Ashish parmar

Ashish parmar

1614164356

How do ML and AI help you settle medical claim insurance faster?

‘AI is a vital part of the fourth industrial revolution and will impact every aspect of people’s life’- Fei Fei Li, Professor at Stanford University.

Artificial Intelligence has been one of the most reformative technological inventions in the history of humankind as it has made information systems more adaptive for humans. Over the years, AI has played a definitive role in redefining different industry sectors & sections and the insurance sector in this context has a similar story as well.

We need no superlative analysis to understand the importance of medical insurance, especially in the present turbulent times of the global pandemic. Artificial Intelligence (AI) and Machine Learning (ML) have proved to be a vital cog that has streamlined end-to-end claim settlement, thereby increasing customer satisfaction. The mediclaim management process can now be done faster and better with fewer or no errors.

Gartner, in 2016, predicted that almost 1/3rd of the companies on a global level would use AI in at least one aspect of their sales process by 2020. ML and AI have left no stone unturned and brought a paradigm shift in different industries such as healthcare, aviation, ecommerce, and the insurance sector.

A brief understanding of mediclaim settlement
In the realm of the insurance sector, mediclaim settlement means an agreement form between two different parties which allows the smooth handling of the health insurance disputes. The process of mediclaim settlement begins when a policyholder asks his/her insurer to avail the medical services covered in the policy. With the benefits mentioned in the policy, the policyholder can either get a cashless treatment or get a reimbursement for availed health services.

In mediclaim settlement, the insurance company then reviews the claim. The insurance company matches the claim with their record in the claim management software to see if the claim is genuine or malicious. Moreover, there have been cases where the mediclaim insurance companies have instigated investigative operations to find the genuineness of the claim done by the policyholder. AI has been a breath of fresh air for the healthcare insurers as it allows them to manage claim settlements better. Here is how it is done:

AI can predict the pattern of the claim volume so that the insurers can make themselves prepared.
Through in-depth data analytics, insurers can automate the process of fraud detection.
It helps them to pre-assess the claims while simultaneously automating the damage evaluation process.
After understanding the changes in brief that Artificial Intelligence (AI) and Machine Learning (ML) has brought in the insurance sector, we now dig deep to see the role these two techniques play.
Role of AI and ML in the health insurance sector
The expansion of AI and ML in different industry sectors, especially in the last decade or so has opened new avenues for the insurance industry and has allowed the insurers to provide a better customer experience. The insurance sector has been marred by three major issues that are proving to be a hurdle for the insurance personnel to deliver a quality customer experience. They are:

Not reaching to their targeted customers at the right time
Not providing right products according to the requirement of the customer
Not giving on-time claim to their customers and failing to find the spurious claims
Related Article: 5 Best Ways Intranet Software Helps Organisations Work Easily
Artificial Intelligence can streamline the redundant processes that creep-in into the medical insurance system and create confusion among the employees. In a typical mediclaim insurance firm, the team consists of agents, brokers, claim investigators, etc. A technologically advanced system like healthcare insurance software consisting of AI and ML will help keep the work in order and increase the efficiency of the employees.
AI-powered solutions help in simplifying the kerfuffle and enable the mediclaim insurers provide more value propositions to the customers

AI can be the catalyst for faster healthcare insurance claims
healthcare insurance claims

(Image source: https://cdn-images-1.medium.com/)

A few unique steps in the AI-enabled insurance work process would help the customers complete the mediclaim settlement work swiftly.

