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AI & Smart Hospitals in Asia - Caroline Clarke, CEO & EVP, Philips APAC speaks to Caroline Clarke, CEO & EVP of Philips APAC on smart hospitals, AI adoption and how these affect the healthcare landscape in the region.

With the growing implementation of 'Smart Hospitals' in Asia, how does AI factor in this?

At the latest Radiological Society of North America (RSNA) conference, a global authoritative industry platform, healthcare leaders worldwide shared some of the biggest challenges they face when managing complex, disconnected workflows. Not only are these leading to workplace inefficiencies, but they can also negatively impact patient care, staff experience, outcomes, and cost. This is where we see artificial intelligence (AI) coming in, connecting the dots, and taking the complexity out to support healthcare professionals and patients at every stage of the care continuum.

Adoption of digital technology, such as AI, is a critical step in building smart, sustainable hospitals. The way we see it, smart hospitals will play a pivotal role in the smart and connected healthcare ecosystem of the future, serving both as a specialist hub and as an orchestrator of care. Instead of providing all services under a single roof, they will focus on using smart technologies to deliver highly specialized care i.e. acute care, diagnosis and treatments for the most complex patients and play a more prominent role in managing population health, offering seamless experiences following patients wherever they go. Digital technologies including AI must be integrated into every aspect of care delivery in smart hospitals to improve operational efficiencies, deliver clinical excellence, and provide seamless end-to-end patient experiences both within and beyond hospital walls.

The good news is 55% of healthcare leaders in APAC are already investing heavily in AI, while 82% predict AI will become a top investment area within the next 3 years, according to Philips’ latest 2022 Future Health Index Report.

The healthcare landscape encompasses a wide spectrum of categories from medical tech, sports medicine, and even clinical research. Which category of healthcare would the deployment of AI have the most limitations and the best results?

AI can help drive positive results across all spectrums of healthcare. For example, we are seeing great success in clinical decision-making and disease diagnostics. From identifying diseases to direct clinical decisions, AI systems can collect and analyze data to provide meaningful insights.

Take radiology, for example. Through smart integrated diagnostic systems, radiologists can better characterize diseases and reduce scan times. Used in oncology imaging, cardiac imaging, and interventional radiology, these systems have demonstrated reductions in time to diagnosis, repeat scans, and follow-up scans.

The key for AI to have the best results, though, is for healthcare staff to be trained on how to use these technologies and understand the data collected correctly. According to our most recent 2022 Future Health Index report, 40% of APAC healthcare leaders are already using data for predictive analytics, 30% are collecting and storing data, and 28% are using data to automate tasks. To enhance operational efficiencies and advance clinical and diagnostic confidence, it is vital that the appropriate staff training and education are implemented to help facilitate the understanding and management of the collected data and application of AI.

Do you think AI can completely take over the decision-making process by healthcare providers down the line?

AI will not completely take over the decision-making process. Instead, it will support healthcare workers to do their job as effectively and efficiently as possible. In clinical workflows, for example, AI-powered solutions can automate and enhance the work of healthcare practitioners by helping to sort through vast amounts of data, thereby helping to overcome the data overwhelm many are facing today. What’s more, it can also advance clinical and diagnostic confidence, speeding up the diagnosis process and giving patients and healthcare providers more time back.

Can you give an example of a successful implementation of AI in a hospital setting?

There are many examples of successful implementation of AI across the care continuum. In cardiac care, AI can take the complexity out of ultrasound measurements by automating manual and repetitive labour. Based on their own clinical assessments, healthcare professionals can then accept or modify the measurements, giving them a powerful tool to enhance their expertise while remaining in control of diagnostic decision-making.

Another example is in radiology, where AI algorithms can help analyze magnetic resonance (MR) images. For example, in neurology, it can scan images of the brain and detect subtle neurological changes over time. This has been shown to improve diagnostic accuracy in multiple sclerosis patients by 44% while reducing reading times. It can also enhance Computed Tomography (CT) imaging, where patient mispositioning is a common challenge. With AI-enabled camera technology and image reconstruction, patient positioning can be improved, radiation doses can be reduced, and image quality can be enhanced, thereby improving diagnosis.

Last but not least, AI can also support monitoring patients remotely for timely diagnosis and care. The MeCare program of the Australian healthcare provider West Moreton Health is a good example of this. When the hospital faced disproportionately high numbers of emergency department visits and potentially avoidable hospitalizations, it sought new ways of making quality care accessible for high-need, chronically ill patients. Remote patient monitoring turned out to be a vital part of the solution.

In partnership with Philips, West Moreton Health developed the virtual patient engagement program, MeCare, which uses home-based medical devices to collect patient-reported outcome data and biometric indicators including blood pressure and oxygen levels. Powered by AI, outcomes are collected and reviewed in real-time to proactively engage participants with personalized health coaching. Early results were significant, showing a 28% reduction in potentially preventable hospitalizations in chronically ill patients.

What is Philips' involvement and role in such medical advancements and what are their plans for the future in this space?

At Philips, we believe the value of AI is only as strong as the human experience it supports. That’s why we combine the power of AI with deep clinical knowledge to create solutions that integrate into the workflows of healthcare providers and people’s daily health routines.

To achieve this kind of seamless integration, it requires that we involve all relevant stakeholders, including end users, from the very beginning of the development process. We provide solutions that enable a durable, reliable diagnostic infrastructure so that hospitals can continue to benefit from their investments and integrate innovative service models and smart digital solutions that increase both workflow solutions and circularity while improving healthcare outcomes.

In a sector as complex as healthcare, no individual player has all the solutions. But through partnerships, ecosystem integration, and new business models, we strive to complement and benefit our customers, patients, and society. By combining the strengths of different ecosystem partners and embracing value-based incentives, I believe, as a sector, we will be able to deliver a better and more human care experience.

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