Hey everyone, my name is, and today I'm gonna talk about a particular section of US healthcare system where the healthcare peers go out and do a contract negotiation with the providers. Providers be being the practitioners, the hospitals the provider groups, and how can we utilize. AI to do those contract negotiation better. What's the current state, the current challenge and what's gonna be the future and what kind of solution proposition that we're gonna talk about in this particular talk. So let's go ahead and get started. As some of you know us healthcare system is quite big. It's a trillion dollar industry where. Various entities are involved and contract negotiations specifically that carries out between these two entities. One being the healthcare peers, another being the the, these, all these providers. It's quite manual and paper-based and it's oftentimes creates a lot of confusion. So that leads to a payment error. Negotiation, delay administrative overhead and oftentimes also creates the stain in the provider relationship with the payer. So AI can make this much better, where instead of having a static manual driven payment method, we can do some sort of data driven, AI model driven payment contract where the system can be much more fair. Accurate and efficient in the whole process. So let's deep dive into, in, into the talk more and see why, where we are, what is the current challenge. As I already mentioned. There are many challenges of today. The number one, and most importantly, is a manual process where we have to spend a significant time to do that, do to do that. Contact reviews, and there's a lot of to and fro. With the providers where we have to finalize the rate and then we need to put that rates into the system. So that's being one. And as a subsequent, because of those manual negotiation, and oftentimes that gets to a human error. There's a payment discrepancy, and because of this payment discrepancy, the providers are oftentimes are disa dissatisfied because they don't get the payment on time and and leads to like unhappiness. And the most importantly from a healthcare peers perspective, because you have a department who goes out and do this contract negotiation with the providers. You have so many people involved doing various contracts, be it practitioners, be it hospitals. Oftentimes hospital contracts are quite complex. So you need to, and there are so many hospitals in a particular zip code, in, not in a particular county where. The insurance company has to go out and do this contracting, and then that's a department overall overhaul and the burden. But but if we continue to do that, it would be a quite significant challenge that, that currently health healthcare system faces. And with that, it also creates, as I said some sort of stained relationship. With the providers because this process is not so smooth and it lacks that the data-driven decision making that we are talk, gonna talk about in the stock. Now we know the challenge. We know where we are. Coming from now, let's talk about what are the advantage that AI can solve. The number one being is the predictive analysis, meaning we can forecast some sort of optimal rate while. Doing the negotiation by utilizing historical data market trends and some sort of pattern. So that helps to do a negotiation. What would be the future rate? Be it, say, office visit, be it a PCV visit, or be it your specialist visit for a particular specialty or whatnot. And it also gives a real time adjustment. Because it's a data driven dynamic we can adjust to that contract more of a real time than somebody has to go over manually do that adjustment, put their correction into the system, and then during the claim processing, it'll pick it up. But if we use some kind of a AI driven solution, it'll be more of a real time, faster, smooth, efficient and that brings to the. Third point is a error prevention because we are doing everything through some sort of a AI tool driven approach data-driven approach. It's more of a errorless system than to have somebody or a group of people to go over manually. Review that, put it in the system. So it makes it much more fairer system, much more efficient system, and as a result, the relationship between the provider and the peer is much more better. It makes the working environment also smooth and providers a happy and that makes also the peer also makes happy. Now let's look at some of the leading examples in terms of what are the industry leaders are doing. So here I have taken some of the examples where, for example, there is an, there's a company called Cognizant who has many many softwares that caters to the need of healthcare. One being the networks modeler, where we can utilize those modeling. Methodology to predict the future price so that during contract negotiation, the providers can know what will be the rate change, say for a given year there's a particular rate. And the future year, the next year, there will be a rate change. If we utilize the historical data pattern, make some predictive modeling and tell them this much, a percentage will be increased for a given service. It makes it much more fairer. And there's another leading Medicaid healthcare peers health s they also often use AI driven approaches where they found it. It tends to lead a reduced time while doing all this negotiation. And then we have United Healthcare where. It's one of the leading healthcare system in the United States. They reported like $110 billion for Q1 2025 revenue. So you can guess how big it is They. These companies are utilizing various sort of AI driven methodology to do the contract negotiation because that helps the entire processes faster and efficient and the smooth. Now let's take a look at the framework, like what kind of, optimization that we can do with that data driven. Now the most important thing is it makes the process smooth. Meaning I don't have to deal with all the paper contracts, I don't have to deal with going through each of these terminology. I don't have to deal with going through each of these contract rates, historical data manually, rather. It's more of a. Data driven outcomes, and it can easily be automated into the workflow system. Meaning if there are multiple system, one does the capitation, one does the claim adjudication one does the validation, one does the clinical edits, all various system does its job in silos. But if we utilize. Some data driven approach where we can integrate that into the workflow. And during claim adjudication, every diff every different parts of the engine can talk to each other, and the entire process can be very comprehensive. And the claim processing is gonna be very smooth. Now let's look at the business measures like. What can we achieve by utilizing these AI driven solutions? Now, there are several case studies where performed where they have studied various aspect of utilizing AI in this AI driven contract rate negotiation. The major ones