In this episode, we’re pleased to welcome Anish Patankar, SVP and GM of Oncology Software at Elekta, to discuss patient-experience data powering AI.Learn how to listen to The Hospital Finance Podcast® on your mobile device.
Highlights of this episode include:
- What is Elekta
- Current climate in cancer care
- How to streamline practice and improve efficiency to provide the best patient care
- Balancing high productivity
- What’s next for Elekta’s oncology informatics platform
Kelly Wisness: Hi, this is Kelly Wisness. Welcome back to the award-winning Hospital Finance Podcast. We’re pleased to welcome Anish Patankar. Anish is the SVP and GM of Oncology Software at Elekta and is a health tech expert specializing in the cloud, Internet of Things or IoT, big data, and artificial intelligence. From building leading-edge digital information solutions to delivering revenue growth, Anish has experience spanning digital health tech, medical devices, education, and security software industries. His work is powered by the belief that even seemingly infinite and unstructured datasets have patterns hidden within, and harnessing that data into a form that impacts human experience is where he does his best work. He received his MBA from the University of California and a Master’s in Technology from the University of New York. In this episode, we’re discussing patient-experience data powering AI. Thank you for joining us today, Anish.
Anish Patankar: Thank you for having me, Kelly.
Kelly: All right. Well, let’s jump in today. Can you tell us a little bit about Elekta and your role?
Anish: Sure. So Elekta is a company headquartered in Sweden, and it started with the neurosurgery solution called Gamma Knife, treating brain tumors with gamma radiation. But since then, Elekta has expanded rapidly into radiation oncology medical devices, but most importantly, Elekta has also been taking leading steps in software solutions for oncology care. We are the first company to offer a comprehensive oncology information system that covers both radiation oncology and medical oncology. We offered the first solution that also covered proton therapy, the first in the market to offer automation and scripting when it comes to oncology care workflows, and also one of the first to incorporate patient-reported outcomes into solutions for oncology care. My role specifically is covering all software products that Elekta offers. I’m responsible for the software portfolio end-to-end. And I’ve been with Elekta for three years and it’s been a journey I’m really enjoying.
Kelly: Sounds like a fascinating organization. What is the current climate in cancer care and how is that posing challenges to personalized and effective treatment?
Anish: So, when it comes to the current climate, as you know, patients are living longer, so that means there’s more and more instances–more and more increases in patient cases for cancer. But it’s not only in older people. We are also finding that there’s a lot more diagnosis of cancer in younger people, unfortunately, right? At the same time, there’s a tremendous amount of innovation in terms of medicines, other treatments, that are hitting the market, but the challenge is cost. There’s another challenge which is access of the best standard of care in developing countries. So, while there’s a lot of focus on automation and productivity and improving clinical outcomes, the biggest challenge is how do you give the best treatment to each individual patient and focus on best overall outcomes: clinical, financial, and access to care. And I think that, today, is going to be the biggest challenge globally for all clinicians involved in cancer care.
Kelly: Yeah, no. Definitely. Cancer cases are projected to increase to 28 million new diagnoses annually by 2040. How can Elekta help practitioners streamline practice and improve efficiency so they’re providing the best possible patient care?
Anish: So Elekta is really getting involved in many ways. Ultimately, this projection of increase of new diagnoses to 28 million is a challenge in terms of the capacity we have in the healthcare system. How many clinicians can we have delivering the best care for every possible patient? And ultimately, this leads to longer wait times, suboptimal care for patients, so how do we avoid all of that? How do we avoid the burnout that clinicians are facing in the system? And you see, the best thing that we can do here, as a leading vendor in cancer care, is to leverage AI and automation. Automation helps in workflow. It helps in decision support, both clinical and operational. As an example, there’s so much innovation and literature. Hundreds of papers coming out every year that say what treatments are better with the new innovation. How can doctors change a few things that may be different from how they’ve been traditionally practiced in medicine? How do you simplify access to all of this new development in terms of research, in terms of literature, for the clinicians?
