One of the main issues with artificial intelligence (AI) is that it requires a lot of data. And that requires data scientists who know how to work with all of that data. The problem is data scientists are in very high demand. There’s an incredible amount of competition for their skills (and not just within health care).
For health care CIOs who want to use AI to solve real business problems, the lack of internal talent can pose a big challenge. In this article, Marc Paradis and Scott Gamester, two data-science experts from Optum, offer insights into fixing the data science talent shortage.
Why the right AI skill set is so rare
AI is not a single algorithm or technology, and it’s more than just math or computer programming. AI includes many different kinds of machine learning, natural language processing, image recognition, etc. All of these use very large data sets. So you need people who can work with the tools and the data, but there’s more to it than technical skill.
When you talk about data science, some people will tell you the lack of skill is in math and computer programming. But that’s not always true. There’s no shortage of quantitative talent. A lot of people can do math and programming. The skills that are lacking are in the methodology, the thought process, and the business context behind what technology you use.
For example, if you’re framing a house, you can use a nail gun and that’s great for that job. But you probably don’t want a nail gun if you’re building a doll house for your daughter. You want a different kind of nail and a different kind of hammer.
In the same way, with AI you need to understand how to use data science to solve real business problems and deliver real value to your members and patients, so it’s not just a case of “We have AI, too!”
There’s more to data science and AI than technical skill.
Two heads are better than one
Good data scientists also need a measure of humility. Quite frankly, this is something that’s often lacking. We emphasize very heavily the importance of humility. Having one really smart person with a PhD is not going to work. No one knows everything, and very complex algorithms don’t always follow the rules or assumptions.
We teach people to be open, to ask for help and to work in teams. You want to start with an absolute minimum of two data scientists. In the technology world it’s called “pair programming.” We actually like the pod concept of four or five folks working together. And we use the two-pizza rule: never have a team larger than can be fed with two pizzas.
Very complex algorithms don’t always follow the rules.
Choose “all of the above”
There are different approaches to getting the right AI talent and skill set you need in your organization. You can hire some PhDs. You can train and retain your own talent. Or you can partner with an outside group.
The first option requires an economics conversation. For example, for a data science PhD coming out of Stanford, you’re talking potentially about salaries in the million-dollar range. That’s not going to work for most companies. We looked at this challenge and asked, “OK, how do we also grow our own internally? How do we build this organically, using what’s out there?”
You don’t necessarily need to hire the hottest algorithm person out there. But you also won’t be able to 100 percent build your own team either. Start with a core of servant leaders and build your teams around them. And have an open environment where it’s safe to make mistakes and learn from each other.
Everyone needs to be trained
It’s going to take some time to train people from within. One of the interesting things we’ve discovered is that you can find very smart, talented, capable people outside your organization. But when you bring them in, they’ll still have to learn your tech stack, processes, policies, maybe even industry. That can be a long learning curve, too.
When we hire, it’s someone who brings a very specific skill set and capability. And ideally someone in health care who has some history with us. In our experience, we have gotten more value more quickly out of the people we have trained internally than we have out of large-scale hiring.
What’s worked well for us is to have teams with mixed levels of experience, from new graduates to the more experienced. We give them a problem, give them the freedom to solve the problem within guidelines, and give them room to fail and learn. And we have the theory and in-classroom training alongside that practical training.
If I hadn’t gone through your coursework, it would have been weeks of back and forth.
Train the business, too
You don’t have to focus exclusively on data scientists. We have found real, measurable value by finding people from all over the organization who have an inclination for, or interest in, data science, and training them, too.
For example, we had a woman from the business side of Optum go through our introductory coursework that lays out the basics of data science and our methodology. A couple months later, she was working with some data scientists who asked if she had a particular data asset. She said, “Do you mean this type of asset for this purpose?” They said, “Yep, that’s exactly what we’re looking for.” So she packaged up the file, did a little bit of cleanup work on it, sent it over to them, and the project moved forward.
She told us, “If I hadn’t gone through your coursework, it would have been weeks of back and forth and miscommunications. And probably at the end, I would have sent them the wrong thing because I wouldn’t have understood the larger context.”
Collaborate to survive
No matter what you do, you’re going to run into unknowns when you release AI models into the wild. Test data is static and safe. But live data morphs and changes every day, and you’re going to need to retrain your AI models.
We teach people to open up, share their knowledge, and pay it forward to get paid back — to contribute their learning, expertise and time to help others. If you do that, you’ll find that when you get to a problem that you don’t know much about, you will have a community of people around you. They will reach out to provide the information and support you need.
-Marc d. Paradis, SM
Originally published at: www.optum.com/resources/cio/artificial-intelligence/executive-viewpoint-lessons-learned-ai-health-care.html – this link is no longer active.
This article can also be found on Marc d. Paradis’ and SIYOM Consulting’s LinkedIn posts
Disclaimer: The opinions expressed herein are my own personal opinions and do not represent any of my previous employers’ views in any way.