors identify potential issues. Dr. [Name] reviews everything and makes all clinical decisions."
- "Is this safe?" → "The AI analyzes images that are already taken—it doesn't affect the X-ray process at all."
- "Does this cost extra?" → "No, this is part of our commitment to providing you with the best care."
Day 1 Cheat Sheet - Front Desk:
DENTI.AI QUICK REFERENCE - FRONT DESK
PATIENT QUESTIONS:
"Our office uses AI technology to help our doctors identify
potential issues on X-rays. Dr. [Name] reviews everything
and makes all treatment decisions. No extra cost to you!"
SCHEDULING:
☐ No changes to appointment length (AI doesn't add time)
☐ New patients may have slightly longer first visit (comprehensive analysis)
REPORTING:
☐ Daily metrics: [Dashboard Link]
☐ Issues: Contact [Champion Name]
Billing/Insurance Staff Training (30 minutes)
Learning Objectives:
- Document AI-assisted diagnoses correctly
- Understand coding implications
- Attach supporting documentation to claims
Module Content:
| Topic | Time | Method |
|---|---|---|
| AI documentation in clinical notes | 10 min | Examples |
| Coding considerations | 10 min | Presentation |
| Claim attachment procedures | 10 min | Hands-on |
Key Documentation Points:
- AI findings are documented in clinical notes as "AI-assisted detection"
- Provider verification must be documented for any billable diagnosis
- AI reports can be attached to claims as supporting documentation
- No separate billing code for AI analysis (bundled into diagnostic services)
Day 1 Cheat Sheet - Billing:
DENTI.AI QUICK REFERENCE - BILLING
DOCUMENTATION:
☐ AI-assisted diagnoses appear in clinical notes automatically
☐ Provider must verify/approve before diagnosis is billable
☐ Look for "AI-Verified" flag in treatment plan
CLAIM ATTACHMENTS:
☐ AI report can be exported as PDF
☐ Attach to claims when supporting documentation requested
☐ Report shows findings and provider verification
NO CODE CHANGES:
☐ Use same CDT codes as non-AI diagnosis
☐ AI doesn't create new billable services
Training Completion Tracking
Tracking Mechanism
☐ Create training completion tracker (Google Sheet or LMS) ☐ Champion marks completion after each training session ☐ Central team reviews completion weekly ☐ Location cannot go live until 100% completion verified ⚠️
Sample Tracker Structure
| Location | Champion | Providers (3) | Hygienists (2) | Front Desk (2) | Billing (1) | Status |
|---|---|---|---|---|---|---|
| Denver Main | ✓ | 3/3 | 2/2 | 2/2 | 1/1 | Ready |
| Phoenix East | ✓ | 2/3 | 2/2 | 1/2 | 1/1 | Not Ready |
| Tucson West | Pending | 0/4 | 0/2 | 0/2 | 0/1 | Not Ready |
Go-Live Training Requirements
- Champion: 100% certified
- Providers: 100% (no exceptions) ⚠️
- Hygienists: 100%
- Front Desk: 80% minimum (schedule remainder in first week)
- Billing: 80% minimum (schedule remainder in first week)
Ongoing Training Cadence
New Hire Training
| Hire Date | Training Requirement | Deadline |
|---|---|---|
| Within first week | Champion delivers role-specific training | Before first patient interaction |
| Within 30 days | Review Day 1 cheat sheet comprehension | Manager verification |
| Within 90 days | Assess proficiency, address gaps | Performance review |
Refresher Training
| Frequency | Audience | Content | Duration |
|---|---|---|---|
| Quarterly | All staff | Feature updates, workflow refinements | 15 min video |
| Annually | Providers | Clinical accuracy review, new detection capabilities | 30 min |
| As needed | Champions | Advanced troubleshooting, new champion certification | 2 hours |
Training Material Updates 🔵
☐ Establish process for Denti.AI to communicate feature updates ☐ Central team reviews updates and modifies training materials ☐ Champions notified of training material changes ☐ Track version numbers on all training documents
7. Change Management
Executive Sponsor Communication Plan
Board/Investor Updates
| Timing | Format | Content | Owner |
|---|---|---|---|
| Implementation kickoff | Email + deck | Investment thesis, timeline, expected ROI | CEO/CDO |
| Monthly during rollout | Dashboard | Progress metrics, risk register, timeline status | VP Ops |
| Wave 1 completion | Board meeting | Pilot results, go/no-go decision, revised projections | CEO/CDO |
| Full deployment | Press release draft | Competitive differentiation, patient care messaging | Marketing |
| Quarterly post-deployment | Business review | ROI validation, optimization opportunities | CFO |
Sample Board Update Email (Wave 1 Complete)
Subject: Denti.AI Pilot Results - Proceeding to Wave 2
Summary:
We completed our 4-week pilot of Denti.AI at 3 locations.
