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The Rapid Growth of APPs and Burgeoning Risk for MPL

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Federal Administrative Actions Impact MPL

While medical liability-related legislative activity has shifted heavily from the federal environment to the states, the same cannot be said for all regulatory activity. Thanks to the McCarran-Ferguson Act, states remain the dominant focus of regulatory matters affecting medical liability insurance.

The State of the MPL Market: Claim Severity Rises, Policy Price Increases Moderate

Every six months, the MPL Association’s Research and Analytics Department issues a report analyzing these metrics with valuable take-aways that offer industry stakeholders insights into the industry’s financial performance.  

 


 

 

COVER STORY

Generative AI Offers the Potential to Upend the Customer Journey By Streamlining Claims, Underwriting, Data Analysis, and More


By Amy Buttell


While technology and effective leadership has succeeded in reducing the pain points around business processes in medical professional liability (MPL) insurance, the industry still presents an enticing target for further improvements. Generative AI is the most recent in a series of technological innovations designed to reimagine the customer journey, revolutionize day-to-day business in the industry, eliminate pain points, remove manual work, and introduce efficiencies designed to cut costs and speed up a wide variety of processes.

As a conservative subset of the traditional property and casualty insurance industry, the MPL sector is cautious in regard to the adoption of generative AI. This caution isn’t entirely unwarranted, as new and revolutionary technologies tend to over-promise and under-deliver while taking far longer than originally forecast to achieve material improvements.

That being said, generative AI is a true game-changer because of its interactive nature. Contrast the journey of one of your customers today with what it could be in a future generative AI-empowered universe. Today, your customers, who are healthcare professionals and organizations, are either seeking information about MPL insurance, looking to obtain or renew a policy, filing a claim, or checking up on the status of a claim.

In today’s world, that means getting on the phone, mobile app, or website and navigating through a variety of prompts or screens to find the information they want and acting upon it. In tomorrow’s generative AI world, all they will need to do is answer a question in response to an interactive generative AI assistant such as: “What do you want to do today?” No other prompts, drop-down menus or screens will be needed, because that interactive generative AI assistant will completely customize their journey based on where the customer wants to go and what they need.

Instead of calling your toll-free number or a broker or filling out a form on your website after a Google search, your customers can instead interact with their favorite generative AI assistant, which will then gather information in response to their unique queries across brands, websites, search engines, and more. In the most optimal future, from the interactive generative AI perspective, your customers could ask a generative AI assistant for help finding an MPL policy and lock that policy down within a short period of time, without having to move from their office or interact with a single person.

If you’re having a hard time imagining that, join the club. Many executives, entrepreneurs, managers, and employees can’t even picture how that would happen or what it would look like. But experts assure us this is the future. In this article, we’ll explain how this might work and explore some potential use cases for improving processes in the MPL insurance sector.

The Coming Operations Revolution

Generative AI has the potential to turn how organizations interact with customers upside down. Right now, those interactions are organizational-driven. In other words, your customers have to interact with you in prescribed ways, such as through website menus, customer-service phone prompts, or app screens. In the generative AI future, customers will drive these interactions through their quest for information that meets their needs. That means a whole-scale reinvention of your operations to align with the new ways customers will gather information.

 




An article in Harvard Business Review puts it this way: “Now with generative AI, personalization will go even further, tailoring all aspects of digital interaction to how the customer wants it to flow, not how product designers envision cramming in more menus and features. And then as the software follows the customer, it will go to places that range beyond the tight boundaries of a brand’s product. It will need to offer solutions it thinks the customer wants to do.”

“Solve the full package of what someone needs, and help them through their full journey to get there, even if it means linking to outside partners, rethinking the definition of one’s offering, and developing the underlying data and tech architecture to connect everything involved in the solution,” the article continued.

The same will be true inside organizations. Instead of the silos that we now have, each employee will have an AI assistant. Accenture puts it this way: “Imagine every employee in your company had an assistant that ‘knew’ everything your organization had ever known—the entire history, context, nuance, and intent of the business and its operations—and could process, analyze, and use that information in a matter of seconds, in infinitely repeatable ways.”

Such a system will lead companies to “reinvent the way work is done. Every role in every enterprise has the potential to be reinvented, as humans working with AI co-pilots becomes the norm, dramatically amplifying what people can achieve,” the article stated.