Analysis of the information provided
The below mentioned information/data is first drawn out from the medical document to kick-start the process of healthcare insurance claim settlement through AI

Necessary information like the diagnosis report of the customer, severity and type of disease, etc. is first extracted from the document in a textual format.
Following this, information about CPT (Current Procedural Terminology) codes- the service or procedures performed on the patients are also extracted.
The importance of both the systems mentioned above is that the first one is necessary to process the information and the second one looks at the authenticity of the information.
Fraud Analyzer
Insurance companies lose a significant amount of money to fraudulent claims; nevertheless, such a problem can be rectified with cognitive Artificial Intelligence technology.
When an AI-powered system analyzes several symptoms and diagnoses, it can come up with a tentative treatment for the disease. This treatment will enable the insurers to gauge a tentative cost of the disease, based on different factors like the severity of the disease, location of the hospital, etc.
So when a customer files for an insurance claim, the insurance provider can refer to their data and save themselves from future mishaps.

Processing the medical invoices
With the help of AI, the medical invoices can be automated, ruling out the chances of human intervention. It is a four-step process as mentioned below.

Bounding boxes around the medical invoice text
The boxes then go through a scene text decoder which uses a sequence neural network.
LDC (Levenshtein Distance Correction) is applied to the localized boxes for better accuracy
Each line item is then put in a specific insurer category.

Continue reading: https://toptechpublisher.com/how-do-ml-and-ai-help-you-settle-medical-claim-insurance-faster/

#machine #artificial-intelligence #machine-learning #insurance-industry

What is GEEK

Buddha Community

How do ML and AI help you settle medical claim insurance faster?
Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Ashish parmar

Ashish parmar

1614164356

How do ML and AI help you settle medical claim insurance faster?

‘AI is a vital part of the fourth industrial revolution and will impact every aspect of people’s life’- Fei Fei Li, Professor at Stanford University.

Artificial Intelligence has been one of the most reformative technological inventions in the history of humankind as it has made information systems more adaptive for humans. Over the years, AI has played a definitive role in redefining different industry sectors & sections and the insurance sector in this context has a similar story as well.

We need no superlative analysis to understand the importance of medical insurance, especially in the present turbulent times of the global pandemic. Artificial Intelligence (AI) and Machine Learning (ML) have proved to be a vital cog that has streamlined end-to-end claim settlement, thereby increasing customer satisfaction. The mediclaim management process can now be done faster and better with fewer or no errors.

Gartner, in 2016, predicted that almost 1/3rd of the companies on a global level would use AI in at least one aspect of their sales process by 2020. ML and AI have left no stone unturned and brought a paradigm shift in different industries such as healthcare, aviation, ecommerce, and the insurance sector.

A brief understanding of mediclaim settlement
In the realm of the insurance sector, mediclaim settlement means an agreement form between two different parties which allows the smooth handling of the health insurance disputes. The process of mediclaim settlement begins when a policyholder asks his/her insurer to avail the medical services covered in the policy. With the benefits mentioned in the policy, the policyholder can either get a cashless treatment or get a reimbursement for availed health services.

In mediclaim settlement, the insurance company then reviews the claim. The insurance company matches the claim with their record in the claim management software to see if the claim is genuine or malicious. Moreover, there have been cases where the mediclaim insurance companies have instigated investigative operations to find the genuineness of the claim done by the policyholder. AI has been a breath of fresh air for the healthcare insurers as it allows them to manage claim settlements better. Here is how it is done:

AI can predict the pattern of the claim volume so that the insurers can make themselves prepared.
Through in-depth data analytics, insurers can automate the process of fraud detection.
It helps them to pre-assess the claims while simultaneously automating the damage evaluation process.
After understanding the changes in brief that Artificial Intelligence (AI) and Machine Learning (ML) has brought in the insurance sector, we now dig deep to see the role these two techniques play.
Role of AI and ML in the health insurance sector
The expansion of AI and ML in different industry sectors, especially in the last decade or so has opened new avenues for the insurance industry and has allowed the insurers to provide a better customer experience. The insurance sector has been marred by three major issues that are proving to be a hurdle for the insurance personnel to deliver a quality customer experience. They are:

