The healthcare industry is in the midst of an AI revolution. OpenAI, Microsoft, and other tech giants are pouring resources into projects like HealthBench—a new initiative aiming to set rigorous, clinical-grade standards for AI performance across a spectrum of healthcare tasks. The goal: to ensure AI can be trusted, measured, and safely integrated into the complex world of patient care.
But what does this mean for ABA (Applied Behavior Analysis) practice owners and the broader autism service community? For those who’ve built companies on ethical, sustainable growth and client-centered care, the benchmark conversation is more than tech hype. It’s about...
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how our rapidly evolving field is at risk of swapping one kind of clinical guesswork for another, unless we get intentional—now.
What Is HealthBench? And Why Should ABA Business Leaders Care?
HealthBench is an open, collaborative research project, launched in early 2025, that provides a rigorous benchmarking platform for evaluating large AI models on diverse healthcare tasks. Unlike tech demos that cherry-pick a handful of use cases, HealthBench challenges AI models with everything from medical imaging to patient dialogue and administrative workflow, using real-world data.
What makes HealthBench different from previous efforts is its commitment to transparency and peer-reviewed, open-access performance data. As Forbes reports, the hope is that this will help the industry separate hype from real, clinically-relevant AI capabilities. It’s no longer enough for an AI to “look smart” on a PowerPoint—if it can’t pass the HealthBench battery, it’s not ready for real patients.
For ABA practice owners, this signals two things: 1. AI is coming—fast—to all areas of healthcare, including behavioral health and autism services. 2. The time to understand and shape how AI benchmarks are set—and who benefits from them—is now.
The Unique Risks in ABA: A Field Still Defining Its Own Standards
It’s no secret that ABA is still new to the world of insurance, compliance, and large-scale service delivery. Most BCBAs in practice have less than five years’ experience. New business models are still emerging, and, as research confirms, there’s already a wide range of recommendations for something as foundational as treatment hours.
A recent paper published in the journal Behavior Analysis in Practice (Springer, 2025) found striking variability in how BCBAs determine recommended hours for clients—even among those working in similar settings with similar cases. This variability isn’t just a matter of personal style: it reflects different levels of experience, organizational culture, payer expectations, and local norms.
For parents and payers, this means there is often no clear standard. For business owners, it’s a compliance risk—and an ethical minefield.
The Promise and Peril of AI in ABA
At first glance, the promise of AI seems tailor-made for ABA. Imagine a smart assistant that helps schedule, document, and even support clinical decision-making with evidence-based recommendations, freeing up staff for more client time.
But here’s the concern: when a field as variable as ABA plugs its data into an AI “benchmark,” whose standard is the machine learning from? If treatment hour recommendations already swing by 30–50% between clinicians, what’s to stop an AI from encoding the average as the “right” answer, regardless of individual client needs?
Even worse, what happens when AI is embedded within billing software, scheduling platforms, or even payers’ own utilization review tools? If an AI-powered billing platform profits more when it recommends more hours, there’s a subtle but powerful incentive to nudge recommendations upward. A child who may need 15 hours could suddenly be assigned 18. At scale, that’s a massive increase in revenue for the software vendor—and a huge risk for ethical ABA providers.
This isn’t a hypothetical concern. Forbes’ coverage of HealthBench and other AI benchmarking initiatives points to the urgent need for not just technical transparency, but ethical and operational safeguards. The benchmark isn’t just about “can the AI do it?”—it’s about who decides what “good enough” really means, and who gets to check the work.
When Clinician Experience Is Shallow, AI Can Easily Take Over
The reality is this: with high caseloads, rapid staff turnover, and pressure to meet billing targets, newer BCBAs are often looking for shortcuts, guidance, or ways to make decisions more efficiently. If the field itself is uncertain—if even seasoned clinicians are making educated guesses about hours, intensity, or supervision—how much more likely is it that less experienced staff will simply defer to the “smart” tool in front of them?
AI doesn’t need to replace judgment to cause harm; it just needs to become the path of least resistance.
This is why benchmarks like HealthBench matter so much. They force the industry to ask, “How do we know this is working?” and “Who decides what success looks like?” But for ABA, a young field with a shaky consensus on standards, there’s an added danger: the risk of standardizing our own inconsistency .
Ethical Leadership in the Age of AI
So what should ABA business owners and clinical leaders do, given this landscape?
- Demand Transparency: If you’re evaluating AI tools for scheduling, billing, or clinical support, ask for the evidence. Who validated the model? What data was it trained on? Is there third-party, peer-reviewed evidence that it does what it claims?
- Keep Clinical Judgment at the Center: Make it clear to staff that AI is a tool—not a replacement for individualized assessment, parent input, and supervision. Reinforce this in your onboarding, handbooks, and training. Don’t let the tool become the authority.
- Understand the Incentives: Always ask, “Who profits if I follow this AI recommendation?” If a billing or scheduling vendor stands to benefit, scrutinize the logic. If a payer mandates use of an AI platform, push back for access to the benchmark data.
- Educate Clients and Families: Transparency shouldn’t stop with staff. Help families understand how decisions are made, how AI may (or may not) be used, and what their rights are if they disagree with a recommendation.
- Stay Active in the Policy Conversation: Benchmarking efforts like HealthBench are shaping the future of healthcare standards. ABA practice owners need to be in these conversations, advocating for benchmarks that reflect the field’s best values, not just what’s most profitable or efficient.
The Takeaway: Don’t Outsource Ethics to Algorithms
The promise of AI in healthcare is huge—but so are the risks, especially in fields still defining their own standards. The best ABA businesses won’t just adopt whatever tool comes along; they’ll stay focused on what matters: ethical, transparent, client-centered care.
Let’s use benchmarks wisely. Let’s push for AI that truly helps—not just for efficiency or revenue, but for better outcomes, less variability, and more accountability. Because in the end, the real benchmark for ABA isn’t how many hours an algorithm can recommend—it’s how many lives can be changed for the better, one family at a time.
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