How AI can enhance the humanity in health care on Government’s Future Frontiers

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Today’s guests:

• Sara Siegel, global Health Care sector leader and partner in the Health care Strategy practice at Deloitte UK
• Maurice Fransen, partner at Deloitte Netherlands specializing in AI transformation
• Dr. Avi Tsur, director of the Women's Health Innovation Center at Sheba Medical Center in Tel Aviv
• Renee Yao, global health care AI startups business development lead at NVIDIA

The health care industry is facing a workforce crisis: The World Health Organization estimates that there will be a shortfall of 10 million health care workers by 2030.¹ That can lead to increased wait times, decreased health outcomes, increased worker burnout, and other negative effects.²

AI may be a game-changer for the health care industry, but apprehension about the technology may be slowing down adoption.

In this episode of Government’s Future Frontiers, we discuss how AI may change the way health care is delivered. While the technology can increase efficiency and cut costs, our guests note that focusing on these aspects may miss the point. Instead, they point to the potential of AI to help make health care more human by freeing up clinicians from administrative burdens and speeding communication with patients.

Our guests today are Sara Siegel, global health care sector leader and partner at Deloitte UK, and Maurice Fransen, partner at Deloitte Netherlands. They have been working with clinicians and health care systems to tap into the power of AI to transform work in the sector. We also speak with Dr. Avi Tsur, director of the Women's Health Innovation Center at Sheba Medical Center in Tel Aviv, about the ways AI is transforming maternity care, and Renee Yao, global health care AI startups business development lead at NVIDIA, a major supplier of AI hardware and software.

Siegel warns that technology alone cannot drive this transformation. “Technology doesn't work in health care unless there’s clinician adoption and clinician leadership. ”

Fostering that adoption is a one challenge to the AI transformation of health care. How well society confronts health care shortages and other challenges may depend on whether the industry can make the case that this technology helps retain the humanity of health care.

Tanya Ott: Have you tried to make an appointment with a health care provider lately? Whether it was for a general checkup or a more specialized issue, chances are, you had to wait for an appointment. In some cases, people are waiting more than a month to see their general physicians. To see a specialist, they’re waiting even longer.

But patients aren’t the only people feeling frustration. Clinicians themselves have identified areas where the system moves slower than necessary. Take sharing information with specialists.

Avi Tsur: I see a patient [and] at 24 weeks, I send her for a glucose challenge test (GCT). But I'm in the hospital and she's in the lab [that] is in [her] HMO (health maintenance organization). She goes to a primary provider, to do the GCT. [It finds] she has gestational diabetes. But I don't get the result. The result goes to the primary provider. He's not treating her pregnancy, I'm treating it, but I don't see the results. So, this doctor that's not treating the patient in this context [is] sent [the] lab. He didn't know why he got a result or understand what it means, and I don't know it. This is a full waste of time.

[Or] We have patients that need to receive chemotherapy. They're sent to [take a] CBC (complete blood count) a few days before the primary physician sends them. He gets a result of very low platelets, very low white blood cells. He doesn't know what it means. All this waste of time could have been saved.

Ott: That’s Dr. Avi Tsur, director of the Women's Health Innovation Center at Sheba Medical Center in Tel Aviv, and the problem he’s describing is a persistent one that stretches across multiple health systems.

Tsur: People are working too hard. They work too hard, and they don't have time to think over the changes needed to work less hard. So it's kind of a vicious cycle.

We hear from many physicians that the biggest challenge in continuity of care [is] that they cannot communicate directly with the primary physician of the patient, so they don't know what happens before the patient arrives to our medicine department. What happens after the patient is discharged?

Ott: This breakdown in communication should be easy to fix, right? Maybe not.

Sara Siegel: [Health care is] not like any other sector of the economy. I know where the baked beans I ordered are, they’re at the depot being packed into the truck. It's going to be delivered at my house. Oh, I'm the next stop. They're five minutes away. We have so much amazing transparent data in our daily lives in most of what we do. And then, we go into a health care episode, and you feel like you’re 20 years behind.

Ott: That voice belongs to one of our guides today as we tackle some of the problems facing health care. Sara Siegel is the global health care sector leader and partner in the Health care Strategy practice at Deloitte UK.

My other guide is Maurice Fransen, a partner at Deloitte Netherlands. He sees the same sort of technology lag that Sara mentioned.

Maurice Fransen: I think people are expecting a lot more from their health care provider in [terms of] technology, from making appointments [to] knowing if there is a delay. If I do a blood test somewhere here and I go 10 or 15 kilometers to a hospital and they don't have the results, or I have to [wait for] a letter—we really should take the next step there. The possibilities are there. A lot of other sectors are doing it, and it’s necessary to take that step.

Ott: I’m Tanya Ott, and today on Government’s Future Frontiers, we’re looking at how technology, particularly AI, is poised to address some of the challenges facing health care—sometimes in surprising ways. But this is not technology for technology’s sake—it’s technology deployed to make health care more human.

Before we focus on technical developments that could transform the health care industry, why do we need to change how we are cared for when we are sick or injured? And are all the challenges the same the world over?

Siegel: No matter what happens in the economy, demand for health care continues to rise year on year at a steady and increasing rate, usually a rate faster than what is provisioned in terms of what we put aside to spend and to pay for health care. So cost is a huge challenge. Demand is a forever challenge. Workforce is a newish and growing challenge. We are probably millions of people short of what we need in terms of health care workers around the world. I know that in the United Kingdom we are tens of thousands of doctors and nurses short.³ So we will have to rely on doing things in new ways. We will have to rely on patient and consumer responsibility for elements of their health care, and we will have to look at technology and creative new ways to see what we can do in a more streamlined and automated way.

The challenge, but also opportunity, that I would mention is technology. In health care, there's a lot of technology that's brought to bear, but there are still huge inefficiencies, and there is still opportunity to make service of a higher quality, a lot higher level of efficiency using technology.

Ott: A shortage of skilled workers can lead to backlogs of diagnoses, and surgeries, and so on. How do these combined challenges affect the clinicians and nurses at the sharp end? Add the lack of funding in health care across the world and where does that leave the sector, Sara?

Siegel: The cost challenge and the workforce challenge collide in a really unfortunate way for clinicians, because if it was just a simple economics problem, if we don't have enough clinicians and everyone was fighting for them, well, then costs would go up and they would get paid tons more, and we wouldn't have the problems that we see in many countries, which is that some nursing roles are less than minimum wage.⁴ There are food banks at hospitals.⁵ That's a combination of life getting so much more expensive, combining in a really unhelpful way with the workforce shortage.

People are really frustrated. They don't feel like they're getting a fair deal and they're tired because after they worked super hard during COVID-19, that was all just everyone putting their everything into it. And then they didn't get a rest because everything that wasn't done during COVID-19, cancer diagnoses, etc., was all piled up and backed up and it's just gotten more and more intense. It just feels relentless.⁶

Fransen: What we see is that the joy of working in health care is not that big anymore. People are leaving because the huge pressure.⁷ Pre COVID-19, we’d seen people were under a lot of pressure and now they still are. So you see people leaving because they are not enjoying their work anymore. And then on top of that there's the cost pressure, so that doesn't make it attractive for new people to step in.

Ott: Particularly when one of the major tasks that health care professionals have to carry out is the endless amount of paperwork that they have to fill out. How much of an impact is that having on the profession?

Siegel: It’s a massive drain on the motivation of those resources. Many clinicians say 30% to 50% of their day is spent on clinical admin [tasks].⁸ If we could free up all of that time, that would be [like] a doubling of the workforce.

And that is, I think, one of the greatest promises of generative AI. Doctors and nurses spend time looking through notes. They spend time looking for diagnostic results. They spend time writing up notes, putting things in different systems. These are things which can all be streamlined, and can all be digitized, especially with tools like generative AI, which can search a patient's longitudinal health record for the whole of their life to see if they’ve ever taken a drug and had an adverse reaction to that drug.

