In an interview with Velina Nacheva of TRENDS, Andrew Moose, Head of Innovation & Health Systems Transformation at the World Economic Forum, discusses some key takeaways from Davos on AI-driven health intelligence and what the Gulf has to offer the world on that front.
What are some of the key takeaways of the week?
I think that the theme was aptly put in place, which is the spirit of dialog. On the one hand, there continues to be a great deal of disagreement about the way economies and the geopolitical order are unfolding. We find ourselves in a new era along those lines. I think Canadian Prime Minister Mark Carney put it well in terms of where we find ourselves in the world today. But I also think that there remain many people who are very committed to working together, and many of those were here in Davos this week. We had a very strong presence from the US delegation. Despite some of the commentaries to the contrary, it was very clear to me that they are interested in working with businesses from all parts of the world.
From a health standpoint, we can continue to see AI’s dominant evolution as companies, much more quickly than they anticipated, are advancing with these AI models and learning about new capacity and capabilities from those tools. But at the same time, you’re starting to run into the real challenge of actually scaling those solutions. There was a lot of discussion about how you prepare your organization, how you free up capital to be able to invest with a real sense of urgency to adapt those models. Those who are lucky enough to have a clear picture of where they want to head are moving much faster.
Was healthcare high on the agenda when it comes to discussions within sessions and action points? What’s next?
I think it was higher than it’s been in the past. I would say there’s still room to go. We don’t feel that our partners don’t feel that health is where it needs to be on the global agenda. In part, we’ve heard that there’s still fatigue from COVID, that there was so much focus on health during that period. And there are just competing priorities in terms of people needing to shift spending to increase defense, to increase investment dollars in technology and energy.
A lot of what the goal of our health partners was this week was to talk about how health is not just about an industry; it’s about life-saving interventions and being able to maintain a steady supply of much-needed medical interventions, but it’s also a matter of national security. It’s also a matter of economic stability. You really can’t have economic growth and prosperity and productivity without a healthy workforce, without a healthy population. That was a lot of the messaging that many of our CEOs and board chairs were promoting in the discussions with the public sector.
The white paper that was shared on the topic highlights the jump from digital tools to health intelligence. So, what does it take to make that system smarter globally? What experiences from the Gulf can be implemented elsewhere?
We were asking the question, where are health systems in the world making progress and advancing? And who should we spotlight? We had a lot of requests to say, we really need exemplars. We need to see places in the world where digital health is advancing and making progress. A lot of the efforts have been made in Abu Dhabi, which was one of the areas that our partners pointed us to. It’s not only helpful to understand architecturally and from a vision standpoint what they’re doing, but by understanding those examples more, we can then go back to our geography and show that and be able to explain how the investment is positioned, how it is sustained, and how they’ve been successful as an inspiration to drive action in other markets.
Making those health systems smarter really requires a strong strategic shift, both in mindset and also with investment, where you’re not just looking at how we leverage AI, but you’re really saying, how do we build intelligence through a significant data pool, training those models, having the AI, and having all that come together to really drive decision-making. The whole point of the system is, what decisions am I making based on this data or AI? What can it automate? And how do I need to reimagine my system to do that?
How did Abu Dhabi approach this transformation?
In Abu Dhabi, there was a transformative vision. There was clarity about the health challenges in the region and what they were really hoping to focus on by taking this much broader data set. So not just looking at health metrics, but looking at people’s genetic makeup to be able to personalize drugs, looking at their transport patterns, how far away they are from exercise facilities, and really taking those broader social determinants of health along with other health data, which has allowed a much more significant number of decisions that can be made off of that data when you have it all in one place.
And how can such intelligence-driven digital solutions help support chronic issues?
Obesity and metabolic health are significant challenges for many in the region, and really for many in all parts of the world. Having those intelligence-driven solutions really allows you to change how you’re addressing those chronic diseases. First of all, you’re able to detect them a lot earlier. And so it’s much easier to address those challenges as they appear, as opposed to waiting until someone is suffering extensively. That’s a lot of the focus: to shift not just from how we address obesity, but how you address metabolic health, looking at insulin sensitivity, inflammation, cardiovascular risk markers, and being able to identify those sooner, get people the interventions that they need, and then monitor those outcomes, ultimately trying to help them change their behavior, which is the hardest part in any health system.
