Name:
Improving cancer care through digital health: an interview with Marcos Gallego Llorente
Description:
Improving cancer care through digital health: an interview with Marcos Gallego Llorente
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Duration:
T00H07M15S
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Content URL:
https://cadmoreoriginalmedia.blob.core.windows.net/f225d463-d3dc-4158-8ea2-905727267b5e/OC - MARCOS - V1.mp4?sv=2019-02-02&sr=c&sig=nEKhQoHEPz4xcOrT61eK9XNOwm2w2z%2BBMYYvNjMVpWg%3D&st=2024-05-13T15%3A33%3A55Z&se=2024-05-13T17%3A38%3A55Z&sp=r
Upload Date:
2023-11-01T00:00:00.0000000
Transcript:
Language: EN.
Segment:0 .
[AUDIO LOGO]
MARCOS GALLEGO LLORENTE: My name is Marcos Gallego. I'm a senior consultant at Vintura, and adjunct professor at the IE University in Madrid. [MUSIC PLAYING] So what we discussed is the fact that taking decisions in cancer is really difficult. You need many doctors-- medical oncologists, radiation oncologists, surgeons, pathologists, radiologists --to meet together for one hour or two hours a week to discuss, 20, 30, or even sometimes 40, cancer patients.
MARCOS GALLEGO LLORENTE: That basically means that you have three or four minutes per patient to take a decision on what is their treatment going to be for the next number of years. And the treatments are getting more difficult as well, because now you need medical oncology treatments, also sometimes radiation oncology treatments, surgery, and there are advances in all of those fields. So what happens is just because of that, complexity is becoming bigger, and the amount of patients is increasing, and the stakes are increasingly higher, because, at some point actually or increasingly, patients can actually fully recover from cancer by using these treatments.
MARCOS GALLEGO LLORENTE: So what we discussed is, how can we use digital health to the fullest extent to ensure that digital health tools can help doctors to make those decisions? The need is clear, and then there are three large challenges at the moment, in order to ensure you actually can integrate those tools in the clinical practice. We said challenge one is actually finding the right value.
MARCOS GALLEGO LLORENTE: The fact is that tools can be very different. And the value that a tool gives can actually change a lot, even if it has the same description, so to say. So, is a tool going to be helping only radiologists? Is it going to be helping the MDT teams at a whole? Is it going to be helping at an organizational level, at a hospital efficiency level? So they have to find the right value. And for that you need to really talk to doctors to find what the value is going to be, and how that tool can actually be integrated in the system.
MARCOS GALLEGO LLORENTE: Two, and this is something that doctors really care about, is those models need to be explainable. That basically means, you need to know why certain decisions were reached, and you need to know that if a similar patient, a similar input is given to the model, a similar conclusion is going to be reached. Otherwise, it has no value. So it needs to be explainable and replicable.
MARCOS GALLEGO LLORENTE: And then third, then we actually then thought about integration. So in order to ensure these tools can be used in a clinic readily, then actually you need to be ensuring that they fit in the current pathways of care, and they fit in the current reimbursement model. So who gets paid for what, and at what level, and also who pays for exactly what service. And that is also quite difficult, because at the end of the day, the current economics work very well for drugs, for treatments.
MARCOS GALLEGO LLORENTE: The current economics work very well, obviously, for Med Tech, for large equipment, but there is not that much of a model yet on how to reimburse these kind of digital tools. So they need to be integrated economically, and also then in the day-to-day practice of doctors. And those three things are the biggest challenges. We thought about some solutions. One of them is to ensure that you talk to the doctors fully, to develop anything, and then from there, everything else ensues.
MARCOS GALLEGO LLORENTE: Once you talk to the end user, once what data you need to show to then the payers and the insurances or the health care systems, then at some point those tools will end up being hopefully successful, and hopefully used every day in the clinic. [MUSIC PLAYING] Doctors are excited, and we've been talking to doctors for a year on a number of projects, that we do actually for digital tools on MDTs, and they are very excited.
MARCOS GALLEGO LLORENTE: They see problems on current tools and, obviously, this is why we still don't see that much adoption, but that doesn't mean they are not open to the right tools. So, they will always keep being happy, trialing things, testing things, and making pilots on things. And at some point, we will start seeing more and more use cases. I think it's a matter of time, effort from different startups to trial their own products, and then investment, and willingness.
MARCOS GALLEGO LLORENTE: [MUSIC PLAYING] So for me, actually, it's taking this one step further. So how can we use AI to help doctors, or anybody actually, make treatment decisions. And, for that obviously, we need data from many sources. So we need data from patient records, we need data from radiology, from pathology, from genetics, from the socioeconomic status of the patient, real world evidence.
MARCOS GALLEGO LLORENTE: And then we need to compare that data with everything else, and with outcomes. But, at some point, that will be possible. And it will be very exciting to see if you have a certain input, a certain set of data, different modalities, it can actually reach an output, in this case, a treatment decision with a certain likelihood of success, because the algorithm making this decision has been trained on hundreds and hundreds of thousands of, in this case, patients data.
MARCOS GALLEGO LLORENTE: [MUSIC PLAYING] And then again, this is taking this even one step further, right? Can you at some point, and this will be possible eventually, right, make a full digital twin of a human body, or at least of a section of an organ, or of a system? And I think, work on digital twins is increasing.
MARCOS GALLEGO LLORENTE: There already some success stories on infrastructure. There are also digital twins on other sectors. And in health care, obviously, it's very complex. Complexity is extreme, but at some point, we'll start seeing digital twins that work for making models on clinical decisions.