The buzz around artificial intelligence (AI) in healthcare is deafening, and it’s easy to get swept up in visions of AI-driven diagnostics or robotic surgeries. But as healthcare leaders, our priority is finding practical, actionable steps that deliver value today while keeping patients at the heart of everything we do. That’s where low-risk AI applications shine—they offer a way to harness AI’s potential quickly and safely, giving the industry time to mature before tackling more complex uses. These tools work behind the scenes, streamlining administrative and financial tasks, and freeing clinicians to focus on human connection rather than paperwork. This isn’t about replacing people; it’s about empowering them to excel at what they do best.

So, what exactly makes an AI application “low risk” in healthcare? It’s an application that stays clear of directly influencing patient diagnoses, treatments or care plans. Instead, it tackles operational challenges, relying on data like billing records, scheduling information or publicly available datasets—information that’s less sensitive than clinical records. If something goes awry, the impact is minimal—think a billing hiccup, not a life-altering misdiagnosis. Even so, data security is non-negotiable. Whether it’s billing details or patient demographics, compliance with regulations like HIPAA and GDPR is a must to protect privacy and maintain trust.

These tools work behind the scenes, streamlining administrative and financial tasks, and freeing clinicians to focus on human connection rather than paperwork. This isn’t about replacing people; it’s about empowering them to excel at what they do best.

Three ways to drive low-risk AI excellence in the revenue cycle

Let’s explore three standout examples of low-risk AI applications in the revenue cycle—each addressing real pain points with clear benefits. First, there’s automated insurance eligibility verification and claims submission. Manual checks for insurance coverage are tedious and prone to mistakes, often delaying payments or triggering denials. AI can be used for cross-referencing patient details with insurance databases in moments to confirm coverage before a procedure and submit accurate claims afterward. The result? Fewer rejected claims and faster reimbursements—sometimes cutting days off the process. It relies on insurance and billing data, which is sensitive but not clinical. Risks like incorrect eligibility calls can emerge, but “human-in-the-loop” oversight keeps errors in check.

Next is AI-powered denial management. Denied claims drain revenue, often due to simple errors like miscoding. AI can dig into denied claims, spotting patterns—for example, a specific code that keeps getting flagged—and suggests fixes. Picture it as a tireless assistant saying, “This code’s the problem—let’s adjust it.” Hospitals using these tools have seen denial rates drop significantly, with some recovering millions in lost revenue. Biases in AI analysis are a risk, so regular audits and algorithm updates are advisable to minimize them.

Third, automated coding assistance tools take on the headache of medical coding. Coding errors lead to denied claims or compliance issues, and manual coding is slow. AI scans clinical notes and suggests billing codes—like a coder’s trusty sidekick saying, “This visit looks like a 99213.” It speeds up billing and boosts accuracy. While it taps into clinical documentation, which is highly sensitive, it’s low risk because it only provides suggestions and support. Human coders make the final call. Oversight ensures mistakes don’t slip through.

There are many more cases of low-risk examples within the revenue cycle, and these revenue cycle applications showcase AI’s ability to save time, cut costs and reduce errors without stepping into high-stakes clinical territory. Optimizing revenue cycle processes with AI could save healthcare systems millions annually. But it’s not just about money—staff get relief from repetitive tasks, and patients experience smoother interactions. Risks like data breaches or AI glitches persist, though. That’s why robust security like encryption, access controls and human review are essential, even for these “safer” uses.

Technology that drives connection

Everything I’ve mentioned is about amplifying the human touch, not replacing it. AI can handle the grunt work, letting providers focus on patients rather than clicking boxes. For healthcare organizations ready to dip a toe in, the path is clear: test a low-risk application, measure its impact and scale thoughtfully. The future of healthcare is bright—and with the right approach, AI can help light the way, responsibly and effectively.

To learn more, check out our website for more information on how we’re driving AI workflows within Altera’s solutions.