Most of the time, if you do have obesity or some other chronic disease, there’s a comorbidity with mental health. And so you’re not always going to be able to just focus on chronic disease and metabolic health. It means you have to solve for mental health as well. These new toolsets do bring new ways to bring together disparate information, disparate pieces of data, in ways that you can really start to build policy and individual behavior and shape that around it.
How can you use that knowledge within the workforce and support employees effectively? What will be your message to HR leads?
We started an initiative called Healthy Workforces about two years ago, and then we also established a chief health officers’ community of about 50 chief health and chief safety officers in organizations. We’re focusing on both physical and mental health. A lot of what that tells us is that this shouldn’t just be the chief human resource officer’s responsibility. Ultimately, given the increase in pressures on health and the impact on productivity, this is really becoming a matter of the future of management and leadership.
We really made the case last year in our Thriving Workplaces report that this isn’t just an HR officer’s responsibility, but ultimately it’s the CEO, the C-suite, and the board, because we’re all influenced by our environments. It’s the CEO and the company that set the culture, but also the services that you have available, the food in the cafeteria, and the physical setup of the office. And we know that it’s easier to influence people’s behavior as a group than it is to influence a single individual.
How should leaders think about culture and productivity?
We really wanted our companies and CEOs to reflect on the environment they are putting in place in their organization. Are they adding to people’s stress? Are they taking it away? And then how do you ultimately enable a team at the manager level? In our research with McKinsey Health Institute, about 60 to 80 percent of the productivity gains that you can derive from health and well-being interventions come at the team level, which means the team has to be comfortable talking about these things and being able to put strategies in place with four or five people working together to offload stress from one individual.
That requires new managerial training, a different perspective, and it requires leaders who are setting the tone that this is okay to work through these issues. This is part of what being a good corporate team looks like, and to normalize those discussions, because all too often today, there’s still stigma.
What does the data say about performance and well-being?
We work with a researcher named John Deneuve out of Oxford on a lot of this with our community. And their data shows that companies that do well on well-being indices outperform their competitors by about two to one. We live in a world where there are more pressures than in the past, requiring greater resilience. And those companies that can do it well are outperforming their competitors by about two to one. So there’s a very clear productivity and business case, irrespective of the desire to improve your employees’ long-term health.
Has this focus intensified post-COVID?
I think that period opened a much more broad-based discussion about what it means to be productive at work. When do I physically need to be there versus working from home? You still see those negotiations playing out today, not just from one company or one industry to the next, but I think also there’s a lot of difference across generations. Older generations that may have had more loyalty from their employer may be more willing to sacrifice their time or make commitments to an organization, whereas we see a lot of younger employees who may not quite feel the same loyalty. So they’re much more protective. I think we’re in a new era when it comes to that. And I think that you’ll continue to see physical workspaces evolve to really meet human needs more than maybe they do today.
How are workplaces adapting physically?
We released this report called The Human Advantage: Stronger Brains in the Age of AI. We’re not only working with companies—we have companies that are focused on mental health treatments or hospital systems or those working on Alzheimer’s or cognitive decline—but we also have real estate companies that are part of that consortium: JLL, Steelcase, and a number of others. They’re physically trying to adapt corporate spaces to be conducive to strong brain health and building brain skills. They’ve even implemented what they call brain gyms, where there are digital screens, and you can go and take a break from your work. And there are different activities that you can do to, based on proven research, improve and strengthen your brain.
Abu Dhabi has become a leader in health intelligence. Which innovations could close the global health gap?
I’m not sure it’s just about one tool or intervention. It’s really about the ability to reimagine the health system, which requires that you really deeply understand the capabilities of these systems and what they’re able to do, and that you have a strong understanding of what the future needs of our health system are, and to be able to match capability with the needs that are emerging in the future.