This is the big, big picture, which isn’t likely to happen tomorrow. But it’s coming, and just as search engines, email, software, and the internet destroyed phone books, printed newspapers and magazines, snail mail, and more, generative AI is likely to destroy—or at least drastically reshape—the traditional search engine and the “let your fingers do the walking” approaches we currently use. That doesn’t mean that your job or your organization is necessarily in danger. What it means is that, just as in the late 1990s and early 2000s we adapted to websites and search engines over phone books, paper catalogues, printed newspapers, and magazines, we will now have to adapt to this generative AI revolution.

Where does this revolution begin? With baby steps within individual areas of your organization. Let’s take a look at a few areas that generative AI can potentially affect in beneficial and cost-saving ways, sooner rather than later.

Streamlining Claims Management

Generative AI has the potential to eliminate manual processes and speed up claims processing. Instead of claims professionals spending hours going through MPL claim documents, a generative AI assistant can automatically extract specific information from claim documents, delivering it to a specific claims professional. Specifically, a generative AI assistant can “summarize all medical documents into claim notes, extracting keywords or answering questions” about documents involved in a specific claim.

Improving efficiencies within the claims lifecycle would eliminate pain points that stress customers and employees. Generative AI has the potential to consolidate claims data, create rule-based decisioning, and improve visualization technology, reducing the manual burden on employees and enabling swifter responses to claims stakeholders.

Generative AI chatbots, armed with detailed information about the claims process and specific claims, will be able to answer questions about benefits available for a claim, provide updates about the status of a claim, and create workflows and reminders around specific claims.

Risk Management and Underwriting

Today, MPL insurers suffer from data overload. That’s because, while there is a fire hose of data available to help with risk management and underwriting, getting the right information to the right person to make an actionable decision is incredibly challenging. Generative AI holds the potential to deliver data directly to decision makers swiftly and easily.

Larry Van Horn, founder and CEO of Preverity, told Inside Medical Liability Online that the organization is experimenting with proprietary generative AI technology designed to drastically simplify their data interface. While this is a specific example from one data provider, it is likely that most, if not all, data providers will eventually have such capabilities.

“What we’re evaluating is whether any of our underwriters or carrier partners could simply interact with our analytic front end and all of our data assets by asking a question in a ChatGPT-like format, instead of going through lots of effort to get the data they need,” he said. “We’re working on trying to structure our data to answer a question in such a way that generative AI is on the front end, so our partners don’t have to download data.”

By eliminating the barrier to accessing data, such as writing code or exporting tables and information, you’ll potentially be able to ask the simplest of questions, Van Horn continued. If you wanted to know the risk profile of one particular provider as part of the underwriting process, you could ask the generative AI, which would answer your questions with easy-to-understand data points. “This would be very much in the context of what we’re already doing, just making it more accessible so that you don’t have to be a data scientist to figure out what you need to know and how to find it.”

Other Use Cases

There are many more potential areas where generative AI can positively impact MPL insurer operations, including:

  • Claims Reserving: By analysing historical data and considering a wide variety of risk factors, generative AI can support claims reserving decisions. This functionality could help insurers manage their financial resources more efficiently and effectively.
  • Quote and Bind: Generative AI can validate data accuracy, automate repetitive tasks, and identify and extract relevant information from documents, removing manual processes and streamlining timelines.
  • Regulatory Compliance: A constantly updated, proprietarily trained generative AI can keep your organization updated on applicable insurance regulations across states, territories, and countries. The right generative AI assistant could also interpret regulations and recommend adjustments while reducing the amount of time and effort involved in meeting compliance requirements.
  • Pricing: Leveraging generative AI changes to underwriting and risk management will allow for more accuracy in pricing. MPL insurers can potentially move from pricing based on specialities to dig deeply into the actual procedures performed by clinicians and their specific histories to make more informed pricing decisions.

Ultimately, there’s no crystal ball that will tell us exactly where and how generative AI will be the most impactful in terms of MPL insurance. But what we do know is that if the industry as it stands doesn’t take advantage of this technology, there’s the potential for other organizations from the outside to use it to disrupt business as usual.


 


Amy Buttell is Editor of Inside Medical Liability Online.

Generative AI has the potential to turn how organizations interact with customers upside down.

Right now, those interactions are organizational-driven. In the generative AI future, customers will drive these interactions through their quest for information that meets their needs.

That means a whole-scale reinvention of your operations to align with the new ways customers will gather information.