Beyond the Scribe: What a Real “Co-Pilot” for Care Advisors Would Have to Do

Ambient AI documentation tools have quietly become one of the biggest wins artificial intelligence has delivered inside U.S. healthcare. Clinicians spend an estimated 40–50% of their workday on documentation (Awesome Agents), and a fast-growing set of tools—including Abridge, Microsoft’s Dragon Copilot, Suki AI, DeepScribe, Nabla, and Heidi Health—can now listen to a patient visit and automatically generate a clinical note.

Abridge alone is now deployed across more than 150 hospitals, including Yale, Kaiser, Sutter, and Mayo, and holds the top “Best in KLAS 2026” ranking for ambient AI. Microsoft’s Dragon Copilot is used by more than 3,500 organizations, including Cleveland Clinic and Stanford (AIpedia; Lime AI). The American Hospital Association has documented health systems using these tools specifically to help clinicians spend less time typing and “be more present with patients” (American Hospital Association).

That is a real win. It is also exactly where, in a chapter of my forthcoming book, The Wrong Turn: How American Healthcare Lost Its Way and How We Find the Road Back, I argue that the current version of the “AI co-pilot” for care teams stops short of the job that actually needs to be done.

Documentation Isn’t the Bottleneck That Matters Most

In the book, I describe a tool called Co-Pilot—built not for the clinician’s paperwork, but for what I call a Personal Vitality Advisor’s outreach work.

Its job is not to record what already happened during an appointment. It is to help the advisor know, before the appointment, who on their patient list has quietly stopped sleeping well, whose “low energy” flag from six weeks ago was never followed up, or who is experiencing a life transition that often precedes a health decline.

The book puts it plainly: The point is to help the advisor “stay proactive without becoming intrusive”—surfacing the person who needs a check-in before that individual becomes the person who needs an emergency room visit.

Today’s dominant ambient AI category solves a real and valuable problem: clinician burnout and note-writing overload. However, it is fundamentally reactive. It activates when a visit is already happening.

The proactive layer—identifying who should be contacted and why before a visit is scheduled—is a thinner and less mature part of the market. Most current tools branded as “AI co-pilots” for care teams remain, functionally, transcription and documentation engines with clinical decision-support features layered on top (Stork.AI; Awesome Agents).

CES Is Already Asking the “Beyond the Hype” Question

This is precisely the gap that CES programming highlighted this year. The 2026 Digital Health Summit included a session titled “Agentic AI in Health Care: Beyond the Hype.” The session focused on a more advanced layer of AI in which agents help make clinical decisions, manage operations, and partner with clinicians across care settings.

Another session, “Healthcare 2035: A Vision for the Next Decade,” pushed the same question a decade into the future: What might care coordination look like once AI agents—not just scribes—begin performing meaningful parts of the outreach work?

The economics make the case for closing that gap urgently. The OECD estimates that the direct financial burden of misdiagnosis, underdiagnosis, and overdiagnosis combined represents approximately 17.5% of total healthcare expenditure in a typical OECD country (OECD).

Many of these cases represent missed opportunities when an earlier signal or a proactive nudge to a care advisor—rather than waiting passively for the next scheduled visit—might have changed both the outcome and the cost.

What “Good” Would Look Like

A truly proactive Co-Pilot, following the model I describe in the book, would need three things that most current ambient AI tools are not yet designed to provide:

• A signal layer that spans time, not just a single visit. It should detect a slow six-week decline in sleep, energy, or mood—not merely transcribe today’s 15-minute conversation.
• A privacy design that earns trust rather than assumes it. Researchers examining consumer AI health chatbots have identified inconsistent privacy policies, limited user control, and uncertainty about when HIPAA protections apply (arXiv research paper; CyberScoop). That scrutiny must be applied even more rigorously to a tool designed to monitor patterns across months rather than minutes.
• A clear boundary between “proactive” and “intrusive.” The American Hospital Association’s reporting on ambient AI emphasizes that clinicians want to be more present with patients—not to subject them to more surveillance (American Hospital Association). That is the same line a responsible Co-Pilot must carefully walk.

Ambient AI scribes have proved that the healthcare market will adopt AI inside the exam room. The next test—and the one worth watching at future CES Digital Health Summits—is whether anyone can build the version that reaches people before they ever need to book the visit.

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