Hey everyone. I am Dave back, and welcome to my talk. Today I will discuss about how AI is transforming healthcare claim adjudication, an area that's historically been bogged down by lots of manual processes and inefficiencies that leads to rising cost. We will explore how AI technologies enable faster, smarter, and more accurate claim processing. While also helping reduce fraud and improve overall satisfaction for the providers and members. So let's get started. So those who are familiar with the United States Healthcare System, they know us. Healthcare spendings a lot. To put it into prospective, last year in the year of 2024, US spent like approximately $4.9 trillion. That's roughly 17.6 percentage of nation GDP. And that's a lot. And when that, there's a wastage happen on that close to $5 trillion, even a tiny percentage seems a lot to give some stats. On that roughly 30 percentage of the claims. In, in all the insurance companies together, they do manual processing and that means real human efforts for every third of a claim, and that's a lot. Administrative costs in US healthcare is roughly three $50 billion annually. It's a massive chunk of it goes into processing and reprocessing claims. The average claim cycles is about 45 days from submission to payment to the providers. And there is also roughly 7.5 percentage error rate, which can lead to denials, resubmission and provider members. Frustration of course. So as you can see, this entire process needs a lot of disruption because claim processing takes a lot of spending in various aspect, and also that involves fraud. So we have to see how AI can help this entire workflow. So to, to get to get started, we have to see how AI is stepping in to improve this. There are multiple ways. The first would be the automation through RPA, which is robotic process automation. That helps process routine claims which are rule based, repetitive and has a pattern logic. Definitely takes away a lot of manual efforts out of it. And the next one would be the, some kind of an intelligence. No, via machine learning ai that can identify patterns, detect anomalies, and even learn from its own adjudication outcome so it can get better. And another one is. Going to be through some NLP. That plays a significant role by extracting structured data from unstructured clinical nodes and documents. Because healthcare system is full of different entities, each of them maintains their own system. So there is a potential for. Lot of unstructured data. So NLP can help to make them structured. So some kind of an intelligence can help to process those, make the process more efficient and faster. And finally it's gonna be the validation through some AI based ruling gene that can dynamically apply peer policies to, to incoming claims so that the policy check intact. And we know whatever intelligence is applied, it's being validated and thoroughly checked. So going from here what does all do? We implement all of these what it can help. So the first is going to be the faster processing. Of course. It's definitely gonna be faster than manually adjudicating millions of claims some organization have. Noticed that it reduced adjudication time up to 75 percentage, and that's a lot. It also comes with accuracy improvements. Error rates can drop up to 80 percentage, which means fewer claim denials and fewer appeals either from members. Other providers. This also leads to a 30 to 40% reduction in operation cost. So that means fewer people, fewer correction, more claim process per day. So the efficiency definitely increase. And of course the last. Not the least is most important is everyone is happier when things process faster. Provider get providers get paid faster and the members experience fewer delays and the complaints. So overall the system gets more efficient, streamlined. And smarter, so everybody gets benefit out of it. Now, if we look into some case studies, there are a few there are a few insurance companies that the insurers now or the peer, they have tried various AI stack in their. Claim adjudication, workflow and and observed many significant results in us. There are many insurers. Some of them are highlighted in the case studies. One of, one of the example involved. Implementing machine learning for complex claim types like specialty pharmacy claims that needs a special type of approval authorization approvals and processing those claim because it's a very high dollar claim. Auto adjudication as a result of implementing AI improve almost 80 percentage resulting in millions of dollar in annual savings and higher provider. Satisfaction. Another peer adopted NLP to analyze clinical document documentation in real time which led to a 65 percentage reduction in processing time and significantly improved fraud detection. And last one is a robotic process automation helped increase monthly claim capacity by 1.5 million claims. And that's a lot. Because oftentimes we get a lot of claims based on a commercial health plan. And you have to process them on time so that you don't get delay on provider payment, otherwise you have to pay interest. So if we apply some kind of RPA stack in the claim processing workflow, it definitely helps. And it makes things faster and oftentimes accuracy also increase than your manual trim processing because whenever there's a manual process involved, that oftentimes leads to a manual error. Now. Now let's talk about the fraud detection because it's also one of the important part of claim education now how it can help specifically how AI can help to detect the frauds and be compliant with your compliance like hipaa and other healthcare compliances. So the first one, it can spot unusual behavior in the billing. That comes in the regular claim files whether it's medical claims other hospital claims like UBO four or CMS 1500, no matter what type of claim it is. In your EDI gateway. You can filter it through all this unusual behavior and the AI can also check the historical pattern from the same providers or the across various providers to match it up and map all these animals to detect the unusual relationship with the claim and the providers. That certainly helps with the claim volumes. And the second one is, can automate