Key results:
• 23% increase in caries detection vs. pre-implementation baseline
• 15% improvement in case acceptance (patients value AI confirmation)
• No net increase in appointment time
• 94% provider satisfaction (would recommend to colleagues)
Recommendation: Proceed to Wave 2 (8 locations) beginning [date].
Full analysis attached. Questions welcome ahead of next board meeting.
[Executive Sponsor]
Regional Manager Briefing Guide
Pre-Implementation Briefing (Week 1)
Agenda (30 minutes):
What is Denti.AI? (5 min)
- Brief technology overview
- Why the organization is adopting it
Your Role in Implementation (10 min)
- Supporting champion selection at your locations
- Monitoring readiness assessments
- Cascading communications to office managers
- Addressing staff concerns escalated from locations
Timeline and Your Locations (10 min)
- Which wave each location is in
- What to expect during each wave
- Your required participation in go-live support
Q&A and Concerns (5 min)
- Surface any known challenges at your locations
- Identify staff who may resist (address proactively)
Regional Manager Talking Points for Office Manager Conversations
KEY MESSAGES:
1. "This is happening, and your location is in Wave [X]. Let's make it successful."
- Non-negotiable, but supportive tone
2. "You'll have a champion who receives special training. I'd like your input on who that should be."
- Involve them in decision, but maintain central approval
3. "The central team is supporting you. This isn't something you have to figure out alone."
- Reduce anxiety about another initiative they have to manage
4. "Staff concerns are normal. Let me know what you're hearing so we can address it."
- Open channel for resistance signals
5. "After this is done, you'll have one of the most advanced clinical tools available."
- Connect to pride and competitive advantage
Staff Resistance Framework for Multi-Location Dynamics
Common Resistance Patterns at Scale
| Pattern | Signal | Response |
|---|---|---|
| "Wait and see" | Locations delay actions, hope initiative dies | Public timeline commitments, accountability |
| "We're different" | Location claims unique circumstances exempt them | Address specific concerns, hold to standards |
| "Overwhelmed" | Location cites other priorities, no bandwidth | Assess legitimately; either defer wave or provide support |
| "Not invented here" | Staff skeptical because decision came from corporate | Involve staff in customizable elements |
| "Compliance theater" | Location goes through motions but doesn't adopt | Track usage metrics, not just training completion |
Resistance Intervention Escalation
Level 1: Champion-Level (first 48 hours)
- Champion has direct conversation with resistant staff member
- Understand root cause (fear, workload, skepticism)
- Address with information or escalate if beyond champion's ability
Level 2: Office Manager Involvement (if unresolved after 48 hours)
- Office manager reinforces expectations
- Connects to performance standards
- Documents conversation
Level 3: Regional Manager Involvement (if unresolved after 1 week)
- Regional manager has direct conversation
- May involve HR if performance-related
- Evaluate if individual will comply or requires different action
Level 4: Central Team Involvement (exceptional cases)
- Pattern of resistance affecting rollout viability
- Consider location wave deferral
- Address systemic issues (leadership, culture)
Internal Marketing
Initiative Naming
Choose a name that creates identity and momentum. Options:
| Name | Tone | Best For |
|---|---|---|
| "ClearView Initiative" | Clinical, professional | Conservative DSO culture |
| "AI Forward" | Modern, progressive | Innovation-focused DSO |
| "DiagnostiQ" | Clever, memorable | Younger staff demographic |
| "[DSO Name] Smart Imaging" | Branded, proprietary | Strong brand identity |
Recommendation: Keep it simple and connected to patient benefit. "Clear Vision" or "Smart Imaging" resonate without being gimmicky.
