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Data science in healthcare comes to medical imaging technology

Philips Healthcare executive Jeroen Tas says advanced medical imaging combined with data science is important in the transition to value-based and personalized care.

This is part two of a two-part interview with Jeroen Tas, CEO of Connected Care and Health Informatics at Philips Healthcare. At the RSNA 2016 medical imaging conference in Chicago in November, Tas talked with SearchHealthIT about how data science in healthcare is augmenting radiology and medical imaging technology in the transition to value-based care. In part one Tas talked about advances in medical imaging and the personalization of healthcare.

What are some of the key trends in medical imaging technology, such as incorporating data science in healthcare, and how are they advancing radiology?

Jeroen Tas: Radiology has been kind of the same for 100 years. Radiologists look at images, interpret those images, and translate that into a diagnostic assessment of the clinical condition of the patient. And medical imaging technology has improved a lot. But now, we're suddenly going to make that leap where we started looking at imaging in the context of a much deeper understanding of the patient.

Radiology is really going beyond imaging. Radiologists are starting to become more like clinical data scientists.
Jeroen TasPhilips Healthcare

We've heard some radiologists say: 'Hey, if I really understand the relevant information from the lab test, from the history of the patient, if I can pull in these prior studies and quantify them and look them in the context of what I'm looking at today, if the patient has cancer and I can look at the genomics and the pathology of the patient, my diagnosis becomes way more precise. And because it becomes more precise, it's going to be way more relevant for that personalized treatment plan.'

Radiology is really going beyond imaging. Radiologists are starting to become more like clinical data scientists. Many of the forward-looking radiologists probably won't even want to call themselves radiologists in the future. They still will use imaging as an absolutely critical aspect of diagnosis because we can see so much in images. But they're going to look at things like the application of artificial intelligence to [the billions of stored medical images]. What if we can start applying it to all these images? What can you start seeing that's new even if you did an MRI four years ago or you did an X-ray two years ago?

How is data science in healthcare working with medical imaging technology now and in the near future?

Tas: Let's say you did an MRI of your brain. We can now go back, start applying the software and the software can start looking at how things changed between what you did two years ago and what you do today. Is there more on this image that we can see then, actually, where you were looking for the first time, because it's always done with an intention. If we look at an X-ray of a heart, we may look at certain aspects. But we can go back and start seeing much more, and this will add to the understanding of the patient so it will give us, again, a deeper profile of the patient. And now, if we aggregate it over thousands of patients, we're going to see incredible things.

Jeroen Tas
Jeroen Tas, CEO of Connected Care and Health Informatics at Philips Healthcare

How will medical imaging technology change as healthcare moves toward value-based care?

Tas: Value-based care is much more organized around outcomes. Healthcare will actually move much more to early intervention and prevention because if you can help somebody early on, you do a procedure and then you help that person live a healthier life. You avoid downstream complications and, therefore, downstream cost. If we really start applying value-based care, we're moving much more toward driving those outcomes. It will make it more specific to you as a person. If you have a history, let's say, of heart disease in the family, it may prompt you to have a customized program for you to try to avoid heart disease.

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