is how soon, how fast we can lock in the contracts do we save any dollar value, and do we increase the fairness? And satisfaction with the providers. And the answer is yes too. And there are some case studies were performed where they have found close to 42 percentage. They were able to lock in the contract and it was way more faster. And that lead to a significant dollar value save on a when this particular case study were. Perform on a lesser number of contract, not on a large scale. And this also helped with the provider disputes because the new art system is very fair and clean. The disputes were very LA and everybody was happy. So it's definitely there was a miserable business impact and it help the system overall and makes the payer system to go towards more this data-driven solution. Now we talked about the challenges. We talked about the various entities that they are utilizing these AI driven solutions. We are talk, we talked about what kind of outcome that we are expecting. Now, if we have to implement, we as in any healthcare payer system has to implement this AI driven solution. What are the stages? What are the steps that they can do? The first and the most important is like. Assessing your current state, where we are do we use a legacy system? What kind of data that we have? Can we transfer the data from one system to another system so you identify the gap and potentially make this quantify can we quantify where and how can we implement? And the second thing will be define strategy. We have to implement some sort of a AI tool, which AI tool is the best to solve that need. And we also have to establish some kind of a governance because healthcare is all about governance, where data is very important. So we set up this governance, we set up the criteria, we set up the right tool, and then we phase out this. Deployment in multiple phases. Phase one being we piloted out with a less number of contracts. And see the results see the improvement, quantify that. And once that is being done, we can integrate that solution into a large scale with a large number of contract and see how that's, that is doing. And we some kind of a figure out, some kind of an ROI and quantify the entire. Results. Now the next one is, okay. What? It's fine. We can do that. What is the best practice? Of course, the best practice is always gonna be. Assessing your system. Are you coming from a legacy system? If you are coming from a legacy system, can we exchange those data from a legacy system to your newer AI driven system? And if we do, what kind of efforts that you need to do? And once you implement, you also have to train your staff who has to. Utilize those tools, how to handle it, how to run it, how to do this do how to do run the entire workflow into your existing system. And then of course, as I said, you also have to employ some kind of an governance because US healthcare system is quite regulated. We have to follow various regulations and the rules. So we have to have some kind of a governance and once. All this done. We make sure everything is implemented correctly and fairly. And then as a result, what would, what is the outcome? What is the ROI, what is the KPI? And we can measure all sort of stuff. Now the one that I have talked many times because of this manual as of today approach where it oftentimes leads to a provider dissatisfaction. But if we implement this newer AI driven solution, it's more of a fair rate. Settings where data-driven approach creates this this contract with the provider and it's trend, then the relationship with the providers and when the contract rates are set using more of a transparent data-driven framework, both parties are happy and it makes the process itself is quite smooth. So this definitely helped. To the to this relationship between the provider and the payer. And it also helps to make the payment system is more transparent and predictable. Meaning when we use any kind of AI tool, use the historical data, run the predictive model, model the future price, and go with this negotiation and say, this is gonna be the rate for, say. Say a particular specialist visit for in our office settings. And if it is fair, it's all predictable, transparent, and that will also help the provider to understand what they're gonna be paid. And the entire process is very very laid out, integrated in the workflow. Everybody's happy. Now the future innovation. Now, when we are here, we also have to look at, okay, fine, we can utilize the ai, why, where the AI stands as of today, where we are going, and what kind of different methodology that we can implement. The first being is the natural language processing, the NLP most heavily used in the ai. Wherever we implement where we can. Take any data, extract it, and interpret to the right contract language so we can totally eliminate this manual review. So that's probably one of the best use case of AI that we can do in this context. And another one is gonna be we can utilize the blockchain based payment. Some of the healthcare system already started doing it more of a in-house closed blockchain system where the payment to the provider or some kind of a decision making happens automatic with a smart contract. Yeah. And the third one is being the market. Adaptive contracts, like oftentimes healthcare system is keep changing, regulation, keep changing. There is CMS, there is state and everybody makes their changes on time. So if there is a change happens, and because we utilize some sort of a AI driven solution, we can. Adapt to this market strategy on the go and implement the solution much more quicker so that claim processing can happen on the up to date market data. And the last, and most importantly, is the value based optimization. Because AI can align your payment based on the value of certain predefined task, the member can do or the provider can do based on that, the payment can be made and scheduled. And the last but not the least, I wanted to end this talk with this particular closing remark is AI is here to stay. It will shape and help many aspect of our life, not necessarily the healthcare system specifically in our talk that involves related to the provider contracting world between the peer. And the providers. It has many element, integr and the complexity involved. If we adapt today, we know in the future we can utilize those tools and make the system much more fairer. Efficient and make everybody's happy. So overall, it'll also help the members who can go to the provider and get the service provider is happy to provide the service. So overall, this is the future. I can see AI is here and it's gonna be implemented in this particular section, I hope. Everybody had a good time listening to me and with this closing remark I wanted to say, if you really wanted to talk to me regarding this particular topic, please ping me in LinkedIn. I'm happy to assist and help you on that. Thank you so much for listening and tuning on this talk.