September 23, 2020
Data sharing between hospitals is key to improving stroke workflow
Ludo works at the University Medical Centers in Amsterdam in the Emergency Department as a Trauma and Emergency Radiologist. We interviewed Ludo during the COVID-19 lockdown when he was working from home a lot more.
So firstly Ludo, how did you come to be an emergency radiologist?
A little bit of chance and a bit of luck. I am a broad-oriented physician, meaning I’m interested in a variety of things. Radiology concerns a lot of physiological systems in the body, which appeals to me. When my predecessor left the emergency department, I jumped at the opportunity to become an emergency radiologist!
What I love about my job is that you always know there is room for improvement and things are always evolving. If you look at stroke, for example, CT perfusion was discovered, then the success of endovascular therapy followed. We then had to work out how to efficiently transfer patients to intervention centers with all the important information, and this is where Nicolab has come in.
I have been very impressed with the performance of StrokeViewer, and I really believe it is doing better than the others because it encompasses all aspects of emergency stroke care, from start to finish.
That is great to hear, what’s it like being at the heart of an emergency department?
Our Prime Minister recently said during the COVID-19 outbreak: “you should accept that you will make 100% of your decision based on only 50% information that you have, and you should be able to cope with that problem.” I think this is a great way to look at it, even in the emergency department. It can be difficult to accept this, and people often want to take their time to make a clear diagnosis, avoiding mistakes. But my state of mind is that it’s important not to see it as a problem but a challenge, and do your utmost best to make the best decision from the 50% of the information you start with.
Everyone knows if you only have 50% of the information, sometimes you’re wrong, and coping with that in the emergency department is often the main issue for emergency radiologists. You have to put the different bits of information together like a puzzle and come to a conclusion as best you can.
I enjoy that challenge the most, in a timely fashion. It will be interesting to see how artificial intelligence performs. Diagnosing a stroke patient is difficult. I am an expert, so most of the time I can see where the problem is, but juniors can’t identify the problem as quickly. In that aspect, artificial intelligence is very important and has the potential to take the pressure off.
When have you felt most challenged at work?
Well, what is happening now is people have recognized more and more how useful the CT scanner is. When I first joined the ED we only had one sliding gantry CT scanner at the emergency department. But now we have two scanners covering three shock rooms.
This means we can have three acute patients arriving simultaneously to the ED when a lot of hospitals would only have the capacity for one. Basically, triple the pressure, which can be the biggest challenge when you have to make treatment decisions and give a fast, and accurate, diagnosis. But this is also what I enjoy the most about my job!
From your professional perspective, can you explain some of the challenges and gaps in the emergency department?
The challenge is, of course, from an operational point of view. Everyone has to be very strict, and everyone involved wants to do things their own way, which can result in delays. People generally think when they are busy, they are doing valuable work, but that is not always true given the circumstances!
It is a skill of knowing what is necessary for the patient before things have even happened, and ensuring you stick to it and do nothing unnecessary.
The second part is the infrastructure. If there is a lot of data that has to be calculated from the scanner, this can take time. We now accept this and have to wait a minute or so, but we know how valuable time is.
A faster calculation of data would be really helpful. Maybe that is also where artificial intelligence solutions would work, and a part of that is you calculate them, and then it has to be transferred to the PACS system.
That is not working as fast compared to what I want. I know the moment it is scanned and calculated, but I can’t see it. I still have to wait some minutes. People are not always aware of how much damage is done every second. When you are 15 minutes slower there can be huge consequences, and this is something we should share. So I would say the main challenges are time and sharing the information.
Administering endovascular treatment (EVT) one minute earlier would impact both patients’ lives and hospital’s finances.
In the current stroke workflow when a stroke patient comes into the ED how do you go about making your decisions?
This is really a team effort, and we regularly discuss the optimization of the workflow with all the stakeholders. When a stroke patient comes in, I try to be as clear as possible, and I want the other team members to also work as efficiently as possible without wasting time: non-contributing tests should be avoided.
It’s a matter of making a sequence of binary decisions in a strict manner, with always the possibility to adapt the workflow to the circumstances.
Is it a stroke? Yes. What do I need to know? Is it hemorrhagic or not? Is another diagnosis possible? What about brain perfusion? Are there filling defects visible on the CT Angiography? Are the radiological findings and clinical findings comparable? What type of therapy should be started, intravenous or endovascular? This should be performed in a timely fashion: Time is Brain!
In your opinion where do you think AI could be most useful for you and how could it be implemented into the radiology department?
Although it wouldn’t be typically thought of as AI, I think the calculation of penumbra and core is very useful but still needs some further studies. It’s great that support from AI calculations is now accepted. In my opinion, if you want to implement a new AI-powered tool into a healthcare system, it needs to require very little effort from the hospital’s end. It needs to be something that is easy to implement, easy to use and easy to interpret. The moment it is presented to physicians with very little effort, then they can accept that they have to know what it is all about!
Can you see how AI can have a helpful role in the stroke workflow?
Yes, there are certainly a few ways in which I think AI benefits the workflow. The most obvious role is the recognition of clots which for me is not the most difficult challenge when I can correlate it with the perfusion calculation, but some of my colleagues can have some difficulties in finding the clot, so there is definitely an advantage in that.
On a broad scale, we have quite a few parameters, which is great when trying to diagnose patients. The question we really want to know the answer to is “what will the patient outcome be for a particular treatment?”. I know this means taking into account many parameters.
Some interventionists are reluctant to treat older patients (for example, over 80 years old), but I believe for every individual it is different. Some 85 years old may have at least another 15 years of valuable life. So this can be a moral problem. Hopefully, in time, when enough data is available, we will be able to predict the probability of a beneficial outcome for a particular treatment.
What is your experience of AI in the stroke workflow and do you see it adding value in your opinion?
One of the most important things StrokeViewer does for me is the transfer of patients’ information between hospitals and radiologists, which can save so much time. When a patient is on its way to you, knowing already all the information the primary center has found means there is no risk of wasting time repeating procedures.
This has been of great value for us when trying to diagnose and deliver the best care.
The system we had in place before meant that we were unsure whether CTA had been performed for half of the cases or we were unable to see the images, so time would be wasted. Whereas now we always have the information beforehand and can make a quicker decision, whether it is worthwhile for the patient to go directly to the endovascular suite. In that respect, AI is great support for clot detection.
Are you excited about the potential impact of AI and the future?
Yes, I think there are interesting ways in which AI can support several processes and have a positive impact, which is exciting to see. I also do have my reservations, but it’s not a fear. Personally, I think it is illogical to think AI will take over and humans will just be the support to AI.
The human brain is very different compared to AI, even though AI is the product of humans. I think the human brain functions very differently, therefore AI will help support and strengthen us.
Our AI-powered clinical decision support system offers a complete assessment of relevant imaging biomarkers within 3 minutes