It can summarize notes from a consultation, put it into electronic medical record, translate it into the language of the patient or their family, put it on their patient-facing health app. These can all be done with a single touch of a button. Maybe a clinician wants to read over and check it, but it's not them manually writing things out or typing them in two different places anymore. And that is a huge opportunity.

Fransen: For us [in Europe], the main problem [is for] the nurses, because that's the biggest volume. They have a lot of administrative burden, they lose their joy in work, and they're leaving, health care right now.⁹

Ott: What tools are being developed to transform us from where we are now, to where we want to be? And what will it look like when we get there?

Fransen: A lot of solutions are already there, but still not that mature. In all kinds of situations, you can use [AI] to free up time. In clinical situations, [there are] examples of, predictions of certain diseases or X-rays being analyzed by AI. But I think a huge difference we can make is in the administrative part of a nurse and a doctor.

What they have to do right now is summarize all the discussions that they have, input by using the keyboard of their computer. There are some products already where you can use your voice to just tell what has been going on, or record a conversation that you have been having, and then summarize it and get all the information out of it that you need. The first steps are there, the first products are there, but we need that on a large scale. And that can free up 30%, 40%, maybe up to 50% of time of people working in health care right now. And that will solve a lot.

Ott: These tools rely on something called ambient Intelligence. I asked Renee Yao, who leads Nvidia’s Health care  Life Science Business Development group, to explain.

Renee Yao: Ambient intelligence essentially is leveraging AI to be able to help proceed and understand the environment around it, like maybe in a doctor consultation room. [It’s] using gen AI and large language models—technologies like that—to help automatically capture, summarize, and analyze conversations between the doctors and patients. And that can significantly help reduce health care professionals’ burnout and sometimes even increase patient engagement and outcome as well.

A company called Abridge, they basically use gen AI to summarize doctors and physician notes that are [medical record software–integrated] already. They were able to help save physicians time—about three hours a day—just on manual documentations and medical notes and summarizations. Doctors and the nurses will now have the tools in their hands to be able to focus on what matters most, which is engaging with the patient and be able to have more meaningful conversation with them and make the best care for the patients.

This technology is adding value now. It is being trained on medical content. It can actually understand medical language very, very well. And not only just technology for technology's sake: [they have] figured out how does it integrate into the existing workflow. The doctors themselves don't have to learn new things. Doctors are busy enough already, right?

Ott: I asked Sara what sort of day-to-day tasks could be done by AI and technology to provide the doctors and nurses with more time for the actual health care?

Siegel: Outbound calling to follow up with patients, preoperative checklists, clinical coding, [population] health analytics. Notes, summation, filing of claims. You name it.

Even if you go on to the discharge and social care side of things—support for loneliness, different types of social care roles that don't require physical intervention. There are so many elements of tasks within jobs which can be done in an automated way, either in the background by a bot or by a “digital human,” which people really like interacting with, incidentally.

Ott: An example of that “digital human” is a health agent created by Hippocratic AI. Here’s Renee again.

Yao: When [a health care provider doesn’t] have enough people, we have limited time per patient. Answers to a patient tend to be short and concise, right to the point. Whereas Hippocratic did a demo at one of the technology conferences about a month and a half ago. In that demo, it was an elderly patient talking to a virtual agent about the experience [of] coming out of a surgery. And this patient was lonely, and she wanted to talk about gardening. She didn't really want to quite go into her experience post surgery. And then when the bot was patiently, empathetically answering every single question with care and even provide recommendation on how to better the gardening skills, towards the end of it, the patient was like, okay, that's enough information for me. I don't need more than that. The bot was like, okay, that's great. Still answered everything just as [if it were the] first time talking to the patient, probably 15 minutes into the conversation. So I think engagement like that, the realism of how to talk to a patient empathetically is coming into our care now.

That allows the doctors [and] health care professionals to be able to focus on patients a lot more where the doctors uniquely add value. Back to that Hippocratic example, towards the end of that demo, there was one action that the bot itself could not actually take. It was redirected to the doctor with a summarized note on what happened in the conversation and why an action was triggered for someone that has much more of a health care experience to come in and then engage with the patients for next step.

Ott: How vital is it that the clinical workforce buy into any changes that are being suggested?