And that’s where AI comes in. You cannot expect every physician in every corner of the country or in every region to keep up with all of this. And AI can really harness all of this research literature and bring this to the clinicians at the point of care so they don’t have to worry about going to another system trying to keep up or go to every single conference that’s out there. Another possible way, of course, that we are looking at is introducing more and more standardization in terms of the care pathway that can be offered for every patient. Increased predictability in the system in terms of what outcomes– or what outcomes we can get with what treatment that we can prescribe, and all of this while keeping the cost as low as possible. In terms of operational decision support, what we’ve been looking at is leveraging AI in terms of sometimes simple things like voice transcriptions, the interactions between patients and doctors in the doctor’s office. How can you transcribe this with the least amount of error possible? This really improves efficiencies in terms of AI helping with billing and encoding core captures. So, things that are a little bit in the background but really help with reducing clinical burnout so that the doctor or a clinician don’t have to go in and type everything up all over again. Somebody has to review it to make sure there’s no errors, everything is coded correctly, so AI can really also help on the operational side, right? All of this, we are looking at ways to help prevent burnout, make sure the efficiency of the clinicians is really high, and at the same time, making sure that the patients are getting the best care possible. So, at Elekta, we are really looking at AI and automation both from a clinical perspective but also from an operational perspective.
Kelly: Very interesting. One of Elekta’s goals is, quote, “Working toward a world where everyone has access to the best cancer care.” How do you help companies maintain a balance of high productivity while still providing the best possible personalized care?
Anish: So now, if you look at the ecosystem of clinics and hospitals that are involved in cancer care, right, there’s a lot of variation in the world and even within the US in different markets. There are community hospitals. There’s best-in-class economic institutions. There are teaching hospitals affiliated with the National Cancer Institute. So, there’s a whole range of options that patients have based on where they stay, what environment they are in, what socioeconomic factors are in play, and of course, what kind of treatment they seek and what are their preferences. So, with all of this, if you take into account–we believe in an open ecosystem. We don’t believe that any one vendor will have all the answers for all of these factors and variables that are in the mix. And what I mean by open ecosystem is really pushing and opening up access to the treatment data of patients to the social determinants of health and other data that everybody should have access to, that then they can build the best solutions that work in every possible setting. So, community hospitals may choose a mix of solutions that work better for them, and the teaching hospital can choose a mix of solutions that work best in their setting, right, because they also may have a medical university. They have teaching staff, so their needs are different. A community hospital is more focused on efficiency sometimes. Then there are rural clinics, right? So democratizing access to care, democratizing access to patient data, having an open ecosystem, meaning there’s no data or no proprietary data that vendors are locking in. Opening this up, helping all companies get that access to data, build the best solutions that a clinic needs in their setting in their environment, and ultimately, then, patients having the choice based on where they stay, what’s their insurance scenario, what kind of preferences they need to have, what kind of treatment they prefer. All of this, I think, we can help democratize, increase productivity, and yet, giving the best possible care to every patient.
Kelly: That sounds fantastic. How can Elekta increase patient engagement in their care journey?
Anish: I think in so many ways. I mean, patients are really, really getting more and more engaged in their own care. They’re no longer sort of just taking sort of a recommendation they would get from a particular physician, right? They always nowadays are getting more and more secondary opinions, making sure that their preferences are heard. And ultimately, how can we make it easier to make sure that patients can put in their preferences before treatment, during treatment, and after treatment into the care cycle, into the solutions that the clinicians are using, what we in the industry called patient-reported outcomes? And these patient-reported outcomes have to be something that is tied into the oncology information system or any electronic medical record, right, that clinicians have access to so that they can take every decision in every step to know that this is what the patient needs. These are the side effects that the patients are facing. These are the requests coming in sometimes from the patient, but also from the care team, from the patient’s family, friends, who are the other people involved in the patient’s care cycle. How do we take all of that into account?
