I'm going to be co-moderating this session with Dr. Evelyn Decker from AMC in Amsterdam and Pradeep Bhandari. The first talk of this last morning session is going to be by Dr. Shayan Thakkar, who's going to be discussing institutional setup, implementing CAD diagnostic, and the study detection CAD systems colonoscopy in a urinoscopy unit. Thank you. Thank you. Thank you all for having me here again. So I'm going to talk to you now about institutional setup, incorporating computer-aided detection and computer-aided diagnostic colonoscopy into the unit. And so the questions I thought we'd go through here now are why computer-aided detection and computer-aided diagnosis should be important to hospitals and ASCs. What's the financial benefit to the institution to support subscription or capital expenditures? What is the healthcare value of these systems? What does the implementation team look like? What are the technical aspects of implementation? What is the marketing framework? What outcome should we measure? And what are alternative pathways for implementation? So let's start with the top. Why CAD E and CAD X should be important to hospitals and ASCs? And so we know AI tools possibly increase ADR by up to 50%. They can contribute to a reduction in healthcare costs, reduce cancer diagnoses through increased ADR, of course, and reduce need for pathology through diagnose and leave strategies. We've seen that in the detect study, that up to a 15% increase in the adenoma detection rate. And so when it comes to healthcare value, we know that healthcare outcomes achieved relative to the cost of care, assessment to guide improvements and achieve better outcomes at lower costs, and then ultimately a use of value-based framework can improve quality and seeks a return of health. And so for any hospital-based implementation, early engagement of value analysis team really remains key. And so the next question we'll answer is, what is the financial benefit to the institution to support subscription or capital expenditures? And so when it comes to computer-aided detection systems, what we've seen is that in varying experiences there's an increase in ambulatory payment classifications. And this has ranged from anywhere from 27% to 49%, depending on the site. And the argument that we believe is true when it comes to utilizing artificial intelligence is that if you see an increase in ADR, you will likely see an incremental shift in billing with polypectomy. If you resect more suspicious lesions, you will see an uptake in pathology revenue. And if the findings are histologically significant, you will see these patients on shorter surveillance intervals. And this is in counting any increase in volume that you see from AI to attract new markets. So the next question is, what is the health care value of these systems? And so this was a nice micro-simulation modeling study that was performed and basically looked at computer-aided detection and the impact it had on overall costs. And we see that as we use computer-aided detection, the costs of colorectal cancer ultimately go down. And this is especially highlighted in a vertically integrated health system that we're seeing more and more health systems move to. And so when they looked at this data more closely, not only was there an improvement in colorectal cancer cases, of course, from this micro-simulation modeling, again, but there was a nominal increase in incremental reduction in cost per patient of approximately $57. And so this really translates to quite a significant reduction in spend per year in terms of colorectal cancer-related costs through this model with the AI model, which was almost half a billion dollars. We know that computer-aided diagnostics really has the impact, has the ability to reduce costs. Mori and colleagues actually looked at a study, looked at their own data, and they were able to, a prospective study where they actually looked at the implication it had on costs in the areas that we spoke about. And luckily, this was a little bit touched on in a prior study, prior talk, but in essence, what they found was that there was anywhere from a 7% to 20% reduction in colonoscopy-related costs from the utilization of computer-aided diagnostics with a diagnose-and-leave strategy. And as such, depending on the sites that it's located in, as such, this is another aspect of how these systems can really be of significant value. So these are the current CADE and CADX systems that are available across the world and in the different countries that they're in. And so I guess the next question we'll have to ask is, what does the implementation team look like? And of course, this has the players that we would all expect, the physician leadership, the nursing leadership, IT representation, clinical biomed vendors, medical legal representation and ethics representation. And as was stated in a lecture earlier by Dr. Cascani is that education is really important here in terms of making sure that there's dedicated in-servicing with all teams and everyone understands the goal and what purpose we're really trying to accomplish here. I think that form of education can ultimately help these implementation endeavors be successful. There's also technical aspects of implementation. What type of timeframe we're looking at with respect to implementation, compatibilities of hardwire versus wireless systems. The purpose of implementation, is it for a clinical use, research or training and the impact on ancillary equipment when limited or shared ports exist. We encountered such a challenge at our institution when we use a wireless system to essentially try and use a computer aided detection system to communicate and we were losing color throughout. And obviously that was a major challenge, but thankfully with the help of Biomed, these are how you overcome these opportunities. So what about marketing? So marketing frameworks are quite important with respect to how we ultimately further grow these systems. And this can be through press releases, this can be through direct marketing to patients, this can be through social media posts and social media outlets, but ultimately this is all different ways in which we can participate with our colleagues or with our patient community and ultimately enhance the adoption and implementation of the AI systems. And so this is just an example of some postings that can be placed or direct communication to patients, but when it comes to outcome measures, really what we're going to be looking at is the impact on adenoma detection rate, the impact on withdrawal time and overall procedure time. We're going to look at the volume trends in the units with the impact of marketing campaigns that occur. And we also want to look at what it does to compliance with colorectal cancer screening in the market as well. And finally, we'll look at financial outcomes and polyp and cancer misrates that are associated. One other aspect to touch base on is really novel pathways of implementing these artificial intelligence modules. And one pathway when the traditional pathways aren't there, or if the interest is there, is the research pathway, of course. And we did a survey kind of looking at how we can be successful, what's the best practice in terms of adopting artificial intelligence or building an AI type research pathway. And it starts with things like this, attending seminars and meetings, followed by finding a computational scientist to work with, getting some pilot seed funding that then can lead to larger funding, and ultimately develop a model that can be of significant benefit to answer the question that you may have in hand. And so this is one other way of trying to bring these models into place. There's also opportunities for grants and support through the ASGE. There's equitable healthcare distribution through the ASGE that, you know, our center was one of the centers to be awarded, National Science Foundation or the National Institute of Health also has varying pathways for artificial intelligence modalities. So in conclusion, you know, these systems and corporations are multifaceted and process optimized through a multidisciplinary approach. I think healthcare value at the unit level is quantified by the improvement in ADR and Paul differentiation and the cost of such implementation. The landscape of improved part of detection and from diagnose and leave strategies can provide a significant delta opportunity for any center, any unit in order to all ultimately optimally adopt artificial intelligence. And then the technical and research aspects of implementation as well as innovation grants provide mechanisms for adoption and can also be considered if standard pathways become challenging.

computer-aided detection

computer-aided diagnostic

AI tools

adenoma detection rate

healthcare costs