AI vs. Algorithms for Dental Billing

Dental billing is still stuck in the dark ages.

Old-fashioned paper statements are still ubiquitous, and the way those are sent is primarily still manual and time-consuming.

Fortunately, that’s all changing now.

With the advent of AI in dental, essential components of practice operations from imaging to patient scheduling are experiencing a renaissance. The same is true for billing.

KEY TAKEAWAYS

• There are significant tradeoffs between a fully AI-automated system and rules-based algorithms for patient billng. From efficacy to system control, both technologies will offer a different experience to practices and patients.
  
  
• While dental AI software is not quite ready for managing the intricacies and complexity of patient billing, there are RCM solutions that are currently available that are bridging the gap between workflow algorithms and AI.

The future of dental patient billing with AI

Because wouldn’t it be great to have an AI assistant that could:

1. Identify and create cohorts for patients that are eligible for billing
2. Send secure statements to patients (or the AI assistants of patients) that are personalized in tone and modality for each individual
3. Analyze payment and cash flow patterns, and provide actionable steps to course-correct from declining trends

The use cases are endless, and as your Practice Billing AI learns from your practice (and thousands of other practices simultaneously), it will continue to refine its knowledge about how dental patients behave and pay their bills.

But patient billing within dental is incredibly complex with layers of edge cases and conflicting systems that make a fully autonomous AI-powered system a dubious prospect—at least right now.

Because let’s face it, billing is a “touchy” subject.

Relinquishing control and letting a self-regulating and self-learning system run wild with your practice’s finances is a scary idea. It’s even scarier considering that your patients’ financial data will also be intertwined in this system.

So before we’re inundated by a dystopian future of AI running your books, let’s consider what’s out there now, and how it’s positioned to work in conjunction with nascent AI systems.

AI Billing vs. Algorithm Billing

Let’s consider some definitions.

By AI Patient Billing, we are referring to an AI system that autonomously audits patient accounts, sends statements to patients, collects payments, writes back data to your ledger, and generates reports that the office manager or RCM team can use to instruct the system to iterate its approach. This system will require zero staff intervention on a day-to-day basis.

By Algorithm Patient Billing, we are referring to the current patient billing systems that use rules-based workflows and a dunning process to filter and identify patient accounts, and send statements across communication channels like SMS and email.

This is what software like Pearly accomplishes and is improving to meet future needs of dental organizations. Factors including how old a balance is, which CDT codes are referenced, and what kind of insurance the patient has are all considered in these systems. 

On the surface, it might seem like AI-powered systems will win outright once it’s ready; however, there will be numerous tradeoffs once the two co-exist.

To that end, we've compiled some pros and cons of each system to compare and contrast the two:

AI-Billing - Pros:

1. Adaptive Learning: Continuously optimizes billing strategies based on outcomes and payment patterns without manual intervention
2. Predictive Analysis: Can forecast which patients are likely to pay and prioritize collection efforts accordingly 
3. Personalization: Tailors payment plans and communication strategies to individual patient history and behavior 
4. Anomaly Detection: Identifies unusual billing patterns that might indicate errors or fraud 
5. Natural Language Processing: Can interpret unstructured data from patient communications to improve collection strategies 
6. Complex Pattern Recognition: Identifies subtle correlations between patient demographics, procedure types, and payment likelihood 
7. Dynamic Prioritization: Automatically adjusts collection priorities based on multiple factors (balance age, amount, patient history) 

AI-Billing - Cons:

1. Higher Implementation and Ongoing Operational Costs: Can require significant initial investment, and may require subscription fees and regular updates
2. Black Box Problem: The AI decision-making processes may lack transparency and be difficult to explain (to patients and new staff) and alter the billing operations once activated.
3. Potential Bias: AI systems can perpetuate existing biases in historical data
4. Training Requirements: Needs substantial historical data to perform effectively. Can can be addressed at scale.
5. Technical Complexity: May require specialized staff or consultants to maintain, adding to overhead costs.
6. Regulatory Compliance Concerns: May raise questions about decision transparency for compliance purposes.
7. Integration Challenges: Often more difficult to integrate with legacy practice management systems.

Algorithmic Systems - Pros:

1. Transparency: Clear, predictable logic that staff can understand and explain to new staff and to patients with billing questions.
2. Lower Costs: Generally less expensive to implement and maintain.
3. Consistent Application: Applies the same rules to all patients without variation from pre-set logic.
4. Simpler Implementation: Easier to integrate with existing systems without interrupting practice operations.
5. Predictable Outcomes: Results are consistent and predictable based on defined rules, which can be tailored to your practice's needs.
6. Easier Compliance Documentation: Clear decision paths for regulatory requirements
7. Less Data Dependency: Can function effectively with less historical practice data.
8. Direct Control: Practice administrators can directly modify rules without technical expertise.

Algorithmic Systems - Cons:

1. Binary Decision-Making: Typically uses yes/no logic rather than probability assessments.
2. Updates Required: Rules must be manually updated as conditions change or edge cases present themselves.
3. Missed Opportunities: May miss collection opportunities that don't fit predefined patterns.
4. Limited Personalization: Applies same approach to diverse patient situations. Pearly solves this with Dynamic Language application.
5. Lack of Learning Capacity: Doesn't automatically improve from outcomes without manual intervention or updates.

Why dental AI billing isn’t ready yet

Understanding this comparison, it’s difficult to point to a purely AI-powered billing solution and say that it’s the future panacea for all patient billing. Both conceptually and technologically it’s not quite there yet, and the dissonance is deafening.

The transition from manual billing to AI-powered systems might cause whiplash to both patients and practices, and can ruin those relationships in many cases. 

The stepping stone between those two approaches is rules-based algorithms.

With billing workflows from solutions like Pearly, your practice can bridge that gap as AI is tested in the crucible of the dental industry. 

Remaining in full control of who is billed, when, and with what language is paramount to maintaining both healthy cash flow and strong relationships with patients.

If you want to learn about how Pearly is trailblazing the industry toward an AI-powered future, you can book a free demo to tour our billing platform.