Bruce Ramshaw, Caresyntax - Studio Interview | LSI USA '24

Bruce Ramshaw  0:00  
Hi, I'm Bruce Ramshaw. I'm Chief Medical Informatics Officer at Care Syntax. Care syntax is a surgical intelligence platform, and we're offering a clinical data as a service to healthcare industry partners. We partner together and go into clinical environments to apply our value based CQI method, clinical quality improvement method, so we can get real world data and ideally measure value based outcomes and help our clients assess the value of their products in real world patient settings. So we have a variety of clients. Some of our clients are the biggest, top five medical device companies in the world, and some clients are near startup, just getting on the market, trying to assess and demonstrate the value of their products in real world patient settings and everything in between. So if a company has a desire to assess the value of their product in real world patient settings, we can do that, and we do that with better data, because we curate the data with the clinical team and the industry client, it's faster access to data because our method is quality improvement, so it doesn't require IRB submission or clinical trial. And so we can price our service at a fraction of traditional clinical trial pricing. What's happening in the real world. One of the problems with traditional prospective, randomized control trials is the evidence that's generated in that controlled setting doesn't translate into the real world. So when a product comes to market, we want to help them understand very quickly where's the value proposition that product and hopefully we can demonstrate where and in what circumstances, what procedures, what patient groups, does their product really generate lower cost, improved outcomes for patients? The other area, we've had a lot of work in off label use of products when products are being used appropriately by the clinician to help their patient, but it's not a current indication. We've had two successful FDA approvals to award new 510, KS for expanded indication removal contraindications using only our CQI method, which, again, doesn't require IRB submission or clinical trials. So we've kind of had the validation with the FDA that our method is high quality in terms of not just patient outcomes and demonstrating value for the client, but also for regulatory processes. I think what we're doing is helping to really apply systems and data science principles to healthcare. We haven't been using data science appropriately. We are healthcare is still mainly functioning under what's called reductionist science, and what we see is incredible fragmentation, variable quality, and until we apply systems and data science principles, appropriately, we're going to have an unsustainable healthcare system. So what we're doing is helping to facilitate that by building in our platform of data and analytics infrastructure that's built for the entire patient process. That's that's the shift right now. All of our healthcare function is in fragmentation. Fragmented there's the clinic, EMR, there's the hospital. EMR, the or functions separately from the ED. And nobody has really built the data around the patient process, and that's what we do. In the next couple of years, we are going to have a data and analytics platform that can be automated, acquisition of data, cleaning of data, analysis of data, visualization of data. We're teaching data science to healthcare and clinicians, and with that automated platform, we'll be able to make this a part of every patient care process for each clinician. Right now, when a patient comes to Doctor, I spent 30 years as an academic surgeon, and I was considered an expert in hernia disease, but when a patient came to me with a hernia problem, I didn't know what was the best value treatment, or even the best value mesh, because I had to have educated guessing with the appropriate data and analytics infrastructure. Into healthcare, we can generate predictive algorithms, or the predictive probabilities of which patient is which patient is going to have best value treatment of the options are available, and it'll give us probabilities of which treatment is most likely to be most valuable and which treatment is going to be potentially most harmful. Patients, people, human beings, complex systems. They all fit into clusters. And if we use the data and the analytics appropriately, we'll be able to have that level of predictive ability in healthcare. Our mission is pretty simple, and this that we're functioning under a scientific paradigm called reductionism that's not valid in the real world. What what we need to see happen is this scientific paradigm shift to systems and data science so that we can have a sustainable healthcare system globally. And the neat thing about the science is it requires us to work together collaboratively across the globe, because ultimately, when you generate algorithms in each local environment, we'll have a ceiling on our ability to improve unless we share knowledge and we collaborate and we share algorithms with each local clinical site around the globe. When we do that, and. Reflect patients from Africa and patients from Asia. When we network those algorithms, we have the highest predictive ability and we can achieve the ongoing success of lowering costs, improving outcomes and seeing a sustainable healthcare system globally in.