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Claim Adjudication Process In Healthcare Ppt – The insurance industry is notoriously bad at treating customers. But not in China. Over the past few years, China’s tech giants have made huge strides to become the center of insurance innovation. Just look at one example: WeSure, an insurance platform derived from the messaging app WeChat, celebrated more than 55 million users on its second anniversary. This means that the biggest challenge for Chinese insurers today is not just to digitize business, but to go beyond traditional offerings and even combine insurance with other financial services. In order to compete, insurance companies are revolutionizing the industry using AI, IoT and big data.
Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS) are the most important metrics for any insurance company. In the US, however, they have largely lagged behind as insurers have not kept up with expectations as other industries have picked up. Since the claims process has the biggest impact on customer satisfaction, let’s take a look at how technology can revolutionize costs, operations and customer experience.
Claim Adjudication Process In Healthcare Ppt
Why are insurance companies struggling with digitization and automation in the first place? Leaving aside the typical reasons, such as staff reluctance to change or lack of budget and technical resources, there is one big reason that stems from the nature of insurance — insurance processes are usually too variable and unstructured to be easily incorporated into a digital workflow. .
Where Does Your Medical Claim Go After It Leaves Your Provider’s Office? — Imagenet
For example, claims data exists in multiple formats (photos, handwritten documents, voice notes) and is transmitted through multiple channels (email, attachments, phone calls, chats), making it difficult to obtain and analyze with high accuracy without the personal attention of an agent. And when it comes to decision-making, it’s often more nuanced than an off-the-shelf system can handle—needing to understand the context of each individual case.
Does this mean that the insurance industry can never be automated and we need human input for every part of the process? Of course not. But this requires more advanced approaches that mimic human perception and judgment, such as artificial intelligence, machine learning, and ML-based robotic process automation.
“Claim automation is truly the Holy Grail of insurance. Essentially, it works based on the three most important metrics that insurance companies care about: retention, expenses and loss ratio. If we can solve common problems in claims through automation, then we can significantly improve all three metrics.”
Let’s see where and how automation helps improve the process. And we will start with the first point of contact between the insurer and the insured.
The Five Steps Of The Claim Adjudication Process
A first notice of loss, or FNOL, is the first notification to an insurance provider that an asset has been lost, stolen or damaged. This is a document detailing the incident and damages, following the client’s personal account of what happened.
Many insurance companies still receive FNOLs over the phone, and the call center operator often takes a long time to gather all the information from the policyholder, usually with numerous follow-up calls.
Today, electronic FNOLs are common, where instead of calling the insurer or handing over documents in person, a customer can use a chatbot or mobile app to fill out the required information, upload media files and scanned documents, allowing the insurer to process claims faster and with better accuracy.
If you use modern claims management software, ask your supplier which FNOL suction systems they integrate with. Some, such as Guidewire and Snapsheet, will have digital FNOL interfaces out of the box. If there’s nothing in their catalogs that suits you, you can choose a third-party FNOL digital intake system like Capgemini and Wipro and integrate their APIs with your own IT efforts.
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If you rely on a legacy system for your operations and don’t have the resources to fully modernize, you can connect to FNOL providers using a legacy EDI connection such as Netsmart or OneShield. Additionally, sometimes the cheapest and most efficient option will be to create your own FNOL consumption capabilities that best suit your workflow.
Digitized and structured FNOLs are integral to the success of business automation, and you will see this in the following chapters. But while eFNOLs provide a better customer experience, they may not be particularly useful for insurers who still need to follow their normal workflow of digitizing handwritten documents and photographic evidence, transcribing audio and video reports and liaising with by clients regarding missing information. Let’s look at ways to solve these problems.
For decades, organizations have used optical character recognition (OCR) to process physical documents, primarily converting handwritten and printed text into machine-coded text. While OCR is extremely accurate for typed text, OCR uses manually created templates and can make small mistakes in important information like someone’s name, date, or price, rendering the digitized copy useless. This means that files processed using traditional OCR must be reviewed manually, which is far from automated.
A modern alternative to OCR is Intelligent Document Processing (IDP), also called Cognitive Document Processing (CDP) or ML OCR. This AI-based technology uses natural language processing, computer vision, and deep learning to improve document quality, classify documents, and extract unstructured data that can then be transformed into usable structured data.
Real Time Adjudication For Health Insurance Claims
IDP is commonly used in robotic process automation, allowing many common business tasks to be automated using predefined workflows. For example, an RPA bot using IDP will be able to analyze documents sent by a customer, extract relevant data from text and media and send it for further processing, such as manual verification or fraud detection algorithms, all without human intervention.
Intelligent document processing usually works on top of RPA software. Read our article where we discuss RPA implementation options separately. The good thing about IDP systems is that they are not necessarily focused on the insurance business, and the choice of providers is wider. Depending on where you are, you have two implementation options.
Use an ML-based RPA system. Solutions like UiPath or Automation Anywhere have their own cognitive document processing capabilities and will require minimal effort on your part.
Implement a third-party IDP. Whether you’re looking to complement your existing RPA activity or upgrade the traditional optical character recognition you’re using, look to vendors like Appian, InData Labs, or insurance-focused Infrrd.
Insurance Adjudication Simulation Across Multiple Institutions And Levels Of Learners
Claim triage is the process of sorting a large number of claims by urgency. In the event of catastrophic events or simply during peak season when insurers are faced with a high volume of claims, they need to quickly and confidently determine which claims should be resolved first and by whom. This is where predictive analytics can come in handy.
Based on historical data, predictive analytics uses statistics and machine learning techniques to determine the probability of future events. Because carriers collect and store all accident data, they can apply predictive analytics to distinguish between easy claims that can be automatically accepted and complex claims that need to be adjusted accordingly.
You probably already use some kind of analytics. (Use our article to determine your analytics maturity level.) But predictive analytics requires a much larger investment than any of its previous iterations.
Learn about analytics technology. Get integrated data management tools, such as BI dashboards and data warehouses, that allow non-technical people in your company to access and make decisions using analytical data.
Claims Resume Samples
Develop a data-driven culture. People will only be able to use these tools when they are built into their existing workflows and reflect their needs. To do this, be sure to train your employees so that they know where and how to find analytical data.
Hire a team of machine learning experts. Hire data professionals to drive automation through data pipelines, modeling and technology integration.
However, FNOLs need not be the only source of instantaneous claims data. IoT devices are a valuable source of real-time data that can help speed up the claims management process.
Internet-connected products, such as water sensors, smoke alarms or car sensors, provide insurers with vast amounts of information and subsequently insights.
Automated Medical Billing In The Healthcare Industry
Homeowners in the U.S. file more than 14,000 water damage claims each day, with an average cost of up to $15,000. Not only can this harm be prevented by using sensor alerts, resulting in fewer claims, but the data from these sensors can be applied to predictive analytics models and help make decisions on such claims much faster.
Data from IoT devices can also automate FNOL submissions. Sensors can not only estimate the loss more accurately than the insured, but also do it instantly. In the case of car accidents, telematics data on speed, braking, direction of collision, etc. will describe in detail the actual development of events and speed up the delivery of judgments and detection of fraud.
The infrastructure is served by IoT platforms such as Cisco IoT, AWS IoT Platform or Microsoft Azure IoT. You can view our comparison to make your choice.
But you also have to be guided by the level of perception — which devices you will use and target. The easiest solution would be to partner with the device manufacturer and offer sensor installation as part of your service. See examples of HSB, Progressive and Hippo.
Redefining Claims Processing In A Connected World
In typical situations, the damage assessment is done manually in the workshop and/or by the adjuster
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