The following is a guest article by Shalini Balakrishnan, Senior Engineering Manager at blueBriX

Conversations about AI in healthcare often sound like two people talking past each other. One camp sees AI as a game-changer that can expand access, cut through inefficiencies, and bring much-needed data insights to the frontlines. The other warns of cold, unempathetic “digital therapists” that risk doing more harm than good.

In behavioral health, both the above scenarios aren’t just a theory anymore. States are stepping in with legislation, researchers are publishing evidence on both promise and pitfalls, and providers are feeling the weight of real-world pressures that AI might help ease. For clinicians, administrators, and policymakers, the question has shifted from “Should AI be integrated into behavioral health EHR?” to “How do we use it responsibly, without losing the human connection at the heart of care?”

The Challenges are Real: Burnout and Bias

To understand why AI matters in behavioral health, we first need to look at the routine realities of providers and patients. Clinicians spend long hours wrestling with documentation, compliance tasks, and administrative demands that pull them away from quality face-to-face care. It’s no surprise that burnout is at an all-time high, with many providers feeling they’re losing the very sense of purpose that drew them into the field.

Bias adds another layer of complexity. Even the most dedicated professional can be influenced by unconscious assumptions or the fatigue that comes after back-to-back sessions. This can creep into assessments, treatment plans, and follow-ups—creating uneven outcomes that affect patient trust and safety.

These challenges aren’t isolated issues. They feed into one another, amplifying strain on providers and risks for patients. That’s where AI agents, when designed and used responsibly, start to show their potential.

What AI Agents Bring to the Table

So how do we start easing this pressure without compromising care? AI agents come in not as replacements for clinicians, but as tireless co-pilots that work in the background to lighten the load and fill in the gaps.

Take documentation. AI-powered scribes and ambient tools are already proving their worth in major health systems, cutting down hours of after-work charting and helping providers reclaim time for direct patient interaction. Less paperwork means less burnout—and more energy for the clinical conversations that actually move care forward.

AI agents also excel at spotting what the human eye or ear might miss. By analyzing patient self-reports, EHR histories, or subtle cues in speech and text, they can flag warning signs like mood shifts or suicidal ideation in real time. These early alerts give providers a crucial safety net, helping them step in before risks escalate.

Bias, too, can be mitigated. Structured, AI-driven assessments trained on diverse data can reduce the variability that creeps in when fatigue sets in or unconscious bias shapes decisions. While not perfect, these systems can bring more consistency and fairness to the process—so patients receive care based on need, not chance.

But the Question Remains: What Happens When AI Isn’t Guided by Human Oversight?

For all the promise AI brings, there’s a reason regulators are stepping in. Left unsupervised, AI in behavioral health can do real harm. Several states, including Illinois, Nevada, and Utah, have already taken action—banning or tightly restricting AI systems that attempt to provide therapy or treatment without human oversight.

The concern isn’t abstract. Studies and real-world cases have shown AI chatbots mishandling high-risk situations, from failing to recognize suicidal ideation to offering misguided or even dangerous advice. Some patients, after prolonged interactions, have reported experiences now being described as “AI psychosis”—a state of confusion or paranoia triggered by overreliance on digital companions.

These stories underscore an important truth: technology, no matter how advanced, lacks empathy. It cannot replace the nuanced judgment, emotional intelligence, and human connection that are at the heart of effective behavioral health.

How to Deploy AI Responsibly in Behavioral Health

Think of it as a co-pilot—handling the routine, flagging the risks, and making space for providers to do what only they can do: connect with patients.

• Take administrative burden, for example, documentation, billing, and compliance are essential but draining tasks that consume hours of a provider’s week; AI agents can automate much of this work—drafting notes, completing forms, even scheduling follow-ups—while keeping providers in the driver’s seat; the provider still reviews and authorizes the final action, ensuring oversight and accuracy
• AI can also act as a second set of eyes and ears; by scanning patient-reported data and conversational cues, these tools can surface red flags like self-harm ideation or sudden mood changes; the clinician still makes the judgment call, but now with an added layer of safety and awareness
• And when it comes to bias, AI offers another way forward; properly trained on diverse datasets and embedded within human-led workflows, these tools can reduce subjective variation in assessments; the result: more consistent, data-informed decisions that improve fairness across patient populations—always reviewed and acted upon by providers

In this model, AI isn’t replacing providers—it’s empowering them to practice at the top of their license, spend more time with patients, and deliver care that is both safer and more equitable.

