Insights on MedTech

There are many different paths. You could come in as a healthcare professional – as a doctor, a nurse, or an allied health professional (e.g. a phlebotomist, a perfusionist, or a podiatrist). Coming in from the more technical end, such as engineering or data science, is also possible. You can also take a route related to process engineering – designers are always needed to look at the holistic experience of patients and staff, and to answer questions. For instance, why would anyone want to adopt this medical technology that has been developed? Or why would the department head of surgery want to buy this? People working in communications, project managers, and IT professionals are also needed. MedTech is a multidisciplinary sport, and there are many ways to go about it. It’s also been earmarked as one of the growth potential areas by the government, and there’s lots of support for it.

It’s important to ask yourself what angle you want to come in from – from the perspective of a nurse, allied health professional, or engineer for example. If you’re not sure, you could also read up about the industry, or take up online courses related to the area you want to go into. Where possible, you could also take up internships in relevant areas. Even though as an intern, you might be junior and new, you can still contribute your skills in a way that makes an impact. 

Industrial experience is one opportunity, for instance if you’re looking to understand more about clinical aspects, such as how a patient would interact with a product, or how a clinician would use software that you’ve designed.

If you’re working on a specific product or have a specific interest, you could use LinkedIn to see what others have done to get to where they are, or opt for a more direct approach by interviewing people. For instance, I might be working on a particular software or have a particular project for university, but I’m interested in how a surgeon might interact with this. You could find someone who currently is in that field, and ask for feedback or opinions off the record.

About 2 years ago, MedTronic bought a start-up called Digital Surgery, which was started by NHS surgeons in the UK. When you’re a new surgeon, it’s very hard to get time with senior consultants. These surgeons were looking for different ways to gain confidence with surgical procedures, and created an application called Touch Surgery, which records surgical video. The current method for recording surgical video is through a USB stick, but you generally just get a mp4 file out of it, which is hard to access because no one has time to catalogue and compare the procedures over time. And so that’s where digitisation comes into play: if you use cloud computing, you have the processing power to compare lots of videos over time. If you use artificial intelligence (AI) and machine learning,  we can also train an algorithm to automatically identify why certain surgeries take up more time, and provide insights into why that might be. Often, surgeons will record their procedures to reflect and learn from them, particularly when there might be complications or when they’re new to a procedure. Cloud computing also enables surgeons to share these videos in an easy and secure manner.

There’s two ways to think about MedTech across the Asia Pacific. One would be enhancing or enabling access to healthcare in the rural communities of Australia, China and India, for example. How can we create something that enables access to healthcare for those who are not experiencing the benefits because of where they’re geographically located, or how much they are able to pay? Even in more privileged countries, in today’s context, resources are being diverted to fighting the pandemic, and we come back to the issue of ensuring equitable access to healthcare. Medical devices are a way of bridging that gap. 

From a technical perspective, certain hospitals or networks are looking to push the boundaries of what can be done, or what more can be brought out of a specific process. How can we increase efficiency, derive the most value from the healthcare system, and reduce waste? In terms of technical innovation, Japan is an impressive example. MedTech companies in Japan are capable of providing quality products at really competitive prices, and still manage to push technical boundaries. 

This also brings up things like AI on X-ray systems. X-ray systems are adequate on their own, but does AI help us, for example, to see more urgent patients first? Can we make better decisions as a healthcare institution or company because technology has enabled us to? 

There’s a voluminous amount of data in MedTech. As previously mentioned, AI is used to improve operations. It can also be used for predictive analytics. Individually, your smartwatch records your heart rate, oxygen saturation, and step count. In a hospital setting, you have blood tests, temperature, and so on. Historically speaking, a clinician would look at charts and try to observe trend lines. However, with the power of analytics and digitalisation, you are now able to discern that a patient has an 80 percent chance of crashing in the next 6 hours. These capabilities are in play and are getting more accurate. 

The other thing would be AI in the realm of imagery — CT scans, X-rays, endoscopies — where you have AI system diagnostics. The software is able to look at an image and identify a certain type of cancer, the same way Google Photos is able to identify images of a train. That gives the physician an extra level of confidence, which translates to saving lives. 

It’s an important question. The amount of people who die due to medical error is phenomenally large. We’re not talking about negligence in this case, but a system within the realm of human error. Operational principles like Six Sigma aim to reduce error. Data in itself helps foolproof this, and so do system flags, such as electronic medical records with ordering systems that are pre-populated, or dosage alerts in the medical records system. Solutions can also lie in the field of predictive analytics. In terms of medical errors, yes, it is well recognised in the field, and I think that the trust is in technological solutions to bring that down.

The Singapore government has been hugely supportive, with funds directed to start-ups and projects borne out of real problem statements (that have come from our clinicians). One example would be a mobile anteroom, which basically converts a normal individual room (within an hour and a half, and with just 2 people) to a negative pressure isolation room. Building one costs a lot of money, and there’s a lot of opportunity cost as well, since they are not always used. To have the capability to rapidly convert at a tiny fraction of the cost, and then convert it back again, is highly valuable.

There are lots of robotics projects involved in healthcare where we look at reducing manpower requirements, an issue which is very critical for Singapore. Another reason why we do this is also staff safety, such as reducing staff exposure to cytotoxic waste, which goes beyond productivity. A lot of it is also about patient experience because patients see digitalisation and automation elsewhere — in the airline and hotel industry, for example — and they ask for it. We absolutely have to be ahead of the curve and we should be giving this to them.