Not reaching to their targeted customers at the right time
Not providing right products according to the requirement of the customer
Not giving on-time claim to their customers and failing to find the spurious claims
Related Article: 5 Best Ways Intranet Software Helps Organisations Work Easily
Artificial Intelligence can streamline the redundant processes that creep-in into the medical insurance system and create confusion among the employees. In a typical mediclaim insurance firm, the team consists of agents, brokers, claim investigators, etc. A technologically advanced system like healthcare insurance software consisting of AI and ML will help keep the work in order and increase the efficiency of the employees.
AI-powered solutions help in simplifying the kerfuffle and enable the mediclaim insurers provide more value propositions to the customers

AI can be the catalyst for faster healthcare insurance claims
healthcare insurance claims

(Image source: https://cdn-images-1.medium.com/)

A few unique steps in the AI-enabled insurance work process would help the customers complete the mediclaim settlement work swiftly.

Analysis of the information provided
The below mentioned information/data is first drawn out from the medical document to kick-start the process of healthcare insurance claim settlement through AI

Necessary information like the diagnosis report of the customer, severity and type of disease, etc. is first extracted from the document in a textual format.
Following this, information about CPT (Current Procedural Terminology) codes- the service or procedures performed on the patients are also extracted.
The importance of both the systems mentioned above is that the first one is necessary to process the information and the second one looks at the authenticity of the information.
Fraud Analyzer
Insurance companies lose a significant amount of money to fraudulent claims; nevertheless, such a problem can be rectified with cognitive Artificial Intelligence technology.
When an AI-powered system analyzes several symptoms and diagnoses, it can come up with a tentative treatment for the disease. This treatment will enable the insurers to gauge a tentative cost of the disease, based on different factors like the severity of the disease, location of the hospital, etc.
So when a customer files for an insurance claim, the insurance provider can refer to their data and save themselves from future mishaps.

Processing the medical invoices
With the help of AI, the medical invoices can be automated, ruling out the chances of human intervention. It is a four-step process as mentioned below.

Bounding boxes around the medical invoice text
The boxes then go through a scene text decoder which uses a sequence neural network.
LDC (Levenshtein Distance Correction) is applied to the localized boxes for better accuracy
Each line item is then put in a specific insurer category.

Continue reading: https://toptechpublisher.com/how-do-ml-and-ai-help-you-settle-medical-claim-insurance-faster/

#machine #artificial-intelligence #machine-learning #insurance-industry

George  Koelpin

George Koelpin

1602270000

AI: Right Structure of Agents For your Business

In this post let us talk about the types of agents and challenges of data set for the agents.

All agents have the same skeletal structure. They get percepts as inputs from the sensors and the actions are performed through the actuators. Now the agent can either just act on a percept as a reflex for example if you throw a ball at me and I try to catch it (or duck from it given that I am bad at baseball) than that is a quick reaction to the percept. On the other hand if you throw a ball at me and tell me to arrange it by color or count the number different colors that you are throwing at me then I would have to maintain a state to do the counts correctly. So this would involve some state but is still ok. Now, if you want to trouble me further and you tell me to jump twice if you throw red ball at me and do a burpee if you throw a green ball at me apart from catching and counting then you got me for sure

This would involve a complex logic of me maintaining a mind table of what needs to be done on what percept and this is called** condition-action rule**. Now if all these percepts were to be indexed then this would become a significant data set.

Consider the automated taxi: the visual input from a single camera (eight cameras is typical) comes in at the rate of roughly 70 megabytes per second (30 frames per second, 1080 × 720 pixels with 24 bits of color information). This gives a lookup table with over 10 600,000,000,000 entries for an hour’s driving. Even the lookup table for chess—a tiny, well-behaved fragment of the real world—has (it turns out) at least 10 150 entries.

This can become a lot of information.

The key challenge for AI is to find out how to write programs that, to the extent possible, produce rational behavior from a smallish program rather than from a vast table.

So this brings us to 4 documented types of agent programs

#ai #ml # ai and data engineering #scala #ai and ml

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai