Learn About the Center for Machine Learning and Health with Ari Lightman

In an interview with Carnegie Mellon University’s Ari Lightman, get a behind-the-scenes look at the CMLH’s goals and how technology is at the forefront.

Editor’s note: This interview was originally published by the Pittsburgh Post-Gazette on May 17, 2021, and updated for this blog post on September 16, 2022.

The Center for Machine Learning and Health (CMLH) at Carnegie Mellon University seeks to address various challenges and unmet needs within the healthcare industry by providing funding for cutting-edge research. These projects typically encompass technologies and digital tools. The ultimate goal of the CMLH is to generate commercial systems and tools for improving the quality, efficiency, and cost-effectiveness of healthcare.

Ari Lightman is a Professor of Digital Media and Marketing at Carnegie Mellon and an adviser for the CMLH. In the interview below, Ari outlines some of the Center’s priorities and what the future will hold in terms of the digital health space.

What are some of the most pressing questions that you and your students and the Center want to get answered?

“How do we make health care more operationally efficient? We’re coming up with digital tools and digital processes that are designed to create greater levels of efficiency and awareness and promote greater clinical outcomes for patients.

But it’s on top of a system that’s fundamentally broken. That’s problematic.

If you don’t have an operationally efficient system, it’s hard to put additional layers of technology and digital components on top of it. So, we’re also trying to address underlying infrastructure.

For example, dynamic reconfiguration of hospitals is something they had to do over the past few years with COVID-19 to create greater levels of capacity within emergency rooms to ventilate patients, put them on intubation, and to really deal with issues they’re having associated with coronavirus. In doing so, it caused gaps in capacity in other areas.

So dynamically reconfiguring a hospital is something that needs to be done based on data and understanding different flow patterns, as well as understanding different physician rounding. Additionally, understanding how to alleviate physician needs so they could focus on this area is also critical and one of the ways they are doing that is associated with telemedicine.”

What different technologies could help make telemedicine more accessible and offer more information to physicians?

“There are different stakeholders that we must incorporate. There’s the handset manufacturers – chip side manufacturers, the carrier, the apps that reside on the phone. All of these could have some role, or some part, associated with telemedicine.

So, if I go to a doctor’s office, I see a nurse and a physician and I talk to the doctor for 30 minutes and I do a variety of screens. But I might have different types of technology that have been recording over a longer period of time that I can just provide to the physician. Perhaps I check my blood pressure over the past three weeks, whereas if I go into the doctor’s office, he does one blood pressure scan. I may have had five cups of coffee that morning and not slept that night, and my blood pressure is through the roof. Since I’ve been monitoring for three weeks, he could see these changes over time and look for patterns.

There’s this idea of creating more partnership within my health and providing a diary of things that I’ve been collecting and analyzing.

I’m a relatively healthy person, but let’s say I wasn’t and suffered from a variety of different types of illnesses. I might have a specialist who has my information in one repository. I might have another specialist who has my information in another repository.

Bringing all that together in normalization is really difficult for the health community to do. It’s easier for the patient to do. I might have better capacity to do this than my physician might have, and I can provide that data to them. This is the idea of bringing your own data.”

Once you have the data, what algorithms and machine learning tactics are set up?

“It could be something as simple as a decision tree analysis, or it could be something associated with clustering different segments to understand high-risk categories.

But a lot of times you might think about the notion of multimodal data. You might have electronic medical record data, imaged-based data, and social data. Looking at a particular individual holistically might be incorporating all these pieces of data together.”

How quickly could these things change?

“These things take a long period of time to do, but we’ve seen acceleration in digital health space, especially in light of what happened over the past few years.

There are a lot of countries that are far reaching in this space, creating multiple working groups to look at the future of telehealth. My fear is that we might take a step back in this country because of privacy and security concerns.

You can collect a lot of data from an individual during a telehealth visit. And if you, do it in an insecure manner then that opens up a security risk for hackers to steal information. When I visit a physician, theoretically no one is eavesdropping on a conversation. They’re not storing the data anywhere else unless they have a tape recorder. When you’re having an e-visit, all that information is being recorded.

Do you have your Facebook app open? Do you have other things that are going on in the background that might be collecting information?

It’s getting better, but this opens up a whole slew of risks. But that doesn’t mean that it shouldn’t move forward. We’ve got to look at the benefit and the risk. I think in this case the benefit outweighs the risk if we can address the risks in a clear and understandable manner.”