Benefit of AI in Worker Compensation: 3 Detailed Facts

Introduction to Benefit of AI in Worker Compensation

With an increase in self-driving cars and robots, many bet that Artificial Intelligence is bound to reduce workplace injuries, but amidst all, how does AI impact claims management? 

AI has spread its reign over multiple industries, and workers’ compensation is not an exception. Workers’ compensation has witnessed AI in the form of a clinical decision support engine that helps to guide claims professionals, along with innovative analytics programs that look at patient data and claim outcome information to identify risk and intervention opportunities.

How Is The Workers’ Compensation Industry Exploring The Use Of AI?

Due to the AI’s ability to spot the trends that human brains have previously failed to discover, leading to further efficiencies, at the same time, machine learning endeavors to take this to an upper level.

On the extremity, AI-based systems are actually capable and are replacing human employees of a Japan-based life insurance company.

This program is said to have trained to think like a human and analyze data before it suggests the payments. However, for now, the payments must be approved and reviewed by human employees.

Such news is a sign that AI can be used as a mode to automate more and more parts of claims workflow – assisting, but not taking over the human touch.

It is only time now that can tell to what extent AI will affect claims management; however, the market is embracing AI with open arms.

While it may take a few years or maybe a few decades to see AI used on a large scale in the workers’ compensation, there are still areas where AI is used in the compensation process and is changing it for good.

Benefit of AI in Worker Compensation
Benefit of AI in Worker Compensation

Here are the ways in which AI is changing workers’ compensation:

Minimizes Costs

AI and automation contribute to the reduction of cost in the workers’ compensation claims for a few reasons. The first one being – it helps to get to the bottom of what happened.

The traditional method of classifying claims is using the nature of the injury, source of accident, and what physical injuries the worker has suffered.

The codes present in the data may usually be outdated or incorrect. AI helps in determining this by sifting through the bills to analyze and identify the primary diagnosis for each claim, detect the cause/ comorbidities (a significant driver of costs), group the identified direct diagnosis code into diagnoses category on the basis of specific expenses and clinical judgment.

Secondly, it detects anomalies in the procedures and drug administrations. The methods of the utilization review relying on a fee schedule or practice guidelines do not alert the payer in case of an unusual process or drug for any particular type of claim.

Using big data and diagnosis grouping, you can now calculate the probability distribution of the procedures and medicines administered.

It is possible to identify the outliers with the right analytical methods, especially the outliers that do not have high costs when compared to the other claims with similar diagnosis and injury characteristics.

Lastly, AI helps in avoiding unnecessary work. It is unnecessary to increase the cost of minor claims (where the claims do not require a specialist to help like nurse case management) by using expensive cost containment procedures (that must be directed only where they will produce a financial or clinical benefit).

Machine learning/ decision tree helps in scoring the complexity of every individual claim and runs only the ones that benefit the medical experts.

Apart from this, AI, automation, and machine learning reduce costs by selecting the optimal provider for a claim, enabling early intervention in catastrophic claims, and preventing litigation.

AI can reduce more costs by integrating with the human resource management software of the organization by feeding on the employee information in the software.

Transforms Claim Process

There are numerous ways by which AI transforms the administrative process of the back office when it comes to claims.

Machine learning, for example, analyses the massive amount of text-based communication that is omnipresent in claims management.

AI reads the text at superhuman speeds and understands it, further identifying the essential information viz.

Invoice details, claim number, and the date of birth and address of the applicant. Depending on the invoice format, these details can be at different places. AI identifies them irrespective of the layout.

Having absorbed and analyzed the raw data, AI scans other data sources to resolve claims. Claims management fueled by AI provides recommendations on the next-step basis in complex cases where the straight-through processing is not possible.

At present, mostly expert examiners use their experiences to decide what to do next. There may be a vast difference in the next step chosen by a person with three years of experience and a person with 15 years of experience.

AI can assist expert examiners in accessing data from an array of similar cases and recommend the next step after taking the historical data into account to achieve more consistency in the processing claims.

Apart from that, the software can find insurance information of the person – whether he has the insurance for a particular peril.

It reduces the slightest chance of human error that occurs due to manual inputs, in turn making claims handling quicker and accurate.

AI and machine learning can help insurers to aggregate the claims information, allowing them to identify the trend like the common types of injuries or the average recovery costs for any particular damage.

Besides these functions, AI tracks customer responses every week. When the reactions start to go negative, it flags the insurance company, thereby triggering human intervention to ensure that the customer receives a more satisfactory experience.

With advanced data processing, the insurers have a more comprehensive overview of the claims as well as the customer’s risk profile to identify the outliers that may indicate fraud.

They use extremely segmented information to allow the creation of implied policies and prevent the programs for customer segments.

Demonstrates Value

One of the significant concerns, looking at the future of the workers’ compensation, is the trend for large companies of self-insuring their employees for workers’ compensation and outsourcing the claims process to TPAs.

Suppose the insurer wants to prevent or discourage the large companies from self-insuring their workers’ compensation programs.

In that case, they need to have the capability of showing that they can deliver much faster, more economical, efficient, and customized service. And that is where technology and automation come in.

Further visit: 7 Amazing Benefits of Artificial Intelligence in Banking

See also  10 Points You Must Know How AI Influences Advertisement Industry Quickly

In A Nutshell

Sorting, analyzing, and processing the workers’ compensation claims require a large number of manual hours, a vast majority of which are deficient in value.

It is quite evident from the growing demand for AI devices like Siri and Alexa that the world is opening its arms to adopting and accepting AI.

Organizations have installed AI and machine learning in numerous business operations and are continuing to do so every day.

When all your processes are automated and under the supervision of AI, why not apply it to workers’ compensation funds as well?

Author Bio:

Divyang Metaliya is a Business Consultant working with factoHR. Apart from managing and improving business processes, his hobby is to spread his vast area of knowledge to the people out there. He loves to create content that is innovative and engaging for the readers.

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