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Last year, 56% of insurance companies planned to increase their staff within the next 12 months, per an Insurance Information Institute report.
The Great Resignation combined with an already massive industry talent shortage put strain on all lines of personal and commercial insurance.
Per Kate Riordan, director, MSP initiatives, with Verisk, this year isn’t shaping up to be any different: “The insurance industry is seeing a 35% to 50% turnover rate right now as part of The Great Resignation,” she said.
“That’s a significant amount of people leaving that are not easy to replace.”
Across the U.S., voluntary employee turnover costs businesses a trillion dollars each year, according to Gallup data. Hiring a replacement employee costs, on average, 33% of their annual salary.
In the insurance industry these costs trickle through other parts of the business. A lack of experienced adjusters can drive up the cost of individual claims in addition to the costs of hiring new talent. This is especially true of workers’ comp and bodily injury lines which rely on early identification of potentially problematic claims to keep costs from spiraling out of control.
“Claims handling is an intricate process and it is not something that is quick or easy for many claims handlers,” Riordan said.
“Your veteran adjusters and veteran nurse case managers are going to be the ones that have seen a wide variety of claims and they have a better understanding of what to look for, what could be a complexity trigger.”
With staffing shortages and a lack of experienced adjusters, it’s no wonder insurers are turning to artificial intelligence to immediately identify relevant medical data and subsequently any red flags in workers’ comp and bodily injury claims early on in the process.
For insurance companies running on profit margins as low as two to three percent, the added productivity and cost savings brought on by machine learning algorithms can be a major boost.
“It’s enabling companies to stay competitive and profitable,” Riordan said. “There’s a strong appetite to be able to give staff the ability to expedite their work. You can automate this administrative component and give these experts more time to do what they need to do with the data to help injured people.”
When it comes to closing workers’ compensation and bodily injury claims, medical data plays a key role. Medical records let case managers know whether the injured parties have comorbidities that could delay recovery or if they aren’t keeping up with prescribed treatments.
“Every injury is different. People may have comorbidities that affect their ability to recover or there may be factors that prevent them from keeping up with their treatments,” Riordan said.
Kate Riordan, director, MSP initiatives, Verisk
“In order to be able to successfully and accurately adjust the claim and achieve settlement, adjusters, nurse case managers and lawyers need to fully understand the larger medical picture of the individual.”
Analyzing an injured worker’s medical data can be challenging, however. Adjusters and nurse case managers received hundreds of pages of medical records for a single claim that they have to carefully read and analyze.
“There’s a host of different methods within which medical records can be provided in,” Riordan said. “They can be structured data in terms of electronic health records or unstructured data written on doctor’s letterhead, which looks different across the board from one physician provider to another.”
That’s where AI comes in. Tools like Verisk’s Discovery Navigator uses automation and artificial intelligence to read and analyze claims files and flag any issues for the adjusters.
The AI system uses various medical libraries of terms created and validated by clinical experts. The results may indicate that a particular case could have risk challenges. It can identify odd treatment patterns and unapproved providers, procedures or treatment plans. It searches the document for the terms and highlights the relevant results for claims professionals.
The tool is unique in its ability to search unstructured data like medical records for potential issues. Unlike structured data, unstructured records, including handwritten medical notes can’t be easily organized into a standardized format, making it hard for many predictive models to use it. Discovery Navigator searches these types of records and sorts the unstructured data into a usable format for adjusters.
“There are a lot of tools in the market that can process structured data, but the unstructured data is a challenge,” Riordan said. “Discovery Navigator was specifically built to target the unstructured data.”
Riordan says that digital medical and claims file processing tools are more accurate than relying on a human to read thousands of pages of material and flag any issues. Consider how your eyes can glaze over after reading on a screen for some time. A machine doesn’t have that limitation.
“It’s very easy to overlook something after page 600,” she said. “If a claims handler misses a few things, that can cost the company a large amount of money in a settlement,” she said.
With claims only growing more complicated and a shortage of experienced adjusters, Riordan believes AI tools will be critical when it comes to saving insurance companies time and money. Yet despite these advantages, Riordan believes that almost 80% of insurance companies are still conducting record reviews manually.
“These records are getting more voluminous year over year,” Riordan said. “With this tool, individual claims professionals can decide what they need to do with the data as opposed to spending an enormous amount of time simply finding it.”
She points to how long adjusters spend just reading medical records and claims data as one key way artificial intelligence can increase productivity.
With fewer adjusters and case managers on hand to read files, a single individual may be taking on more and more pages of reading, making it difficult to focus on other parts of their job. A mountain of reading with unrealized potential to be automated may also cause experienced claims adjusters to start looking for employers with improved technology. During the Great Resignation, employees tend to seek employers who are using tech to allow them to focus on purpose-driven work.
“It takes a human roughly two minutes to read a single page. These packages can range from a hundred pages up to a thousand plus pages,” Riordan said. “Discovery Navigator is processing a hundred pages in minutes.” &
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