Creating Momentum
☐ Announce initiative at all-hands or regional meetings ☐ Create initiative logo/visual identity (optional but increases perceived importance) ☐ Launch Slack/Teams channel for initiative updates ☐ Feature early wins in company newsletter ☐ CEO video message at launch (2 minutes, authentic)
Celebrating Milestones
| Milestone | Celebration |
|---|---|
| Wave 1 go-live | Company-wide email from CDO |
| First 1,000 images analyzed | Social media post (with compliance review) |
| Wave 1 success metrics | Shoutout to pilot locations, small gift for champions |
| Full deployment | All-hands celebration, certificate for every location |
| ROI validation | Board/investor communication, possible external PR |
Champion Recognition
☐ Create "Denti.AI Champion" digital badge for email signatures ☐ Feature champions in company communications ☐ Provide small thank-you gift at wave completion (gift card, branded item) ☐ Consider permanent "early adopter" recognition (framed certificate for office)
8. Go-Live Day Runbook
Standardized Go-Live Checklist (Every Location)
T-minus 3 Days
☐ Confirm 100% training completion for location ☐ Verify integration is live in production environment ☐ Test with sample image (non-patient) ☐ Confirm champion availability for go-live day ☐ Verify contact information for all support tiers ☐ Communicate go-live date to all location staff ☐ Post Day 1 cheat sheets at workstations
T-minus 1 Day
☐ Final integration test (capture image, verify AI response) ☐ Champion confirms schedule for go-live day (recommend lighter patient load if possible) ☐ Remind all staff of go-live tomorrow ☐ Central team confirms monitoring in place ☐ Pre-position troubleshooting guide at champion's workstation
Go-Live Day Hour-by-Hour Schedule
Standard Go-Live Day (Assuming 8 AM Open)
| Time | Activity | Owner | Notes |
|---|---|---|---|
| 7:00 AM | Champion arrives early, final system check | Champion | 30 min before staff |
| 7:15 AM | Central team standby begins | Central Team | Remote monitoring active |
| 7:30 AM | Morning huddle: remind staff of go-live, address questions | Champion + OM | 10 min max |
| 8:00 AM | First patient images captured | Clinical Staff | Monitor closely |
| 8:15 AM | Verify AI analysis completed for first patient | Champion | Flag any issues immediately |
| 9:00 AM | Check in #1: Champion → Central Team | Champion | Quick status: any issues? |
| 10:00 AM | First provider completes AI-assisted diagnosis | Provider | Champion observes if possible |
| 12:00 PM | Mid-day check-in: Champion → Central Team | Champion | Volume processed, issues, staff sentiment |
| 2:00 PM | Afternoon check: any workflow adjustments needed? | Champion | Address small issues immediately |
| 4:00 PM | Check-in #3: Champion → Central Team | Champion | Day going well or concerns? |
| 5:00 PM | End-of-day debrief with staff (5 min) | Champion + OM | What worked? What was confusing? |
| 5:30 PM | Champion reports to Central Team | Champion | Summary email: volume, issues, sentiment |
| 6:00 PM | Central Team daily summary to stakeholders | Central Team | Aggregate across all go-live locations |
On-Site/On-Call Requirements
Wave 1 (Pilot Locations)
- Central team member on-site for first go-live day at each pilot location
- Vendor technical support on standby (confirmed prior to go-live) 🔵
- Regional manager available by phone
Wave 2+
- Champion leads independently with central team remote support
- Central team available via Slack/Teams (response within 15 minutes)
- Regional manager checks in mid-day
Vendor Support Availability 🔵
- Confirm vendor support hours match your go-live day
- Obtain direct phone/Slack for enterprise support (not general queue)
- Pre-schedule vendor check-in call at end of go-live day
Known Gotchas and Troubleshooting ⚠️
Common First-Day Issues
| Issue | Symptom | Immediate Fix | Root Cause Resolution |
|---|---|---|---|
| Image upload fails | AI overlay doesn't appear within 2 min | Refresh browser, retry | Check network, firewall, DICOM config |
| Slow analysis | AI takes >5 min | May be backlog; wait | Contact vendor if persists; server capacity |
| Wrong patient matched | AI findings on wrong chart | Stop; do not proceed | Contact vendor immediately; data mapping error 🔵 |
| Provider can't see findings | Overlay not visible | Clear cache, re-login | Check role permissions |
| Finding categories missing | Only some pathology types showing | Check settings | Verify configuration matches standard template |
| Sensor images not captured | New images don't appear | Check TWAIN/DICOM bridge | Imaging software integration issue |
Troubleshooting Decision Tree
Image captured → Does AI overlay appear within 2 minutes?
│
├── YES → Proceed with workflow
│
└── NO → Refresh browser
│
├── Overlay appears → Proceed (log intermittent issue)
│
└── Still no overlay → Check image in Denti.AI dashboard directly
│
├── Image visible → Integration display issue (log, continue)
│
└── Image NOT visible → Upload failure
│
├── Try re-capture
│
└── If persistent → Escalate to Central Team
│
├── Central Team resolves → Continue
│
└── Cannot resolve → Revert to pre-AI workflow
Log for vendor escalation 🔵
Patient Communication Script (If Tool Visible to Patients)
For Providers (During Exam)
"You may notice some highlights on your X-rays—that's our AI assistant
helping me review your images. It's like having a second set of eyes.
