Healthcare AI: Better late than never

Hop on LinkedIn, attend an industry conference or simply check your work email and you’re almost guaranteed to see two letters pop up: AI. 

While this holds true for almost every industry, the healthcare technology space in particular has seen a surge of artificial intelligence (AI) tools in recent years, many of which promise to revolutionize the way we work and how we give and receive care. 

From automated administrative tasks and patient communication to AI-powered diagnostics and treatment recommendations, AI's potential in healthcare seems limitless. Many teams are already using AI tools as part of their Electronic medical record (EMR) system, as add-ons to their telehealth software or as standalone solutions.

So if you’re part of a healthcare organization that hasn’t yet adopted AI solutions, it's easy to feel like you’ve missed the boat. Given the rapid development and adoption of AI tools in the space, you may worry you’re behind the curve or feel intimidated about integrating AI into your workflows.

But there’s no better time to start than now. 

If you take a strategic approach to implementation, starting small and staying vigilant, healthcare-centric AI can have an immediate positive impact on the quality and efficiency of your work. And once you’ve gotten your feet wet, you may feel more comfortable taking bigger swings. 

Here are a few tips on how to approach adding AI to your toolkit, even if you feel like a latecomer.

Start small

For most of our lives, AI has seemed like a concept that belongs mostly in the far-off realm of science fiction. Few imagined we’d be able to have lengthy, coherent conversations with AI assistants complete with human-sounding voices, much less be seriously discussing the idea of AI doctors and robot surgeons.

That said, it’s important to remember that healthcare teams can implement AI in ways that are lower-risk, likely more practical and potentially more impactful in the near term.

Documentation is a prime example. 

Documentation eats up a ton of time for the average provider (half of physician time according to some studies). Not only that — it’s also dull, repetitive work that saps providers of their mental energy, forces them to divide their attention between their notes and the patient in front of them, and leads directly to burnout, with one study finding that 62% of physicians pointed to “excessive documentation requirements” as their leading cause of burnout.

With AI, providers can automate much of the documentation process. AI can take notes, generate reports, and extract key information from live conversations and medical records. AI documentation tools like these are relatively inexpensive and easy to deploy. And when they’re developed specifically for healthcare use cases and — crucially — their output is reviewed by users, the final results can be highly accurate.

Such solutions can save providers time and effort, allowing them to focus on what they feel is most important (not necessarily just squeezing in more appointments). They can also act as one more layer of oversight, an extra ear listening in. Providers can’t remember everything, after all.

But that’s just the most obvious entry-level healthcare AI implementation. 

AI can also play a key role in quality assurance (QA), feedback and more. AI tools can be trained to analyze patient interactions based on customized quality metrics and an organization’s best practices. The right AI tool can even provide quality scoring and reporting that saves teams the trouble of manually auditing transcripts or recordings. Some tools can also offer feedback and reminders in real time to help providers have a more effective and efficient visit.

These sorts of healthcare AI solutions — again, relatively easy to implement — can play a huge role in enhancing patient experience and supporting providers as they strive to do their best work. 

The common theme? AI is working over your shoulder as an assistant or co-pilot, offering providers the sort of help they need when they need it while leaving them squarely in the driver’s seat.

Experiment, evaluate, adapt

While many healthcare AI solutions offer low-lift implementations, it’s important to keep in mind that these are not "set it and forget it" solutions. AI tools require ongoing review and adjustment to ensure they're performing as expected and driving the outcomes you want. 

As discussed in the context of automated documentation, you should regularly review the output of AI tools for accuracy. But don’t stop there.

Consider the before and after of AI solutions. In other words: "That's cool and all, but is it making a difference?"

Wherever possible, set a clear, measurable goal or metric you hope AI will help your team achieve. Document what is “normal” for your team — such as time spent on a given task, new staff’s time to proficiency, or QA, staff or patient experience scores — so you can compare this against the “new normal” achieved with AI. 

You can even try a limited implementation, with some staff using AI tools and others acting as a “control” group, then compare the outcomes for each. For example, one study that took this approach found that 44% of providers who used an AI documentation tool spent less time on documentation after visits compared to 18% in the control group, and 45% reported less frustration when using the EHR. This sort of comparison makes the impact of this AI solution clear.

Along with looking for metrics you can track, you should also solicit feedback from staff both before and after you add AI to your toolset to better understand their needs, comfort level and satisfaction with the technology.

Understanding where you started, where you hope to end up and where AI has taken you so far is key to deciding if these tools are really adding value. 

If something isn't working as expected, don't hesitate to adjust or try a different solution. AI is not one-size-fits-all, and what works for one organization may not work for another. Be open to experiment and adapt.

And remember: “Good enough” has no place in healthcare.

Dream bigger

While starting small is a practical approach, it's also important to keep an open mind about the potential for more complex AI implementations in the future. 

As the technology matures, AI could play an even bigger role in areas like provider training (such as providing role play opportunities for providers with AI “patients”), patient communication and follow up (such as autonomous agents responding to basic patient queries or handling scheduling or billing), and more. We may even see AI providers working independently next to human clinicians in the not-so-distant future.

These solutions may not be feasible or appropriate for every organization at this time, but they're on the horizon. By staying informed and open to new possibilities, healthcare teams can position themselves to capitalize on AI's full potential.

Bottom line

AI in healthcare is no longer a question of if, but when and how. 

While the rapid advancements in this space may be intimidating, it's never too late to start. 

By starting small, learning, assessing, and adjusting along the way — while keeping an open mind about future possibilities — healthcare providers can harness the power of AI to improve patient care, boost efficiency, and stay ahead of the curve.

Note: This article originally appeared in Mexico Business News