As Healthcare Software Evolves, Human Expertise Matters More Than Ever

Over the past several months, I’ve had the chance to share my perspective on artificial intelligence in healthcare with clients across the country — and the response has surprised me. They’ve shared over and over how our conversations reframed how they were thinking about the technology, and more than a few have encouraged me to share it more broadly. So that’s what I’m doing here. As someone who has spent years building technology solutions for this industry, I want to offer the same perspective on AI that I’ve been sharing in those conversations. 

I also shared my perspective in this on-demand webinar.

The Opportunity for Healthcare Providers

The healthcare software market is in the middle of a fundamental transformation. Modern, unified platforms are in demand, outcomes are expected, AI is being embedded into everything, and record levels of venture capital are pouring into health companies. The opportunity for our industry is enormous, but so is the risk of getting it wrong — especially in a sector as regulated and high-stakes as healthcare.

I see these trends playing out every day in conversations with our clients and across the market. My hope is that our industry embraces AI, but thoughtfully and prescriptively. To me, that means keeping humans and their expertise involved at every step and using AI to do things more accurately, faster, or better.

I firmly believe that the healthcare professionals who incorporate AI into their workflows — with the right guardrails and safeguards in place — are the ones who will make the biggest positive impact to advance their organizations’ mission. They’ll deliver more value, produce stronger outcomes, and model success for other healthcare organizations to follow. AI isn’t something to shy away from. It’s something to lean into, thoughtfully.

Humans Must be Kept in the Loop

AI should be a secondary tool that helps us accomplish outcomes within certain workflows — not a replacement for humans and their expertise. AI can validate information, help us get started on a project, or speed up a process like provider document collection. Those are great use cases as long as a human is still reviewing the output. We have to stay aware that AI isn’t perfect, and healthcare is not a sector where agents can run unchecked.

Sure, you could hire a small team, spend six months vibe-coding with AI, and spin up a learning management system with content. But what you won’t get is the subject matter review that healthcare demands. And you’re likely years away from that content being accredited for continuing education across all levels. So, while you could do this, most healthcare teams would keep their existing healthcare learning management system with approvals in place, and trust AI for other parts of the process, such as identifying required courses by role or assigning courses in bulk.

My fundamental belief is that healthcare will not begin eliminating jobs because of AI. I think we’ll see transformation, and that transformation can mean getting more from the same resources, or in some cases solving harder problems faster. At MedTrainer we’ve been very intentional about how we approach this. Nothing we do removes the human from the equation. There are ways to do things faster, more accurately, and better — but human expertise remains critical.

AI-Forward, Not AI-Native and Why That Matters

Before we ever introduced AI into the MedTrainer platform, we had already been delivering meaningful success and outcomes for our clients for more than a decade. That foundation matters. We’ve only chosen to deploy AI where it’s genuinely effective and safe.

There’s an important distinction I want to draw here: being an AI-forward company versus being an AI-native company. At MedTrainer, we ask ourselves, “Where can AI truly provide better outcomes?” rather than giving AI access to all workflows and data. In compliance and in healthcare, that second approach is a risky place to be. Right now, I’d put the knowledge level of AI somewhere around that of a high school senior. The models have impressive knowledge bases and real capability, but they still need structure and oversight. 

I think that’s why we’re seeing nationally-recognized accrediting bodies holding back, and rightfully so. They’re more hesitant to license or accredit learning, compliance, or credentialing companies right now because of the flood of fast-to-market, AI-native businesses built on speed rather than expertise and trustworthiness. For context, brand new AI-native companies are expected to drive growth of the enterprise healthcare software market by $100+ billion in the next 10 years.

Healthcare organizations should follow the lead of accrediting bodies and look carefully at how your data is being used and whether the vendor will be a good partner. Is the vendor trusted and what is their reputation in the market? Is your data safe? Is the privacy of your employees and patients protected? 

As an AI-forward company, we’ve built what I call a “kill switch” on the back end. It’s not for emergencies — it’s for clients who say, “AI sounds great, but we’re not ready for that in our organization.” AI in MedTrainer can be disabled easily, giving organizations control over their own pace of adoption.

Looking Beyond AI in Healthcare and What Comes Next

Healthcare is slower to adopt AI than other sectors, and I think that caution is warranted. But here’s what’s encouraging: once organizations do adopt, their internal adoption rates are extremely high. Once our clients learn how to use the tools, they embrace them.

In addition to AI, technology consolidation is driving considerable change in the market and it’s top of mind for healthcare executives, especially those who are financially minded. Why run five, six, seven platforms in parallel when one can get the job done? The average hospital care setting runs over 250 software platforms. Reducing that number is a major initiative, which is why platforms like MedTrainer, that replace multiple tools, are rapidly growing.

At MedTrainer, we’ve been focused on building all three of our core product pillars so we can control the connective tissue between them. The level of interoperability required between healthcare services is actually quite robust. The way someone files an incident report might trigger a policy review or a training requirement, and all of those connections should work seamlessly within a single plaform.

Looking ahead, I believe the healthcare organizations that thrive are the ones that resist the urge to chase every shiny new AI tool and instead invest in partners and platforms with the expertise, governance, and staying power to support them for the long haul. The next few years will separate the vendors built on speed-to-market from those built on trust, accreditation, and deep domain knowledge. The organizations that get this right won’t just keep up with the transformation, they’ll lead it with AI as a powerful tool in their arsenal and human expertise firmly at the center.