I review everything the AI identifies and make all the decisions about
your care. It's one of the ways we make sure we're providing you with
the most thorough care possible."
For Front Desk (If Patient Asks)
"Great question! We use AI technology to help our doctors analyze
X-rays. It can identify things that might be easy to miss, kind of
like spell-check for images. Dr. [Name] reviews everything and makes
all the decisions about your treatment. It's part of how we stay on
the cutting edge of dental care."
What NOT to Say ⚠️
- Don't say "The AI found a cavity" (AI assists; provider diagnoses)
- Don't promise AI catches everything (it's an aid, not perfection)
- Don't compare to human providers negatively (AI helps providers, doesn't replace)
First-Week Daily Check-In Protocol
Champion → Central Team (Daily, 10 min)
Submission Method: Slack message or short form
Daily Report Template:
LOCATION: [Name]
DATE: [Date]
DAY OF GO-LIVE: [1/2/3/4/5]
Patients with AI-analyzed images today: [#]
Technical issues encountered: [None / Description]
Staff questions or concerns: [None / Description]
Workflow observations: [Any unexpected friction points]
Overall sentiment (1-5): [Champion's rating]
Support needed tomorrow: [Yes - describe / No]
Central Team → Champion (Response)
- Acknowledge receipt within 1 hour
- Provide answers or escalate questions
- Confirm next-day support availability
Regional Manager Check-In
- Days 1 and 3: Quick call with champion (10 min)
- Day 5: Debrief call with champion and office manager (15 min)
Escalation Tiers
| Level | Responder | Response Time | Issue Types |
|---|---|---|---|
| Tier 0 | Champion (self-service) | Immediate | Minor questions, troubleshooting guide items |
| Tier 1 | Central Implementation Team | 15 min during business hours | Technical issues affecting workflow, training questions |
| Tier 2 | Regional Manager | 1 hour | Staff resistance, location-specific blockers |
| Tier 3 | Central IT + Vendor Support 🔵 | 30 min for P1, 4 hours for P2 | Integration failures, data issues |
| Tier 4 | Executive Sponsor (VP/CDO) | Same day | Wave-affecting issues, go/no-go decisions |
P1 vs. P2 Issue Definition
- P1 (Critical): System completely unusable, patient care impacted, data integrity concern
- P2 (High): System degraded, workaround available, workflow significantly affected
- P3 (Medium): Minor functionality issue, workaround easy, limited impact
- P4 (Low): Enhancement request, cosmetic issue, future consideration
9. Post-Launch Optimization (Weeks 4–8)
Weekly Metrics Review Cadence
Weekly Metrics Review Meeting
Attendees: Central implementation team, regional managers (rotating), vendor CSM (optional) Duration: 30 minutes Day/Time: Same day/time each week (recommend Tuesday AM)
Agenda:
- Key metrics review by location (10 min)
- Issue log review—open items, newly resolved (10 min)
- Staff feedback themes (5 min)
- Next week priorities (5 min)
Metrics to Track Weekly
| Metric | Target | Red Flag | Data Source |
|---|---|---|---|
| Images analyzed | >90% of images captured | <75% | Denti.AI analytics |
| AI findings per 100 images | Baseline +/- 10% | >20% deviation | Denti.AI analytics |
| Provider override rate | <30% | >50% | Denti.AI analytics |
| System uptime | >99.5% | <98% | Vendor SLA reporting |
| Support tickets per location | <5/week | >10/week | Support tracker |
| Training completion (new hires) | 100% within 1 week | Any gaps >2 weeks | Training tracker |
30-Day Checkpoint: What "Good" Looks Like
Green Status Indicators (On Track)
- ✓ All locations in wave are live and using system daily
- ✓ >90% of images being analyzed by AI
- ✓ Provider override rate <30% (indicates appropriate AI accuracy)
- ✓ No P1 incidents in past 2 weeks
- ✓ Staff sentiment survey average >3.5/5
- ✓ No significant workflow slowdowns reported
Yellow Status Indicators (Monitor Closely)
- ⚠️ 75–90% of images being analyzed (investigate gaps)
- ⚠️ Provider override rate 30–50% (AI may need calibration or provider needs reinforcement)
- ⚠️ 1–2 P1 incidents in past 2 weeks (track for pattern)
- ⚠️ Staff sentiment 3.0–3.5/5 (identify specific concerns)
- ⚠️ Workflow adding 5–10 min per patient (temporary or systemic?)
