Transforming Patient-Centric Cancer Care

[MUSIC PLAYING] Facing a cancer diagnosis is something no one wants to experience even with all the remarkable advances in treatment. Today, I'm joined by Dr. Sunil Verma, AstraZeneca's Senior Vice President and global head of the oncology franchise, who's at the forefront of transforming cancer care. Dr. Verma is not only driving the development of more personalized treatments but also championing the importance of early detection.

We recently sat down and discussed his vision for the future of cancer care, including the role of artificial intelligence, the need to clarify often confusing screening guidelines, and how exactly we enhance the patient experience. If cancer has touched your life, you will want to watch this conversation. Dr. Verma, thanks for joining me.

It's great to be here, John. Thank you.

Well, let's start off and get right at it because you talk about the need to transform oncology care. So if we need to transform it, what do we need to change that we're currently doing?

So there are some important elements that we have to consider as we think about what are those challenges. So one is the complexity of care. When we think about the options patients have, clinicians have, even 5, 10 years ago, we really had chemotherapy, radiation therapy, surgery, and targeted treatments. Then, of course, came immune therapy, which has really helped many patients.

But thinking about the next 10, 15, 20 years with antibody drug conjugates, cell therapies, radio conjugates, the complexity of care is going to be substantially more complex. And I think we need to make sure that we are partnering effectively to simplify care and not make it more complex. So an important element of how we think about partnership, how we think about the future state, is how do we make sure that we simplify the care decision making so that patients are able to access the right therapy at the right time in a more meaningful manner.

I mentioned to you before we went on that I follow you on LinkedIn and like your profile. You talk a lot on LinkedIn about the need for these partnerships. Who do we need to be partnering with? Is it the patient community? Is it regulators? And they don't think of partnership in the same way. Where are those partnerships in your mind that we need to be more focused on?

As a clinician and thinking and delivering about cancer care and through my academic and clinical care, you tend to be very focused right on the patient in front of you. How do we bring the right therapies and the best therapies to that patient? But I think being in industry and being in pharmaceutical companies over the last few years, it has really given me an appreciation that the health ecosystem is very broad, and the partners who are part of that ecosystem need to be much better integrated.

And these partnerships include from diagnostic companies to make sure we have the right biomarker information, from data companies who are able to assimilate information and provide information to the clinicians, to the health systems, and maybe the patients in the future. We have to think about the health tech companies who are also involved in thinking about how do we make sure that the patients have the right information on their hands.

But we are collecting the right information from patients to make the clinical journey much better, much smoother. We're thinking about also how do we leverage the new emerging technologies, such as AI, into the mix and also the education companies-- of how do we make sure the information is disseminated to the clinicians and to patients along the way. So that whole health ecosystem requires partnerships.

We can't talk about transforming oncology care and not talk specifically about AI. So you've mentioned it a couple of times. And I'd want to know where you kind of sit on that continuum where there are some folks that are saying, "You know what? We haven't even scratched the surface of the power of AI."

And there's others that will say, "You know what? It's never going to replace a physician and make that cancer diagnosis." And there's others over here that will say, "Everyone should have a second opinion that's generated by AI." So where does Dr. Verma stand on the role of AI in transforming cancer care?

I think, John, AI is here to stay and will improve cancer care decision-making. So to share a couple of examples and also where we see the future of AI in the decision-making process-- so as a breast cancer clinician 20, 25 years ago when I started my clinical career, a patient with hormone receptor positive breast cancer who would come and see me, I would have an option. I can give this patient chemotherapy, or I can give this patient endocrine treatment. Those were the two options

Ten years ago, 5 years ago, we may have five different options: endocrine therapy, endocrine plus targeted chemotherapy. If you think about the next 5 to 10 years, there may be 10, 15 different options for that specific patient. Now, there's a multitude of factors that can determine what is the right option for that patient. It could be how they responded to prior therapies.

What is their disease status? What is their biomarker information? What is the cancer treatment resistance pathway that has evolved? All of that element and all of that information is really difficult for a clinician to track, measure, comprehend, to provide a specific treatment decision.

And everyone's not a specialist as you are that's treating.

Absolutely not everybody is a specialist. And maybe at an esteemed cancer center and an academic institution, you're able to assimilate and there is a structure. But we need to make sure that the information is available, accessible, scalable, and to make an informed decision irrespective of where the patient is going to go.

And so AI machine learning has a critical role in taking a look at all of those information streams to be able to augment, to be able to support the clinical decision-making process. So I do feel that in the next 5 to 10 years, a clinician who is sitting in front of a patient will say, "Based on my experience, here's a recommendation. Based on the guidelines, here's a recommendation. And based on data and informed decision-making, which is powered by AI and machine learning, here is a recommendation that is specific to you."

And that information will lead to a better decision-making for the individual patient. But also, and just as importantly, it will also lead to less disparity in care between different institutions. Because what you're doing is you're really trying to personalize the care for that patient. And irrespective of where the patient goes, that information is accessible to make sure that that individual patient will have the right treatment decision.

