Can We Automate Our Way Out of Healthcare Cuts?

Lately, I’ve been reading a lot in the news about staffing and service cuts at hospitals and clinics. What struck me wasn’t just the headlines—it’s that this feels increasingly personal. My wife works as a practitioner in a rural, federally funded health clinic. I’ve watched firsthand how under-resourcing affects care, staff morale, and ultimately, patient outcomes. At the same time, I’m seeing stories about states refusing to expand Medicaid, even as their hospitals struggle to stay open. And across the industry, there's rising chatter that maybe we can just automate our way through this.

As someone who works in digital health, AI platforms, and go-to-market strategy, I understand the appeal. But I also know it’s not that simple.

What’s Causing These Cuts?
Financial pressure in healthcare isn’t new, but it’s deepening. Cuts to Medicare and Medicaid funding are placing real strain on provider organizations. In 2025 alone, Medicare saw a 2.83% payment cut—the fifth year in a row this has happened—while a proposed $880 billion reduction in Medicaid could result in more than 13 million Americans losing coverage by 2034 (The Guardian, AMA).

The challenge may soon grow deeper: the latest federal budget proposal includes further cuts to Medicaid and related safety-net programs, which would likely accelerate service reductions and make sustainable solutions even harder to achieve.

In my view, this isn’t just a budget issue. It’s systemic. We’ve created a model that underfunds essential services and expects innovation to fill the gap without investing in the infrastructure that makes it sustainable. Layer on rising costs, workforce shortages, and aging populations, and it’s no surprise that many organizations—especially rural or safety-net clinics—are being forced to scale back staff or shut down entire categories of service.

Can AI Help? Yes—but With Limits
There’s no denying that AI, machine learning, and automation can streamline tasks. In Medicaid programs, for example, AI has been used to assist with eligibility determination and to predict patient risks and outcomes (arXiv). Care planning, coordination, and documentation are all ripe for tools that reduce manual overhead.

But this can’t be a swap-out strategy. CMS has already issued guidance that AI should support, not replace, human decision-making in coverage and clinical determinations (Norton Rose Fulbright). We’ve also seen lawsuits and compliance reviews over the misuse of algorithms to deny care (Maynard Nexsen).
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So yes, AI can help—but only if implemented ethically, with transparency, and as a tool to extend, not replace, human care.

So What Can We Actually Do?
Here’s what I think is realistic—ground-level actions that make a difference:

🏥 At the Policy and Grassroots Level:

• Advocate for Medicaid expansion in non-participating states. The data shows expansion reduces uncompensated care and improves access.
• Push for fair and predictable Medicare/Medicaid reimbursement structures, not year-to-year instability.
• Support funding models that prioritize community health clinics, telehealth access, and preventative care (CT Insider).

💡 As Practitioners and Technologists:

• Use AI tools to free up staff—not replace them. Automate repetitive documentation, appointment follow-ups, and claims triage.
• Build decision support tools into care workflows that help identify risk earlier and coordinate better across care teams.
• Train staff on ethical AI use and ensure transparency in how tools inform decisions, especially in eligibility and benefits management.
• Share success stories and failure points openly. The more we learn from each other, the better the tools—and outcomes—become.

This isn’t about “saving jobs for the sake of jobs.” It’s about making sure patients don’t suffer because a system tried to cut corners where it couldn’t afford to.

So... Is This a Real Problem?
In short: yes. If we don’t address it, we’re not just risking operational inefficiency—we’re risking community health. Automation alone won’t fix it. We need better policy, better tools, and more collaboration between clinicians, technologists, and administrators who are willing to tackle this head-on.

We’re not making a mountain out of a molehill. The mountain has a name now.

Sources

• US health groups vow to fight GOP cuts to Medicaid and Obamacare – The Guardian (link)

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• Medicare pay cuts: How they endanger physician practices – American Medical Association (link)

• AI-driven innovation in Medicaid: enhancing access, cost efficiency, and population health management – arXiv(link)

• CMS clarifies Medicare Advantage organizations' use of AI in coverage decisions – Norton Rose Fulbright (link)

• The legal landscape for AI-enabled decisions for health care claims and coverage – Maynard Nexsen (link)

• CT must act now to save its health care workers – CT Insider (link)