A lot of why we selected Abu Dhabi as a pathfinder is that the Minister of Health there comes from a technology background. And so the vision, the design, and the architecture were quite compelling. In many cases, we don’t see that same attention being applied. There’s a race to implement the tools and try to achieve optimizations, but all too often, that’s creating efficiencies and cost savings in an existing system. It’s not really reimagining to meet those needs.
What made Abu Dhabi’s approach different?
What we saw in Abu Dhabi is that they saw the shifts in their population, where their health issues were, and they understood that it wasn’t just going to be in health systems that they needed data. They needed to be in the communities. They needed to be able to create a more prevention-driven, lifestyle-oriented health system that was moving from sick care to well care.
When you treat health intelligence as national infrastructure, you make a multi-year investment, you make that deep commitment, you have clear standards, you’re focused on continual upgrades, putting in the right levels of energy support and connectivity that moves you away from these fragmented pilots where you’re trying to prove it out and show a real commitment from the beginning.
How does that translate into policy and outcomes?
The level of integration and interoperability that they’ve achieved is really trying to address the entire population. We’re seeing now that it’s able to inform not just things that help physicians and their patients, but also public health policies. They have some 30 or 35 programs that use that data and make decisions about how to allocate money. And so they can identify much more easily where the vulnerable parts of the population are, what assistance they need, and how they should optimally allocate their funding.
What is the broader significance of spotlighting such models?
There aren’t many places in the world that enjoy that privilege, but by spotlighting it and bringing attention and being able to share and allow our network to speak to them and understand how they were able to achieve that, how they were able to position within their government to make that long-term commitment—our hope is that not only does it spotlight what’s possible, but it hopefully provides people with some of the details they need to actually paint that picture for their leaders, and then also understand the strategies involved in making it work over time.
We’ve signed a second phase with them to really understand what it means when you have better intelligence. How does that turn into better policy? How does that turn into better interventions and better health?
Bringing the discussion back to Davos and AI—how do you see the shift from digital health tools to health intelligence evolving by 2030?
If you’d asked me that question a year ago, my answer would be very different. When we spoke to a number of partners this year, they felt like some of the things they were working on would take them two, three or four years. And now, less than a year later, they find themselves much further ahead than they anticipated.
There’s still a lot of conversation this year about tools, models, copilots, diagnostics, and automation. But what we really expect is a shift from what AI can do to what health systems need to do. And so it’s going to be that blending of tools with processes.
How quickly is adoption happening in practice?
I was talking to one CEO today who leads a large health data business in China, and she was talking about how they already have today for physicians, not just one agent working for them, but three or four, depending on the different specialties. And now there’s multi-agent—those agents are speaking to each other, even when the physician is not working. And the adoption has been faster than they thought from the physicians, because the physicians realize this can make their lives better. They have more time to treat patients.
What challenges remain?
It’s been very uneven. Health systems will need to really evolve and incorporate that, and it’ll depend on the pressure that’s on them, either from a cost standpoint, workforce strains, where there are gaps in the labor market, and also the chronic disease levels. When that pressure increases, the motivation to reimagine and put in those tools is much higher.
We’ve already seen a lot of early impact. People are frustrated that they’ve done a lot of pilots. They’ve proved it out. But the infrastructure and capabilities to architect a fully interoperable system with all the data that you need just aren’t there yet in many parts of the world.
How does that affect the future of health systems?
Over time, there’ll need to be that reframing and really better architecture. You’ll need to have full access. The data will need to be much more heavily exchanged across different entities, and you’ll ultimately need to start to create a shift toward prevention. We don’t see enough allocation there either.
It’ll focus initially on productivity, and then it’ll move to some aspects of prevention, balancing between those two. But we are concerned that there’s a widening gap between those that are able to learn quickly, that are really AI-enabled, that have the infrastructure and energy capacities to be able to build out these large models. In other cases, the potential to fall behind is very real. The more fragmented you are, the more difficult it is to implement effective solutions. You can deploy the tools, but you just don’t see the same uplift and impact that you could see otherwise.
