Beyond Benchmarking: High-Fidelity Simulations for Dental AI Agent Evaluation
Elevating Our 2025 Event Strategy
Agent-based modeling (ABM) is a computational framework that simulates the actions and interactions of autonomous agents to gain insights into system-level behavior and outcomes. Applying ABM to LLM evaluation allows for the following:
- High-Fidelity Simulations
Crafting realistic clinical scenarios where agents interact dynamically, mirroring real-world complexities.
- Workflow Impact Assessment
Evaluating how LLM agents influence clinical workflows, including task completion and decision-making processes
- Comprehensive Metrics
Assessing chat quality criteria, engagement levels, user frustration, function generation, parameter extraction, and routing capabilities.
Challenges in Testing Conversational Agents
Testing agents is often tedious and repetitive, requiring human validation of response semantics. The dynamic nature of agent interactions presents challenges:
- Semantic Validation
Ensuring responses are contextually appropriate and semantically accurate.
- Dynamic Conversations
Managing unpredictable multi-turn dialogues.
- Automation Integration
Incorporating testing into existing CI/CD pipelines without disrupting workflows.
Peerlogic's Evaluation Framework
To overcome these challenges, Peerlogic's evaluation framework offers:
- Simulator for Environment Creation
The simulator creates a high-fidelity clinical environment where simulated patients, each with a unique persona, interact within practices configured to match their offered procedures. This approach provides a dynamic and realistic evaluation landscape, contextualizing the environment to reflect real-world dental workflows.
- Quantitative Analysis of Tool Calling and Parameter Extraction
We quantitatively analyze the agent's ability to call appropriate tools and accurately extract necessary parameters, ensuring the agent performs tasks correctly.
- LLM as Judge for Automated Evaluation
We automate the evaluation process by leveraging LLMs as judges. The LLM acts as an evaluator, validating the agent's responses and actions and producing results for automatic tests without manual intervention.
- Concurrent Multi-Turn Conversation Orchestration
Simulating multiple dialogues simultaneously to assess agent performance under varied conditions.
- CI/CD Pipeline Integration
Automating agent testing within continuous integration and delivery processes to streamline development.
- Detailed Performance Summaries
Generating comprehensive reports, including conversation histories, test pass rates, and reasoning for pass/fail outcomes.
Quantitative Analysis and Automated Evaluation
Our framework quantitatively assesses vital aspects of agent performance:
- Tool Calling Efficiency
Evaluating how effectively the agent selects and invokes appropriate tools during interactions.
- Parameter Extraction Accuracy
Measuring the agent's precision in extracting necessary parameters from conversations.
- Automated Validation with LLM as Judge
Employing an LLM to automatically validate the agent's responses within the simulation environment, reducing the need for human oversight.
Conclusion
By employing agent-based modeling to evaluate LLM-based conversational agents in dental healthcare, we gain nuanced insights into their capabilities and limitations. Peerlogic overcomes traditional, labor-intensive evaluation methods by quantitatively analyzing tool usage and parameter extraction and automating the process using LLMs as judges—enhancing assessments and contributing to improved patient outcomes by ensuring AI agents operate effectively and safely within dental workflows.
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Voice vs. Text: The Data
When patients are prompted to engage by voice first, response rates are only 7%.
When prompted by text first, response rates jump to 60%+.
And it doesn’t stop there: of the patients who engage Aimee and take an action (book, cancel, reschedule), nearly 30% return via text for other needs—like confirming appointment times, asking about insurance, or double-checking directions.
This isn’t a small difference. It’s a fundamental signal.
What This Means
The takeaway is clear: people prefer to type, not talk, when starting an interaction.
Why? A few reasons stand out:
- Control: Text lets patients communicate at their own pace, without feeling rushed.
- Privacy: Not everyone wants to speak out loud—especially if they’re at work, in public, or just not in the mood to talk.
- Clarity: With text, patients can double-check details and reduce miscommunication.
- Comfort: For many, a quick written response feels less intimidating than making a call or recording their voice.
The Bigger Picture
This early data reflects a broader trend we’re seeing across industries: patients (and customers in general) want low-friction, on-their-terms communication. They’re not rejecting voice altogether, but they’re choosing to start with text.
And once that initial wall is down, they’re far more open to follow-ups, appointments, and even a call if needed.
Connecting the Dots with AI
That’s exactly what the workflow below shows:
Instead of forcing patients into one communication style, AI adapts.
- If the phone rings three times, AI answers with options.
- If a call is missed, AI automatically follows up with text.
- If someone visits your website, AI is available instantly via web chat.
From there, AI collects patient information, answers FAQs, and even schedules appointments directly into your office management system.
The end result is a seamless experience that feels natural to patients and removes the burden from your front desk.
Why It Matters for Dental Practices
For practices, the implications are huge:
- If your digital front door starts with voice, you’re leaving engagement (and revenue) on the table.