What the Research Shows

This isn’t just theory. A growing body of research is showing what happens when AI is used to support rather than replace providers.

A recent survey published in JAMA Network Open followed nearly 1,400 clinicians using ambient documentation tools. Within just six to twelve weeks, many reported a measurable drop in burnout—directly linked to AI scribes handling their notetaking. 

Other studies, including work in The Milbank Quarterly, highlight how AI tools can reduce overload from insurance claims, repetitive documentation, and constant message traffic. By automating these repetitive but necessary tasks, clinicians reported having more bandwidth for direct care.

Even outcomes are starting to reflect the benefits of collaboration. Research published through Springer has found that blended models—where clinicians retain decision-making power but rely on AI for augmentation—consistently outperform both “AI alone” and “human alone” approaches. The evidence is clear: when humans and AI work together, the results are better for both providers and patients.

Of course, better outcomes don’t come automatically. They depend on how thoughtfully the technology is deployed—and whether the right guardrails are in place.

How Do We Make Sure AI Strengthens Behavioral Health Instead of Undermining It

The answer lies in guardrails—clear boundaries that keep technology in its place.

• First, human oversight is non-negotiable; final decisions about treatment plans or therapy must rest with qualified clinicians
• Second, bias has to be monitored continuously; remember that tools trained on flawed or incomplete data risk reinforcing disparities rather than reducing them; regular audits and diverse training sets are critical to ensuring fair outcomes across race, gender, age, and socioeconomic status
• Third, privacy and compliance can’t be an afterthought; HIPAA, 42 CFR Part 2, and similar regulations must govern every layer of data handling; patients need transparency and consent, not fine print
• Finally, boundaries matter; AI should handle what it does best—reminders, assessments, documentation, trend detection—while clinicians lead on therapy, crisis intervention, and care decisions; patients and providers alike must know where AI begins and ends

With these safeguards in place, AI agents can provide meaningful support without eroding the trust and human connection that define behavioral health. And that trust carries over to another key consideration: choosing the right platforms.

What Should You Look for in an AI Platform That Promises to Support Behavioral Health Care

Start with the basics. Documentation and scoring features should be robust enough to draft notes automatically, generate consistent assessment scores, and track trends over time. These are essential for reducing burnout and improving the reliability of care.

Customization is just as important. Behavioral health workflows vary widely, and rigid, one-size-fits-all systems often create more problems than they solve. The ability to tailor assessments, alerts, and forms to your organization’s needs ensures that the technology adapts to you—not the other way around.

Privacy and security are another must-have. From encryption to consent management to audit logs, platforms need to be built for compliance from the ground up. Anything less risks undermining patient trust.

Finally, look for tools that go beyond the basics of record-keeping. Features like real-time risk detection, natural language processing, and asynchronous patient engagement can extend the reach of providers and improve care continuity. Some behavioral health platforms already highlight these strengths—for example, through capabilities that enable personalized care built into the EHR environment. 

And that sets the stage for the bigger picture: where AI fits into the future of behavioral health.

The Path Forward: Innovation with a Human Touch

Clearly, the future of AI in behavioral health isn’t about machines taking over therapy sessions or replacing clinicians. It’s about weaving technology into care in ways that reduce pressure, close gaps, and make the human connection stronger.

For providers and policymakers, the path forward is clear. Adopt systems that ease documentation, improve detection, and extend access—while keeping humans firmly in control of decisions and relationships. Patients don’t want a “digital therapist.” They want care that feels personal, safe, and responsive, no matter when or where they need it.

If we get this balance right, AI agents can evolve into reliable 24/7 companions: always on, always supportive, never replacing the empathy and expertise that only humans can bring.

About Shalini Balakrishnan

Shalini Balakrishnan is a Senior Engineering Manager at blueBriX, bringing over 16 years of technology expertise. Her focus involves bringing business intelligence and AI-based solutions to the healthcare domain. With 12+ years specializing in Health IT solutions, she has delivered several technology solutions while leading engineering teams in Revenue Cycle Management, EHR development, and patient engagement areas. As an AI enthusiast to the core, she’s passionate about transforming patient care through intelligent technology that makes healthcare more accessible and impactful.