So, we are really pioneering patient-reported outcomes into Elekta’s oncology information system, making sure that, real-time, during treatment, patients can report back saying, “This is how I’m feeling today.” Between two treatment cycles, patients can report saying, okay, how is their blood pressure? We are integrating into different devices to bring that data in automatically, but also allowing for patients to manually say, “Okay. The nurse said I may be expecting nausea after this treatment, but this is really off the chart. I’m super nauseous,” or, “I’m having this side effect,” right? This allows for the care team to intervene, and then the patients can see that the engagement, the time and energy they’re putting in is actually being looked at. The care team intervenes on time, preventing urgent care visits, preventing unnecessary anxiety and trauma for the patients, and unnecessary cost for the patients at the end of all this. So timely intervention by the care team, timely guidance from the nursing staff from the doctors to say, “Okay, if you’re feeling like this, we better not wait for your next appointment next week. You better come and see us tomorrow, or day after tomorrow. This is serious.” Or, “This is expected. Don’t worry about it. Deal with this in a certain way, and then we will see you next week and we will continue the treatment in the following way.” So just having that regular interaction, having a simpler way of reporting their symptoms, reporting how they’re doing, and the care team looking at it and intervening rightly, we can really boost patient engagement in the care journey of the patients. So not only do patients need to have a way of interacting with their care teams, but they need to see that the care team is listening to it, intervening as necessary, and that’s going to really boost patient engagement in our view.
Kelly: That all sounds fantastic. What’s next for Elekta’s oncology informatics platform moving forward?
Anish: So, I think a few things and I’ve covered some of it already in the previous answers, but I think one big thing that even regulation from all countries, all regions, pushing forward is opening up access to data. No more proprietary data-holding. And we believe that data interoperability is key for the best treatment solutions that can be put out there because we believe no one vendor has all the answers in every environment. So democratizing access to patient data, I think, is really big on our radar and we really believe in pushing that forward with all participants in this industry. We also believe that creating an ecosystem of applications and solutions will then deliver the best-personalized treatment for every patient as well and then leveraging all the advances that artificial intelligence technologies are bringing to the fore. I mean, these innovations are coming from other industries, but healthcare is typically a little bit slow in adopting them, for good reason, because we need to make sure that this is actually really working before we can sort of incorporate these things in our solutions.
But things like generative AI, language learning models that are specific to healthcare, using the ontology, meaning the data that is being used by clinicians to define certain disease areas, define certain treatment cycles, what we call ontology in the healthcare space– utilizing specific healthcare ontologies specific to oncology in this case, but it could be for cardiology and rheumatology and other domains, building the right language learning models on top of it, and then leveraging all of that to say, what’s the best care pathway we can put this particular patient on? How can we transcribe the doctor’s notes much better to make sure there’s no errors in encoding? How do we sort of boost productivity in terms of scheduling patients so that we are not giving up slots or having unused downtime at the hospital for medical oncology or our radiation oncology? So just leveraging advances in generative AI using the right language learning models that can be then applied back into this oncology care setting, I think. We really believe that’s the direction we want to move forward in, and we are really focusing on that going forward.
Kelly: Very exciting times ahead for you all. Thank you so much for joining us today, Anish, and for sharing these valuable insights and all this great information.
Anish: Thank you for having me, Kelly. It was a pleasure.
Kelly: And if a listener wants to learn more or contact you to discuss this topic further, how best can they do that?
Anish: So, I mean, from a perspective of what Elekta is doing, I encourage listeners to go to our website, Elekta.com. There’s a whole section on Elekta ONE software. So, our intelligence software solution Elekta ONE–we really showcase what are all the things we do there to boost patient engagement and also boosting personalization of cancer care while maintaining the productivity for the clinician, so I encourage them to look at Elekta ONE on our website. And if they want to reach out to me? LinkedIn. Just send me a private message and more than happy to engage with anyone on this topic.
Kelly: Thank you for sharing that and thank you all for joining us for this episode of The Hospital Finance Podcast. Until next time…
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