- Meeting patients where they are—text-first—removes barriers and builds trust right away.
- Practices that prioritize text-first engagement will see more conversations convert into booked appointments and ongoing relationships.
What Are Your Options?
Early data is telling us loud and clear: text is the front door, voice is the follow-up.
Practices that adapt to this patient preference aren’t just keeping up with the times, they’re creating a patient experience that feels natural, modern, and respectful of choice.
Scaling Dental Service Organizations (DSOs) requires more than adding new locations or hiring more staff. It’s about creating a repeatable model that integrates people, processes, and technology in a way that can flex as you grow. Industry leaders consistently point to the same truth: without alignment, scale collapses under its own weight.
“Scaling without a foundation is like adding floors to a building without reinforcing the beams on it. Eventually something’s gonna crack.”
A unified system with standardized Key Performance Indicators (KPIs) and cross-department visibility is non-negotiable. This foundation helps DSOs avoid blind spots, reduce inefficiencies, and ensure leadership has a clear line of sight into performance across all locations.
Consistent Front Office Operations
The front desk is the patient’s first impression — and often where inconsistency strikes hardest across multi-location DSOs.
- Establish benchmarks and Standard Operating Procedures (SOPs) for intake, scheduling, and follow-up.
- Monitor front office KPIs like call answer rate, conversion rate, and appointment confirmation rate.
“Not having a benchmark is a benchmark in itself.”
Consistency here doesn’t just improve patient experience — it creates predictability and efficiency at scale.
Reducing Missed Call Rates
Missed calls are more than operational hiccups — they’re direct revenue loss. Potential patients rarely leave voicemails; they move on to the next provider.
- Track call answer and abandonment rates in real time.
- Implement scripting and call management tools to improve handling.
- Use AI-assisted systems to recover missed calls and return messages quickly.
Every call answered is revenue retained.
Marketing Funnel Optimization
From lead generation to treatment acceptance, tracking the entire funnel is essential. DSOs that measure only at the top (leads) or bottom (treatment acceptance) miss critical leaks in the middle.
“If all you track is production, you’re only watching the scoreboard at the end of the day, not the plays that got you there.”
- Map the patient journey from first contact to treatment.
- Identify where drop-offs occur (e.g., appointment no-shows, insurance verification delays).
- Use attribution data to double down on high-performing channels.
Optimization isn’t about adding more leads , it’s about converting the ones you already have.
Overcoming System Fragmentation
Fragmented systems and siloed data make scale chaotic. A cohesive tech stack is key:
- Integrate lead generation, patient management, and marketing platforms.
- Ensure there’s a single source of truth for metrics across all departments.
- Eliminate redundant tools and unify reporting dashboards.
When systems talk to each other, leaders make faster, smarter decisions.
Standardizing KPIs Across Departments
Scaling falters when departments track different metrics. Alignment means everyone measures success the same way.
- Standardize business impact KPIs like patient show rates, treatment acceptance, and revenue per visit.
- Create cross-department scorecards that roll up into executive-level reporting.
This shared accountability fosters collaboration and keeps teams focused on the same outcomes.
Balancing Technology with Process Improvement
“AI is not gonna magically fix a bad process.”
Technology should accelerate good processes, not patch broken ones. Before automating, DSOs must refine their workflows.
- Audit current processes to identify inefficiencies.
- Standardize improvements before introducing automation.
- Train staff to adopt both the process and the technology together.
“Technology is there to help you, absolutely. Our job is to adapt to it, refine our process, and make sure it’s as efficient as possible.”
Automation without clarity simply scales chaos.
Continuous Innovation and Adaptation
Finally, successful DSOs never stop testing. Allocate resources to pilot programs, new strategies, and process experiments.
- Test new patient engagement channels (text, chat, AI call recovery).
- Explore new marketing platforms or attribution models.
- Track pilot results and scale what works.
“Change or get out of the way, unfortunately.”
Adaptability is the edge that separates stagnant DSOs from leaders in growth.
Scaling DSOs isn’t about speed; it’s about sustainable structure. By reinforcing foundations, standardizing KPIs, reducing inefficiencies, and leveraging technology to support (not replace) people, DSOs can scale with confidence.
The outcome? More predictable growth, better decision-making, and most importantly, a patient experience that doesn’t suffer as you expand.
👉 For more insights and a step-by-step roadmap, check out the comprehensive workbook, and watch the webinar replay.
A growth-stage DSO can’t afford misaligned metrics, disconnected systems, or siloed decision-making. Without a shared framework, small inefficiencies compound across every location — eroding performance and slowing expansion. The 90-Day Alignment Roadmap gives you a clear path to unify KPIs, integrate data sources, and create a reporting cadence that drives accountability and speed. It’s not theory; it’s a practical toolkit you can put into action right away to build measurable, scalable growth.
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