Red Status Indicators (Intervention Needed)
- 🔴 <75% of images analyzed (system or adoption failure)
- 🔴 Provider override rate >50% (AI calibration issue or fundamental distrust) ⚠️
- 🔴 Multiple P1 incidents (system instability, escalate to vendor) 🔵
- 🔴 Staff sentiment <3.0/5 (change management failure)
- 🔴 Workflow adding >10 min per patient (operational impact, consider pause)
60-Day Checkpoint: ROI Assessment Framework
Tie Back to Baseline Metrics
| Metric | Pre-Implementation Baseline | 60-Day Actual | Change | Target | Status |
|---|---|---|---|---|---|
| Case acceptance rate | [Captured in Week 1] | [Measured at Day 60] | +/-% | +10% | 🟢🟡🔴 |
| Pathology detection rate | [Captured] | [Measured] | +/-% | +15% | 🟢🟡🔴 |
| Treatment plan value/patient | [Captured] | [Measured] | +/-$ | +8% | 🟢🟡🔴 |
| Diagnosis-related claim denials | [Captured] | [Measured] | +/-% | -20% | 🟢🟡🔴 |
| Provider diagnostic confidence | [Survey: 1-10] | [Resurvey] | +/- | +1.5 points | 🟢🟡🔴 |
ROI Calculation Template
MONTHLY VALUE GENERATED:
Additional production from increased detection:
[Additional procedures/month] × [Avg procedure value] = $________
Additional production from improved case acceptance:
[Baseline patients] × [Acceptance lift %] × [Avg TX plan] = $________
Claim denial reduction:
[Baseline denials] × [Denial reduction %] × [Avg denial value] = $________
TOTAL MONTHLY VALUE: $________
MONTHLY COST:
Denti.AI subscription: $________ [per location × locations]
Internal support time: $________ [estimated hours × loaded rate]
Training (amortized): $________ [one-time cost / 12 months]
TOTAL MONTHLY COST: $________
NET MONTHLY BENEFIT: $________
PAYBACK PERIOD: [Total implementation cost] / [Net monthly benefit] = _____ months
Staff Feedback Collection
5-Question Pulse Survey (Administer at Day 30 and Day 60)
How would you rate your overall experience using Denti.AI? (1 = Very Negative, 5 = Very Positive)
Denti.AI makes my job easier. (1 = Strongly Disagree, 5 = Strongly Agree)
I trust the AI's findings. (1 = Strongly Disagree, 5 = Strongly Agree)
I feel adequately trained to use Denti.AI effectively. (1 = Strongly Disagree, 5 = Strongly Agree)
What one thing would most improve your experience with Denti.AI? (Open text)
Administration:
- Anonymous (encourage honesty)
- Digital survey (Google Forms, SurveyMonkey, etc.)
- Champion sends link, central team analyzes
- Share aggregate results with staff (transparency builds trust)
Common Workflow Refinements (First Month)
| Observation | Common Cause | Recommended Adjustment |
|---|---|---|
| Providers not reviewing all findings | Too many low-confidence findings displayed | Raise confidence threshold from 70% to 80% 🔵 |
| Hygienists not flagging AI findings | Unclear protocol | Reinforce training; add step to huddle protocol |
| Front desk can't answer patient questions | Insufficient training | Additional FAQ role-play session |
| AI analysis not appearing for some image types | Configuration gap | Audit imaging workflow, verify all modalities configured |
| Providers overriding findings inconsistently | No documentation standard | Implement required override reason field |
| Staff checking both old system and AI | Parallel workflow extended too long | Set hard cutoff date for parallel workflow |
Centralized Dashboard Structure (DSO)
Per-Location Metrics (Operational Level)
| Metric | Location A | Location B | Location C | ... | Portfolio Avg |
|---|---|---|---|---|---|
| Images analyzed this week | 342 | 289 | 401 | 344 | |
| AI findings per 100 images | 12.3 | 14.1 | 11.8 | 12.7 | |
| Provider override rate | 18% | 31% | 22% | 24% | |
| Avg analysis time (seconds) | 45 | 52 | 48 | 48 | |
| Support tickets | 2 | 5 | 1 | 2.7 | |
| Staff sentiment (last survey) | 4.1 | 3.6 | 4.3 | 4.0 |
AI-generated implementation guide based on public vendor information. Verify specifics directly with Denti.AI.