Artificial Intelligence in Healthcare: Let’s Move Beyond the Label

Key Learnings

  • Artificial intelligence in healthcare is a hot topic, but to what extent is AI being used?
  • AI principle-based technologies are streamlining workflows in healthcare effectively
  • Labels of AI can bread mistrust and distract from innovation

Artificial intelligence in healthcare is a hot topic – there are emerging companies dedicated to its application, studies evaluating its ethical use, and healthcare organizations seeking to be noted as innovative leaders in the industry. 

So is true artificial intelligence (AI) currently being utilized in healthcare? Well, it depends on who you ask. As a Chief Technology Officer, definitions matter. in my opinion, AI-principles are being leveraged and are resulting in some exciting outcomes for the industry – even if it’s not precisely AI.

For example, AI-principles are being used to diagnose and reduce medical errors, to develop new medicines, and improve the patient experience.

It’s the reason why tech-impassioned people like myself get involved in healthcare – to create solutions that solve real challenges and save lives.

At the same time, however, I think it’s important for us to be transparent in our terminology – because we will only continue to learn, grow and innovate if we have true understanding of where we are now.

Understanding our current state

The healthcare industry has started to mature in its use of machine learning, where real intelligence can be gleaned about communication propensities (such as channel or content preferences), and then modify the user experience (UX) based upon known rule sets or hierarchies of actions.

Machine learning is what most are calling artificial intelligence in healthcare these days. But is it really?

By the purest definition, AI is when technology truly makes a decision, takes action, and then evaluates the effect of that action to determine a subsequent action. It’s seen with autonomous cars or even some drones, but we’re still waiting on that level of application in healthcare.

Here’s a recent example:

HealthcareIT News wrote about a hospital using “AI” to “provide COVID-19 risk assessments, virtualize waiting rooms for physician appointments and automate the PCP-to-specialist referral process.”

The streamlining of workflows using chatbots is notable – especially during a pandemic. It’s not typically a time where we have many opportunities for innovation or exploration. And this hospital did automate processes, improve their efficiency and enhance the overall patient experience.

It’s valuable, it’s exciting – but whether it’s entirely AI is up for debate.

When broken down, we see they utilized machine learning and artificial intelligence principles to implement a structured flow of actions, responses, and subsequent actions – but the technology itself isn’t making the decisions.

So does the distinction matter?

Should we be specific about what is or isn’t AI? If you ask someone within IT, they’re likely to say “yes.” Labeling something as AI just to say it’s AI often elicits a visceral response.

But, beyond this, varying claims of AI can bread distrust – and we’re already operating in an industry with a large range of skepticism and lack of trust when it comes to the possibility of artificial intelligence in healthcare.

Instead, I would argue it’s less important to use the label and more productive to clarify the specifics of a use case. For us, like in the hospital example I mentioned earlier, to break down what mechanisms or methodologies are being applied and how.

It doesn’t add to already muddied waters of what qualifies as AI, and it gives our industry the time it needs to warm to the idea of more greatly involving artificial intelligence in care delivery – that we want to pair automation with their healing hands, not replace them.

And, by being transparent about mechanisms and being reluctant to use labels, we gain greater understanding of what’s possible now so we can see what needs to be done to innovate further in the future.

The advancements we’ve made thus far are changing care delivery and saving lives, and I’m excited to witness and participate in the innovation that’s to come.

Want to learn more about technology supporting care delivery? Read our white paper,
“Making the Case for a Clinical Collaboration Platform.”
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