Siegel: The clinical workforce is key to the adoption of new technologies. There’s a lot of clinician power in health care systems. And they need to be the change agents. They need to be the ones leading the charge. They understand best where the pain points are and what’s broken and how these how these tools can fix those things. We only move as fast as clinicians are ready to move when it comes to new technology. And that’s why it’s so important to spend time with clinicians.

I was shocked when we did some sessions on generative AI with some health care clients to see how much nervousness there was amongst clinical workforce about this. And when I started talking through the use cases that we were thinking about, they were surprised to see that they were all use cases that were purely there to help them in their jobs and to make their jobs more enjoyable and help them deliver what they wanted to do at a higher quality way. Technology doesn’t work in health care unless there's clinician adoption and clinician leadership. 

Fransen: What you see in a lot of other industries is that change comes from outside the system. And I do think that applies for health care as well. But it has to be in collaboration with the people working in a current system. We can speed up things by getting all kinds of innovative things from outside, but we need to do it together with people in health care.

Ott: That focus on working with clinicians is at the heart of the ARC Center for Digital Innovation at Sheba Medical Center in Israel. Remember Dr. Avi Tsur from the top of the show? I asked him to explain the rationale behind the ARC Center.

Tsur: You have the clinical discipline. You have the machine learning. You have the biology. You put them together into a commercial entity, and we have the potential to really lead a paradigm shift.

Doctors see unmet needs and think of ideas, even if there’s no organization to support them. However, when there [are] the people to help you think of the regulatory pathway, of the commercial pathway, to help you raise the funds to connect you with the right institutions —that's the difference between an idea turning into a startup [or] dying slowly.

Ott: The ARC center at Sheba Medical is an example of clinicians embracing new technology. But in other cases, there is some hesitance. Sara and Maurice say that may be because our expectations are a little skewed.

Siegel: I think right now we’re scared of technology. We think it’s going to do bad things to us, but we’re already doing bad things to us, and our baseline is pretty low. I know that when we’ve trained early AI tools we’ve said, okay, this is getting it right 75% of the time.

And then we said, well, how much of the time are the clinicians getting this right? This was a tool that was reading referral letters and deciding what to do with them. [Clinicians] were getting it right 50% of the time, and the same clinician was reading the same letter on two different days and making two different decisions. So we have huge variability as humans. Sometimes we’re tired, sometimes we’re grumpy, sometimes we’re distracted. Our AI tools are never any of those things. They’re following a set of rules that we decide. And they work seven days a week, so there's just a step change in quality and speed that can come.

Fransen: It’s an interesting part of adoption, because I think we expect from AI tools [to be] perfect, and people aren’t. Of course [when] we have to take care of sick people, things have to be almost perfect. But we also should compare it to how a doctor does his thing and then see how well [AI does]. And AI, the machine can help a human being to make the right decision. And I think it's more accurate, than in a lot of situations in the past where only a human will decide.

Ott: How those AI tools are trained has a huge impact on how accurate they are. And that brings up another issue that some may be apprehensive about large language models and privacy concerns.

Siegel: The fact is to use generative AI, for example, in health care, these tools need to be trained on large language models. [Companies] can’t train them on open internet. They’re going to have to be trained on proprietary data sets. So those data sets are going to sit within health systems or within national boundaries. And they have to be firewalled in that way. That is the way to successfully run generative AI in health care.

There can be anonymization. You can use fake data, but that won’t help as much. You can rent space on models. And there’s all these different hybrid ideas that have been come up. But we have to agree [on] data governance. And I think patients have the right to understand what’s happening to their data, how it’s being shared, what it’s being used for. That’s what the GDPR [general data protection regulation] rules in the United Kingdom do. But there are questions about to what extent does a patient actually own and control their data, which are being asked in every country of the world.

Ott: Data security and privacy weigh heavily on providers of health care AI. I asked Renee Yao of Nvidia how they address the issue.

Yao: From the technology world, we have implemented certain technologies, for example, like federated learning. Essentially, [this means] that we don’t have to move the patient data around. We can keep data where exactly it is and then build a centralized model that can make decisions for the health care professionals. Technology like that is in place to ensure patient data privacy is protected.