the alert system, some kind of a suspicious claims on the very early on, on the claim adjudication cycle before even it goes for a full adjudication routine. So that oftentimes reduce the system load and more of a detection at the very early on. So overall fraud detection accuracy can improve as much as. 60 percentage with AI compared to traditional rule-based system. So now moving on the implementation roadmap. So if you as a healthcare insurer wants to implement AI in your daily claim adjudication. Workflow, how do you do that? So there are multi-step process. So the first one is definitely going to be the assessment, understanding your current workflows, identifying the highest impact area for automation, because that's gonna be your main focus. And then from there on, you pilot you pilot your assessment into a pilot program targeting few set of claims. See how your AI model results and validate those results. And if your validation comes through positive, you can expand this to a large number of claims. So basically you scale the solution across more claims categories. And finally, once that is. Stern, you can integrate everything into your core architecture for seamless operation. That's that way you can eventually get the benefits of the AI implementation in your claims stack. Now what's next? Now the next is very in terms of AI is very bright. A lot of industries are definitely implementing ai, healthcare of time lags because we have to comply with lot of regulation, compliance and government and stuff like that. But it has many things that we can certainly incorporate. The things that it can go is towards the self-learning AI models, meaning they just don't follow the rules, but they also continuously adapt. I. To new types of claims and improve themselves. The timeframe might vary, but I am looking to see next three to five years, we will have like tremendous amount of growth on that area where when the AI models. Are more matured handling privacy handling healthcare data. Then we can also include the blockchain validation that is also gaining a lot of traction because it's transparency and immutability. So you can basically verify on a open ledger and potentially reducing the disputes and enabling the smart contract based adjudication routine, and and the last one we will also see a lot of these probably is not already happening is that AI and the and the human kind of an hybrid system where AI handles all the bulk of decision ma making map decision making, that's a more rule-based repetitive. And then you have your human experts that focuses on the more complex logic. Now the challenges and the consideration that we need to look, even though we are so excited about having AI into our workflow, not just healthcare for any industry, every industry specifically healthcare comes with a lot of challenges. The one that I have already mentioned. There are many entities in healthcare industry and everybody follows their own data, streamline various data structures. So data quality is one of the biggest that it has to overcome. So AI always work with some kind of a input data that is clean, consistent, and standardized. So if it is. Not that, then it'll definitely has lot of room to improvement. So we have to focus on that, and that's, right now it's a challenge. Second one is going to be regulatory compliance. That rightly because healthcare is a very. Privacy focused sector where information about the member and the provider has to be kept. Private and regulatory involvement from the government is very important. So we cannot just let any ai to to handle those data without any following rules and guidelines. So that's very important. And we also. Have to be careful when we implement the staff training and change management. AI changes, oftentimes job roles, and that transition must be done very thoughtfully. And finally, the ethical implementation is very critical because algorithms must be transparent. Explainable and free from bias. Bias is very important because it oftentimes can take any decision that may go wrong, specifically when it comes to the healthcare system. Now comes to the ROI and the business case. Now, at this point of time, it's quite evident that eventually every business will implement some sort of an ai. Healthcare is not prone to that, not immune to that implementation. But at the same time, there is a clear case of of good ROI like the business case is very strong. Most organizations see ROI within. 12 to 18 months when it is implemented fully, and productivity also increases. There's a cost saving there, there's a reduction in error. And when we give enough time from the full implementation till three years, fast forward, as you can see on my screen, the maximum potential is achieved. And that's where. Things started to get compound when we scaled the solution in a large scale. When you process millions of claims on a regular basis and you start to see your ROI and other metrics. Now moving on, what's the next step and the resources now, if you are considering, in stepping into this journey where you wanted to plug in different AI stack into your claim adjudication workflow, the first step is, would be to have some kind of an implementation guide or some sort of a playbook in your roadmap. Any possible schedule a consultation to assist readiness to some experts who are who are expert in the field or who have already done it, and if not, attend some kind of a workshop training session to upscale your team and then possibly. Leverage some kind of an ROI tool that can give you a prediction of how much savings you can do if you implement some kind of an potential some kind of an AI stack into your workflow. But no matter what, the transformation doesn't happen overnight. But with the right strategy, it can absolutely be achieved. So keep that in mind. Whenever you implement this going forward in any of your health plan. Now, with that, I would conclude my talk and I would like to thank you. So thank you for tuning in. This is give valuable insight about how AI izing healthcare adjudication system and if you'd like to explore more further, feel free to contact me. I would love to continue have this conver conversation. Thank you so much.