TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion and thought leadership area. We welcome back Iris Telehealth for today’s topic on how telehealth and clinical dashboards should be like modern Formula 1 race cars’–displaying the relevant information for that patient visit and increasing clinician presence versus being overwhelmed by data and documentation load. The author, David Bartley, is Iris Telehealth’s Chief Solutions Officer, with over 20 years of experience leading healthcare product strategy across payers, providers, and population health organizations including MedeAnalytics, Cotivi (formerly Verisk Health), eviCore, Healthways and Humana.
Behavioral health organizations are already absorbing a level of administrative pressure that has nothing to do with clinical care. I’ve spoken with CEOs of rural mental health clinics who are deciding between keeping a clinician on staff and sustaining the administrative infrastructure required to stay compliant.
One told me he had seven people on staff doing nothing but collecting state reporting data — compliance work that consumed payroll he would rather put toward clinical capacity. That tradeoff is exactly what funding pressure forces, and it is where AI can do something genuinely useful: absorbing the administrative work so that the next available dollar goes toward a clinician, not a spreadsheet.
What’s keeping organizations from achieving this reality with AI? Well, most of the industry is measuring AI’s value primarily through operational metrics, like how many more patients a provider can see, how fast documentation gets processed, or how lean an operation can run. Those are important operational metrics — but on their own, they don’t tell us whether care actually improved.
The organizations that figure out how to use AI to deepen the quality of each encounter, rather than multiply the number of them, will move care forward.
Leading health systems are already building toward it. At Mayo Clinic, for example, investments in integrated, AI-enabled platforms are helping bring together multimodal clinical data and advanced analytics in ways that surface the most relevant insights at the moment of care. It’s a signal that the industry is moving beyond AI as an efficiency tool and toward AI as a clinical intelligence layer.
Surfacing what matters, exactly when it matters
When I think about what AI should look like inside a clinical encounter, I keep coming back to Formula One. A driver at race speed does not receive every data point the car is generating. They get tire temps, oil pressure, wind shear — the six or seven variables that matter most in that exact moment, surfaced at eye level in what is known as a heads-up display, so they can keep their focus on the track.
A telehealth dashboard should work the same way. Data like relevant patient history, active risk factors and behavioral signals worth exploring, all fixed at the top of the screen. Something buried in a clinical note from two years ago that has not been relevant for months could resurface the moment the conversation makes it useful again. The goal of AI in behavioral health should not be to simply accelerate care delivery but to increase the signal-to-noise ratio inside each clinical moment.
The provider never has to look away from the patient to find it, and the patient never loses the sense that the person on the other side of the screen is fully present. In behavioral health especially, where the therapeutic relationship is the mechanism of care, the quality of that connection often determines whether treatment actually works.
The value of presence
A JAMA Network study published in October 2025, with data collected in 2024, on ambient AI scribes found that documentation load directly erodes the attention providers can bring to each encounter. Clearing that load only to fill the time with more appointments trades one problem for another.
Providers need time between encounters to process what they heard, notice what did not get said and carry genuine context into the next conversation. That kind of reflection is where clinical judgment actually develops and is exactly what gets squeezed out when efficiency becomes the primary design constraint.
Patients who feel that a provider is fully present are more likely to disclose accurately, follow through on treatment, and stay engaged over time. AI designed around that outcome becomes a powerful tool that actually earns its place in the workflow.
Formula One teams do not build heads-up displays to increase the number of races a driver competes in. They build them to make each race winnable. The industry would do well to borrow that distinction.
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