Many organizations have put together [a] trustworthy AI council internally. For us, we ended up putting that together to help build a lot of data factories for gen AI services and then make sure that there is confidential computing and make sure that even open-source technology are aligned with human feedback. These are mechanisms and steps from the technology industry perspective working in tandem with health care industries to ensure whatever we put out there have patient safety and privacy first.

Ott: Finally, there has also been skepticism toward AI among workers in some industries. There may be suspicion that the technology is meant to replace workers, not necessarily help them. My guides disagree.

Siegel: [AI] is meant to bring humanity back to health care. If you think about the average nurse just rushed off her feet, doesn't have time to stop and just chat for a minute about how your cat's doing, or if you liked lunch today—that is what I hope can change when some of the admin burden is taken off our plate. The actual humans, we can become more human again and happier again.

There's a huge opportunity in connection and community, especially for elderly populations who live on their own, of which we have a lot in many countries. And I think the concept of the digital human has been really restorative and amazing in some of these pilots. So I think there’s the humans that can become more humans, and there’s the digital humans that can help the humans who feel lonely and isolated.

Fransen: I totally agree. That’s what the discussions [are] about with clients, but also with nurses and doctors. They are afraid that all this technology will get them pressured as much as they get right now. But I think [AI] should free up some time, so spending time with people, getting the quality rather the [one] minute conversation with a with a patient, that really helps.

Ott: Dr. Tsur said that emphasis on freeing up time has been the experience at the ARC center at Sheba Medical.

Tsur: The number of doctors we have, the number of nurses we have, the number of team members—[that] is not going to change. What will change is the way we spend the time. How many patients can they have and how much attention does each patient receive? So clearly, for repetitive jobs, if automation replaces the physician, then this time goes for doing what we really want to do which is explaining [to] our patients about their situation instead of just providing them information that they could receive in a digital manner.

Ott: We’ve seen so many really rapid changes just in the last year or two with AI, and I am sure that we don’t really understand the full capabilities of the technology yet, but with a fair wind, what would the ideal scenario be if generative AI was used to its full potential?

Siegel: I think what our new technology gives us is almost blank sheet of paper. If we didn't have all of the stuff that we have to deal with today, if we just wanted to deliver the service to get the best patient outcome, with none of these constraints, what would it look like? And it would be really different.

We spend all this time putting things into folders. What if search was amazing and you wouldn't have to file anything? How much time would you save? These are the kinds of things we should be thinking about now. Don’t just do today’s processes digitally. Get the outcome you want in the fastest way.

Right now when we think about health care, we imagine a physical place, don’t we? Imagine what a hospital looks like, or we imagined what the inside of the waiting room are. We spend so much time waiting on our GP surgery. I feel like when we get there it won’t be a physical place at all.

We will be in our day-to-day lives and our ring or our phone or our glasses or whatever [will send] a text saying your meds need to be changed, or take this test, it's coming in the mail. It will be a seamless integration in our everyday life.

Say you are someone who lives in a Bedouin village in Saudi Arabia. Your scan [could] be read by the best orthopedics specialist in Cleveland. There’s no reason why there shouldn’t be more equity of access to high-quality health care because geography will not be as big a factor anymore.

Ott: Dr. Avi Tsur at Sheba Medical has been applying that blank-sheet-of-paper approach to maternal health.

Tsur: I had a long-time dream of developing maternal fetal precision medicine, this breakthrough of being able to tailor the treatment to the women’s molecular profile, so we not only tell a patient she is at risk for preeclampsia, but [also that she] will respond to low-dose aspirin. That’s a great breakthrough that we are leading. That’s changing the outcome. That’s providing best health care services.

The other thing is changing the patient journey, what I call maternal fetal telemedicine. Today, we’re celebrating pregnant patient number 50 admitted to our hybrid care program of high-risk pregnancy at home. These are women that used to spend a month or two months in the department doing fetal monitoring twice or three times a day, doing ultrasounds once a day, blood pressure a few times that. But actually, they could be at home. They could be with the children, they could be working and using technology that allows a remote fetal monitoring, using a remote fetal ultrasound and other digital tools we have for a remote assessment. This is a dramatic change of the pregnant women patient journey through tools of AI and automation.

