“Early detection of disease is a way of both curing patients and delivering better healthcare, but also scaling our physicians,” said Ajai Sehgal, Chief Data and Analytics Officer at Mayo Clinic, in an interview with Phil Sobol, Chief Commercial Officer of CereCore and host of The CereCore Podcast.
When it comes to early disease detection and improving the health of communities around the globe, data analytics are providing quantifiable value in life-saving and time-saving ways for patients and physicians. Data integrated with medical devices and tools are changing how medicine is practiced and these possibilities become a reality when data strategy and governance are in place.
Sehgal shared Mayo Clinic’s approach to data governance, stewardship and literacy programs, and physician engagement. During the conversation, Sehgal explained what some of the building blocks are for their healthcare data strategy and how patients and physicians remain at the forefront of decisions. Physicians were looking to solve problems with data and now they have a solid foundation that is resulting in tangible benefits such as delivering better care, realizing time savings and reducing cost. Leaders should be ready to articulate the positive impact data has on healthcare operations, creating value through revenue to justify investment.
Read an excerpt of the interview below with insights and advice that could help your healthcare organization use data analytics in ways that make a life-changing difference to your patients and care providers.
Stream this full episode to hear all of Sehgal’s advice as well as details about his military career, how he made the move to healthcare technology, and what startup companies have in common with community healthcare.
The conversation below has been edited slightly for clarity and brevity.
Getting your data house in order
Phil Sobol: Data and analytics have really exploded as generative AI has become the buzzword. Tell us about your approach to leading through that change and examples of work at Mayo.
Ajai Sehgal: When we started the Center for Digital Health, I was one of the first employees hired into the Center for Digital Health. We had a mission that started off with getting Mayo Clinic's data in shape— applying good governance, de-siloing the data, and making it accessible in a centralized location.
Mayo Clinic had already done a deal with Google to develop Mayo Clinic Cloud, which is a private instance of GCP, so it gave us a place to put the data.
Now, we had to put it there using the principles of very good data management and governance. We needed to build the literacy programs.
Mayo Clinic data comes from all over the institution. You cannot have a central organization to manage it. We had to build a stewardship organization so that the individual creators of the data could steward that data.
All this is done before the explosion of generative AI. There was a tremendous amount of research at Mayo Clinic on predictive AI. One of the recurring themes was, how do I get to the data? Where is the data? How do I get enough data to train my algorithms?
Our physician researchers had great ideas, but they could not get to the data.
Building that accessible data stack is more than just a data lake or a data warehouse. It is an interconnected system of resources that allow people to easily access validated data with the full context.
When generative AI hit at scale out of Google, Microsoft, and open AI, we were super well positioned to take advantage of it because we had gotten our data house in order.
And I love quoting Cris Ross, our CIO, who at one meeting was scratching his head and saying, you know, I would hate to think where we would be now if we had not gotten our data house in order.
People who do not have their data house in order and try to take advantage of generative AI are going to do so badly.
Solving healthcare problems with predictive analytics: heart disease and workforce shortage
Phil Sobol: I would go so far as to say it is impossible. As you talk to the clinicians at Mayo Clinic, they face all sorts of challenges. You mentioned physicians asking about the data. What are some issues they are trying to solve with data and AI?
Ajai Sehgal: I think we had over 160 different AI algorithms within our software as a medical device pipeline being validated for whether they need FDA clearance or not, and if they do need FDA clearance to get all the paperwork in order. The themes are widespread but distill into two main areas.
One is, how do we improve healthcare for patients with serious and complex illness? That is Mayo Clinic's focus. We focus on the illnesses that nobody else can solve.
The second area is supporting our workforce.
We all know there's a massive shortage of healthcare providers, not only in the United States, but globally. We are not going to be able to train up enough people to meet the demand of an aging population.
What we need to do is optimize the workforce we have, and we call it relieving the administrative burden on physicians.
For disease, early detection of disease is a way of both curing patients and delivering better healthcare, but also scaling our physicians. Because if you catch the disease early, you can actually treat it much cheaper and more efficiently with less physician intervention. And the ability to do that is all hidden within the data.
The new tools that we have today with generative AI are helping us to unlock some of that early detection of disease. A good example of that is detecting heart disease using predictive analytics.
Our clinicians discovered a ripple in a regular 12 lead electrocardiogram, ECG, indicative of a weak heart pump or in medical terminology, left ventricular flow restriction. This normally goes undetected in men, and we are not very good about going to the doctor. How many of us have actually had a stress echo or a cardiac CT? Not many. That is the only way to detect this prior to this algorithm.
Usually, the first symptom is you drop dead, and that does not help anybody. Well, they can now, in a routine 12-lead ECG, detect the ripple with high confidence and then refer that patient for further tests.
Mayo Clinic is now taking that algorithm and partnering with a company called Anumana to make it commercially available to Philips, GE, and others to build in electrocardiogram machines. And that is a really good example of how medicine's getting advanced through data.
