Solomon Banjo: I think when people hear "clinical decision-making," they often think about clinical decision support — so the alerts in an EHR — but that really only barely scratches the surface of what you think about in, your research, Amanda, so I want to talk about the technologies that will advance clinical decision-making in the next decade. So when you think about that prompt, what are you thinking about Kevin?

Kevin Larsen: So there's a whole professional society called the Society of Medical Decision-Making and I got to speak at one of their meetings, it was really cool. Part of what I think about are the known issues with how we make decisions as people like proximity bias, "If something just happened to me, I think it's going to happen again," and so how do we have technologies that help us see where we have those cognitive biases and those blind spots? I think that's going to be a big opportunity for technology.

Banjo: Awesome. Now, Amanda, when you think about the technologies changing the next decade of clinical decision-making, what have you got?

Amanda Okaka: I tend to think about AI, which I think has been on the forefront of everyone's mind for quite a while, but especially the last few months this conversation has really come to the forefront because I think we're getting to a point where folks from all across the industry are really wanting to separate the hype from the reality.

There's a real optimism for this technology to revolutionize clinical decision-making and clinical decision support, but we're in a time where we really need to figure out what's real, what's actually possible, and what is just hype.

Larsen: I couldn't agree more. I also think that we jump ahead too fast to the big solution, the big home run, and I think really we're going to increment our way into this rather than going right for what's the big home run.

Banjo: And I love that call to attention to detail because it is so easy, especially now — AI, ChatGPT, everyone's thinking about what it could mean, and I do want to focus on what it could mean, but I do want to get more specific because these technologies have the potential to have significant implications, whether you're a patient, clinician, or a provider.

I'm thinking about the research we've done, Amanda, and how one of our predictions is that for the first time in all of human history, clinical decision-making will not be memory-based, it'll be technology-assisted. So when you think about that, what does that mean to you, and why is that good for the healthcare industry?

Okaka: That phrasing, the shift from memory-based to technology-assisted is really the most succinct and apt way to describe what we're witnessing. I don't remember the last time that I was in an exam room or in a patient-provider interaction and there wasn't a computer in the room, I didn't check in on an iPad. All of these sorts of things are really becoming standard in our day-to-day practice and I think that that's what it means is having to be aware and being in tune with these technologies both as patients and providers.

I think that there has to be learning on both sides for that and that's why I think it's good for the healthcare industry because technology really has the ability to empower both patients and providers, and as we're seeing more sources of data and evidence and a better infrastructure for integrating that data and evidence to actually drive decision-making, I think that it will really benefit the healthcare industry to help us be able to be more proactive in our approach to treatment and diagnosis as well as how we stratify patients and their risk levels.

Larsen: I think it's going to be safer. I also think that we can look to other parts of our experience in the modern world where this has happened already. So when people say, "Oh, I'm so scared of ChatGPT. Kids aren't going to learn anything." Hey, kids right now use calculators in math class all the time. Do I think that's a bad thing that they're not memorizing long division? No, I hated long division and I always have a calculator wherever I go. Why should I ever have to do long division again?

I think there are many parts of clinical medicine that are going to be the same way where we're going to have this indispensable tool that's already always in our pocket. We're going to use it at a time when we need it, but it's again, not necessarily going to be the big thing. It's going to be the little calculator in my pocket that actually I don't have to rely on my memory to do this thing, I can use this tool.

Banjo: You brought up earlier, Kevin, when you're talking about your talk, the fact that thinking about the different ways in which we have bias in decision-making. I'm curious how you maybe tie that or don't tie that to when we think about things like artificial intelligence being used in clinical decision-making. Do you think there's reason to be optimistic there?

Larsen: Yes, there's both reason to be optimistic and reason to be cautious because artificial intelligence can be a black box to a lot of us. We'd say, "Oh, the computer's going to do this and the computer's going to figure it out. The computer will search the web and find the answer." Well, the web is made by us. We're imperfect. We're imperfect as society, we're imperfect as individuals. It's going to learn from everything that's already out there and what's out there is not perfect, so we have to always be looking inside that black box, figuring out, "Okay, what did it learn from? Was that reasonable? How do I test that this is actually fair and unbiased?"

But other times it's going to be that calculator for me that doesn't see race, doesn't see anything else, it's just a calculator and it says, "Here are the numbers that I see. Here are the important inputs. This is what the answer is going to be," so I think it's going to be a double-edged sword. It's both an opportunity and a risk.

