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Philips improves healthcare by combining data, AI and clinical knowledge

Combining healthcare data, Artificial Intelligence and clinical knowledge offers Philips enormous potential to improve healthcare. It provides the company with limitless opportunities in many application fields. Philips’ future aim is to make a digital twin of the entire human body to improve treatment and even prevent people from getting ill.  

 

This requires close collaboration with universities, hospitals, knowledge institutes and companies worldwide. Therefore, the global health company works in open innovation settings such as e/MTIC. Philips also offers SME, startups and scaleups access to marketplaces so that they can test and offer their AI innovations in a clinical setting.

Professor Doctor Milan Petković is Department Head of AI, Data Science and Digital Twin at Philips. He is also full professor at Eindhoven University of Technology (TU/e) and interim AI lead for Philips Connected Care and is involved in several EU public private partnerships.

Healthcare is in digital transformation

“Philips uses AI for very different purposes and in a broad variety of fields,” Milan explains. “Think of connected care for patient monitoring, sleep and respiratory services or developing solutions for non-invasive treatment purposes. Data and AI both have unique value for Philips. Healthcare is in digital transformation. There are clear opportunities for AI to facilitate digital transformation of healthcare.”

Using data and AI to improve healthcare

“Our aim is to create advantages by using data that is currently being created in healthcare. We use these data to address what we call within Philips the quadruple aim. It boils down to first, improving patient outcome and quality of care, secondly managing cost of care and then finally improving the experience of patients as well as the experience of healthcare providers.”

Value is in combining data, AI and clinical knowledge

“Philips’ vision is based on combining AI with existing clinical knowledge. We have access to a lot of data, AI algorithms and clinical knowledge. By combining them we can create reliable AI that would be safe for patients and reliable to use in healthcare. By our approach we support healthcare in abstracting important insights from data and developing solutions that can adapt to the context in which they are functioning and can be seamlessly integrated in workflows.” 
 

Use AI in a transparent and responsible way

“As a company we want to use AI and data in a responsible and transparent way. We have a webpage on which we publish all our AI enabled propositions. An example of such a proposition is our Intellivue Guardian System. It detects early signs of patient deterioration and gives hospital personnel a direct sign to intervene so that they win time to act.”
 

Training AI algorithms to detect signs of early deterioration

“We were able to develop this system because we had lots of historical data that we could use for research purposes. The data concerned vital signs of patients as well as treatment outcomes. Because of these data we could correlate the outcomes to vital signs, and we were able to train smart algorithms that detect signs of early deterioration. These kinds of systems directly contribute to hospitals’ KPI’s as they reduce mortality rates, costs, and readmission to ICU. Moreover, they directly contribute to a patient’s well-being and lead to better health outcomes and experiences.” 

AI and Digital Twin currently aimed at health systems and devices

“Philips’ vision on AI and the Digital Twin currently is related to making models of medical systems and devices, such as imaging devices or non-invasive devices. We want to do everything in our power to prevent those devices from stopping during treatments, especially in the case of non-evasive surgery. In case of non-evasive treatments doctors are totally dependent on the devices. As we do not want them to break down, we perform predictive maintenance. Based on historical data we predict when a device will break and to prevent that from happening, we schedule maintenance before that time. In this way, we guarantee that the up-time of the devices is increased to the maximum.” 
 

Applying Digital Twin concept to patients

“In the long run, we want to extend this concept towards patients. By modelling patients, we can also offer them better treatment and perhaps also some preventive interventions instead of them getting ill and needing cure. The models will help to either prevent illnesses or to improve treatments and provide better care. An example is the model of a heart, it can help in treatment and guide interventions. We already have models for organs like hearts. In the end it would be great if we could cover a whole person. To achieve this, Philips works closely together with universities and knowledge institutes worldwide.”
 

Extending deep sleep phase by AI

“An example of a recent AI application by Philips that is also aimed at care is the smart sleep headband. It is a battery powered and resource contained device that runs Deep Learning algorithms. It measures brain signals and then determines the stage of sleep a person is in. During the deep sleep phase, it sends signals to extend that phase. During a clinical double-blind test, a considerable percentage (70%) of the participants stated that their sleep had improved.”

e/MTIC great example of win-win partnership

“As I said before we need partnerships for innovation. In our collaboration we practice open innovation. An example of such an open innovation partnership is e/MTIC, a joint innovation med-tech center in Brainport Eindhoven in which Philips, Eindhoven University of Technology, Maxima Medical Center, Catherina hospital and Kempenheaghe work together. It is a great example of a win-win partnership and an example of a smaller health data space in which you have the infrastructure to collect data, data stewards to curate the data and data sharing methods that allow you to facilitate PhD students, professors and us as a company to perform development of these data driven health care innovations.”

Impressive results

“It has led to a series of impressive results. A few examples are more than 80 PhD projects and the development of the healthdot sensor that monitors patients inside and outside hospital walls. Besides that, it has also led to new methods and tools for creating, collecting, and sharing data. These kinds of partnerships are of great importance to Philips.”

The Netherlands is well-represented in EU initiatives

“The European Commission is promoting these types of health data spaces and would like to take next steps. Philips is involved in several EU and Dutch collaborative projects. As a country we are already stepping up and organizing ourselves within initiatives such as NLAIC. Our companies, TNO and universities are participating in these EU initiatives. What is important is that we bridge the gap from cloud infrastructure to value propositions and data and AI innovation.”

Philips offers SME, scale- and startup access to open innovation platforms

“An important success factor when it comes to collaboration, except for proper investments, is that there should be a win-win situation. The outcome of a partnership needs to be beneficial to all partners involved. There should be clear use cases that are developed to save lives and improve health. That is also what our open innovation platforms are aimed at. We provide SME and scale- and startups with marketplaces for different areas so that they can test and offer their AI solutions in clinical practice. If you have smart ideas these platforms are really valuable.”