So that element is, I think, critical. And I think AI is going to have a significant role. And just as importantly, where we're talking about patient experience, imagine a patient who can also learn as to what their experience could be on a specific medicine, using AI-based content to say, "What would be my experience look like on this medicine versus this medicine?"

Let's just even look at breast cancer and colorectal cancer screening where we have guidelines. We talk about guidelines. But then the guidelines sometimes are different between different professional societies, and that can be confusing to clinicians. What's the right age? What's the right methodology?

And then it can also be confusing to patients. Am I at average risk? Can I do this test versus that test? How do we help educate clinicians about these changing guidelines, which can be different based on different medical groups? It can really be frustrating. What's your counsel to them?

So first of all, I am a strong advocate for guideline-based care. I think the literature and the evidence clearly points out that following guideline-based care leads to better and more improved patient outcomes. I think where there may be inconsistencies or maybe different interpretations is really what is the endpoint that is being measured in some of the studies or some of the guidelines.

And that endpoint can vary from earlier diagnosis. And are we diagnosing patients at an early-stage setting to an endpoint? Are we truly improving survival for patients by doing that? Or is it leading to better value-based health care decisions from a payer perspective?

So there's different endpoints that are potentially why these different guidelines may have different outcomes or different measures. So what we can do, and I think what we can facilitate, is to connect some of those critical endpoints to say what really matters. What really makes the biggest, most impactful impact from a health system perspective or from a patient perspective?

And I think as a clinician, I remember-- I recall having those discussions with patients to say yes, on a health system level, the guidelines recommendation is this because it looks at resource utilization. It looks at if we were to not spend the money on screening guideline A, where you were to shift resources--

But ages could be different. Some people could say start mammograms at this age versus another age. One year we're supposed to do shared decision-making on prostate cancer screening.

Yeah.

Now we're supposed to talk more aggressively about--

Absolutely, John.

Folks can just be like, I'm not sure what I should be doing. But patients aren't sure.

But that is really where the clinicians and a patient to say, "Here is what the guideline recommendation is. Here is what the evidence suggests for you as an individual patient. Here is the recommendation, and here is the value proposition based on the recommendation." So I think the guidelines' audience is very different when we're thinking about age, when we think about resources. But that clinician-patient interface on individualizing those guidelines is really where the decision-making happens.

So I think that it's good to have those different guidelines, but we have to also recognize who the audience is for those guidelines. At the end of the day, the patient and the clinician needs to be able to personalize and-- the term, our personalized risk identification-- and have those guidelines really personalized to that specific patient based on their risk criteria and applying those skills. But I think to your point, we need to simplify and help and support our oncology community so that the value of their work is realized and greatly appreciated.

We started off with transformation. So I want to come back and end with transformation. I want to be very practical or have you be very practical and kind of walk our viewers through what you envision that workflow might look like in 5 years from now. Or maybe it's 10 years from now in the sense, will people be getting more care at home?

Will there be much more targeted therapies with fewer side effects? Will we be at a point where cancer won't be in the top three causes of death in the next 5 to 10 years? What's your vision of that transformation? What does it look like practically?

John, it's such a great thing to sort of imagine, because I do think--

And I'm going to play it back to you in 5 years.

I hope all of these elements come true. I do think cancer is going to be cured. I do think cancer is going to be a much more chronic condition. And I do think that patients will have a much stronger voice in their decision-making than they do today. So let's walk through what that future state is going to look like.

So I think care will be closer to home. I think patients will have access to medicines and resources closer to home. I think there will be a greater emphasis on data that we're collecting from patients directly-- whether it's through variables or other tools-- that we are able to predict before a patient gets a side effect on the medicine or we're able to predict before the patient's disease progresses. And we're able to predict before a patient gets into trouble so that we are really proactively managing the patients rather than patients coming to us with a symptom.

I think we'll be able to personalize and individualize that care and decision for that specific patient, not just based on a cancer biomarker report but based on all the other factors that we discussed-- how they responded to prior therapies, what and how their cancer has progressed, where has it progressed, what is the composite information related to wherever the cancer is present-- so that diagnostic element and that cancer informatics element can be blended together to be able to individualize.

I think the patients will have a greater say based on multiple modalities of therapy to say, "Yes, this therapy gives me this value. This therapy gives me this value, but here is the patient experience or here is what I would want my life to be like." So they'll be able to have that shared decision-making that is based on their own individual value system. So we will have that certainty.

And finally, I think we will have a more equitable system where irrespective of patients where they are with zip codes they live in, the access and the type of therapy and their outcomes will be much similar. And I think if we reach that state in the next 5 to 10 years, we're looking at a much better overall health system, a much more value-based outcome, and, more importantly, a system that is more equitable and accessible.

And that will be a transformation of our current system. Well, Dr. Verma, I want to thank you for taking the time today.

It's wonderful to have this discussion. Thank you, John.

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