I’m doing the ultrasound remotely. Pregnant women we follow at home, they do the ultrasound to themselves. We only guide them remotely. This is not a vision. This is not the future. This is happening. This is here.

We'll see much more patients doing things remotely, doing them at home, medications reaching to their home, physicians reaching [their] home[s] digitally, not really traveling to the home[s].

Ott: Both Sara and Maurice see promise in the example of Dr. Tsur’s remote care.

Siegel: Physiologically, we’re all the same. So, ideally, there would be perfect global collaboration. The scientific community is amazing at this. They already collaborate on a global basis. We saw this during COVID-19 in all countries.

In the European Union, they're already doing a lot with this in terms of EU health data and data passports between countries, [which] I think, globally, is a big leap. You know, the 28 EU member states, [for them, this] is a great step forward. But private organizations can already do this, you know, they will use their clinicians to look across national boundaries at patient scans in any country where, you know, they have a patient, or they have a practice. So I feel like, yeah, we’re entering glass half full today. We're inching toward it. There’s definitely a lot of boundaries in the way, but there’s green shoots.

Fransen: Diseases do not stop at the border, so why shouldn't we collaborate on a global scale? I think there are so [many] possibilities on these kinds of things and especially technology like AI because it's not simple. You have to train it. If it has to be become a medical device, you have to go through a whole process, and that takes a lot of time and money. And why not collaborate on that?

Ott: That sort of collaboration can be bolstered by AI. Nvidia’s Renee Yao has a few examples.

Yao: [Recruiting] diverse population for clinical trials is important because when drugs go to market, if it's not tested on a diverse population, it could have detrimental effect. But a clinical trial is expensive, [and] making it personalized is even more expensive.

But now with technology like large language models and gen AI video language models, we are able to speed up patient cohort recruitment, finding the right patient for the right clinical trial from months down to like perhaps days. We are able to design the clinical trial with least amounts of amendments needed to be able to quickly get a clinical trial into the hands of patient that need it.

[There’s also] pharmacovigilance. After a patient takes the drugs, what if there is [an] adverse event? This is where we can gather a lot of data from various different populations to ensured that the drugs that we have in the market [are] serving the right populations.

Ott: And the possibilities expand when developers can use modular building blocks to support their applications.

Yao: We have building blocks for drug discovery, for genomics, for digital health, for imaging and medical instruments to be able to help contour [a] tumor faster, to be able to do software-defined medical instruments, to be able to find an extra candidate and design the molecule a lot more efficiently, or maybe be able to help speed up how you do DNA sequencing in less five hours and [for] US$10. Imagine if you just sit in your office with the oncologist, within the same day you can turn around whether your baby has the disease or not.

Ott: The future Yao describes is an optimistic one, but that optimism is shared by our guides to this episode.

Siegel: You can search through molecules at incredible speeds and cut to drug discovery time into a fraction of what it used to be. This will help cut down on the cost and help us find more useful medicines. I also think that there will be a few geographies or health systems that will be real early adopters, and the gains and benefits that they will see will be the fuel that the rest of the industry needs to get going. I also think there will be some crashes, and some fails, and that will be difficult. It will not be a straight line, but I can already see in some of the early pilots just the huge quality and efficiency gains. So I just remain incredibly optimistic about the future.

Fransen: I'm an optimistic guy, so I think we should be optimistic. I think technology's maturing and the necessity to change things is becoming bigger and bigger. [For] people who have less joy in their work because of the pressure and the administrative burden, if we can explain in a good way how technology can help them to focus on the things that they want to do and that they make them proud of their job, then I think those changes will get there in, in [the] upcoming five to 10 years.

Ott: Thank you for listening to Government’s Future Frontiers from Deloitte Insights. Remember to follow and subscribe, so that you don’t miss an episode. We discussed AI in the context of health care, but in the next episode, we’ll be looking at how AI can enhance, and even transform, the work of government.

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