Banjo: I love that. I want to switch a little bit because we talking about the technologies, but ultimately if it's being used in healthcare, it's going to be impacting patients. Now, something the headlines often overlook because they're very optimistic, is the fact that there's a sizeable, and so far, the data I see the majority of Americans, who are skeptical or cautious about AI being used in their actual care.

How do you see this technology impacting patients and maybe even their relationships with their healthcare professionals that they're interacting with?

Larsen: So AI is a big, broad thing that encompasses lots of different tools and lots of different techniques. I think it's already being used in some ways. For example, if any of us have used things like translator apps, we know that that translation can be indispensable. Right now, I can take my cell phone out and go to a foreign country and I can actually interact through the translation in my cell phone in a way I never could do 10 years ago.

That kind of AI is going to enter clinical care all over the place to help us in ways that we've wanted that help. It may or may not be better than human beings who are translators. Sometimes people have their children do the interpretation for them, and we know that the kids often get it wrong, so is a phone interpreter better than a child? Probably. However, as a patient, do I want a computer telling me what disease I have? Not yet. I don't think that I trust that computer yet to have that kind of decision-making expertise.

Okaka: I think that's a really good point about having to instill trust in these technologies for patients, and what that looks like for me, I think that there's a few different ways that we can think about how this technology will impact patients. I think there are some more impacts that are a little bit more obvious, and then there are some that are more operational that might not impact patients every time they are interacting with the healthcare provider.

But I think at the heart of it, the future state of clinical decision support needs to create technology that empowers both providers to provide more proactive and higher quality care and enable intervention, but also empower patients to be able to communicate with their healthcare providers and have better access to the healthcare system and also be able to learn about their conditions and potentially self-manage them.

Banjo: I wanted to pull on a thread you teased, Amanda, which is the empowered patient. So Kevin, you've worn a lot of hats over your career, but one constant is as physician. So when you think about that empowered patient Amanda's talking about with the ability to ChatGPT my questions, forget Dr. Google or Dr. WebMD, what comes to mind for you wearing your clinician hat?

Larsen: Well, to the same way that you described decision-making is no longer going to be memory-based, that's really what doctors have held for a long time is that memory, and so this levels the playing field in many ways.

Now we're interpreters, we're consultants as doctors as opposed to being the holders of all that knowledge and information, and hopefully these systems are eventually able to see patterns that even we as doctors, trained to see patterns, that we aren't seeing, so additional patterns will emerge.

I think people that experience what we call a diagnostic odyssey, people that have to go to lots of doctors and get lots of tests to finally five years later figure out what was wrong, I hope those people are going to have a much faster time and a much shorter time because the computer can see so many more options and so many more patterns.

This levels that playing field and takes away some of that information divide between doctors and patients so patients actually have information and hopefully more autonomy in making their decisions. But that doesn't take away my responsibility to give you good advice and good consultation.

Banjo: When I think about your math example of, "Okay, no longer hand long division, now we're moving to calculators," or those of us who moved from MapQuest to Google Maps. My sense of direction personally has really deteriorated.

I feel like when we are hearing from providers, there's a lot of fear that comes up, but one of them is what do we lose by being technology-assisted as opposed to coming from a place of being memory-based, of that repetition of the art and the science? What do you say to your fellow clinicians who may have a fear like that?

Larsen: We've always been technology-assisted. We used to use slide rulers. I don't know how to use a slide rule, but my physics teacher in high school could do it way faster than I could use a calculator. Did I miss out on a really important skill because I can't use a slide ruler? I don't think so.

My grandfather was terrific at repairing old-timey tractors. That was a great skill that he had in his memory. Do I have that skill? No. Do I need the skill to fix old-timey tractors? I do not need that skill. So some of this is to give up some of these skills that have been important to us, but realize that they're no longer are they the valuable skill in the world. We have to be in a constant learning mode, lifelong learners, and build new skills on top of the new technologies we have.

How clinicians use evidence to guide clinical decision-making is changing due to many different but related factors. Evolving health care ecosystem dynamics, institutional forces, and clinician preferences will demand changes in both individual clinician competencies and the landscape of clinical practice. Access our report to learn about four predictions for the next decade of clinical decision-making and the implications for medical device and pharmaceutical companies.