I recently came back from presenting at a conference in Morocco and one of our physicians who was Nigerian-born took the same algorithm and has adapted it for a single lead ECG because it can now detect peripartum cardiomyopathy, which is super common in Nigerian women.
It is a genetic trait that causes them to have massive heart attacks while pregnant or shortly after delivery. That algorithm is now being built into a single lead ECG stethoscope by a company called Eko, and that can be distributed to NGOs. They can go out into villages and we can actually take healthcare globally. All because we have our data house in order.
Strategic approaches to creating value through data analytics
Phil Sobol: What conversations should healthcare leaders be having across the continuum, whether it be IT operations or clinical, to prepare their organizations and guide their strategies to leverage data?
Ajai Sehgal: The effective use of data analytics to improve patient care and outcomes requires a multifaceted approach with healthcare leaders engaging in critical conversations, but across several key areas.
Number one: there has to be a convergence of strategic goals. You can't have just one organization within the institution saying, hey, this is what we want to do. You have to ensure that the strategy is shared across the organization.
Data governance. I'd hate to think where we'd be today if we didn't have our data house in order. Getting data well-governed, well-stewarded, well-defined. It's not only the data you need to understand, but the workflow that created that data so that you have the full context of the data. That is critical to success with data and analytics.
Data literacy mindsets are really, really important. And I think one of the benefits of generative AI is that people now universally have a bias for good data governance.
We don't have to sell the story anymore. The story was sold for us because. Part of the fact that generative AI can hallucinate from time to time and produce bad data really illustrates the point of what bad data can do.
Phil Sobol: You might need someone to come in and prove out the value, because margins are tight right now throughout the industry. Have you ever had to prove the value of heading down this path with standardization of data governance and data analytics? Any advice?
Ajai Sehgal: Absolutely true. If you do not tie the expenses that you are incurring for management of data, storage of data, utilization of data to a business value outcome, you're not going to sell anybody on it. That value has to be measurable.
It might be short-term value. It might be long-term value. But being able to tell that value chain story is an essential skill set for any chief data analytics officer or any data analytics person.
What's even better is if you can actually deliver that value and have someone else in the organization trumpet the value that you generated for them.
That's the approach that I try to take is we want to deliver excellence for the people that we're delivering for so that they stand up and say—I can't live without this. This is why it's saving me money. It's saving me time, and I can quantify that.
One great example is in hospital optimization through throughput dashboards. We can now predict when a bed is going to become free within an ICU so that we can schedule a surgery just in time so that when that patient comes into the OR, that bed is free. ICU beds sometimes limit surgical throughput.
Well, if you can optimize that through data, you are actually generating revenue for the institution. And so, value creation is essential, and you need to be able to tie what you are doing to value creation. This is going to become even more important because operating the data with generative AI is expensive. And that those expenses need to be justified.
Apply startup principles to data and analytics
Phil Sobol: We introduced the concept of large organizations that have capital to invest, and smaller ones must be very mindful. What should smaller organizations be focused on? Maybe at the community level where you've got 50 beds in a hospital, they're probably not thinking Gen AI today because it almost seems out of reach. Where should they start?
Ajai Sehgal: Well, from my perspective, the principles that apply to startups also apply to small community hospitals. So, if you want to take advantage of data analytics, start small, fail fast, and scale gradually as you learn. That should be the very first tenet.
Prioritize data security and privacy up front, because it is less expensive to prioritize it up front. Make sure your information security perimeter is sound, especially in today's world where foreign national actors are attacking hospitals daily.
Invest in the engagement with healthcare professionals and physicians. Do not leave clinicians out of the loop. It's not every hospital that has a physician-run organization like Mayo Clinic. Many are administrative run for profit organizations. But make sure physicians are engaged from the get-go, because that's how you are going to generate value.
And then prioritize value-based care. Everybody struggles with economic constraints, particularly smaller health systems. But data and analytics can play a very, very critical role in early detection of disease in optimizing the dollars spent within the hospital.
Make sure you govern your data well so you can do that. And this landscape changes by the day, not even by the week. So, stay informed. Engage external professionals if you do not have the capability internally.
Phil Sobol: Any final words of wisdom?
Ajai Sehgal: Well, you know, healthcare evolves continuously. And today's world, it's evolving so rapidly, it spins your head. And one of the things that people aren't very good at is embracing change.
I think embracing the change is something that you need to do in today's world if you are going to stay relevant. Embrace the change, but always prioritize a patient-centric approach.
Patients come first, especially when you are making decisions, investment decisions. If you put the patient first, you will end up doing the right thing from an ethical perspective.
Invest in people. Technology is changing rapidly. You need to invest in your people to keep them up to date so that they understand how to make best use of the tools.
Stay curious and committed to lifelong learning because the technology landscape is changing by the day. A year ago, there were very few experts in generative AI, and now there's a whole plethora of experts in generative AI.
