How Generative AI Can Reduce Inefficiencies & Improve Member Satisfaction

Like most industries, there is extensive conversation around generative AI in healthcare, driving both excitement and apprehension. According to McKinsey, this new capability could unlock part of the $1 trillion of improvement potential within the industry.

However, generative AI cannot offer value on its own. Rather, it must be applied to specific use cases that improve patient and provider experiences, reduce inefficiencies, and accelerate revenue. As such, healthcare payer executives must figure out how to integrate generative AI into existing technologies and roadmaps to increase user and business value.

How healthcare payers can avoid generative AI becoming a net negative

As members demand more personalization and convenience from health insurers, payers face competitive pressure and rising healthcare costs. These layered pressures are forcing many payers to consider creative solutions to manage risk and reduce organizational inefficiencies.

Generative AI is one potential solution to this challenge. By automatically and efficiently managing data, regardless of volume, the technology promises to free additional resources for more complex and human-centric needs. Its functionality for payers include:

• Sifting through logs and data and summarizing them for human decision-making
• Pulling relevant information on member plans to answer questions quickly
• Speeding resolution of claims denials and other processes that drive member dissatisfaction
• Streamlining health insurance prior authorization and claims processing

However, if payers limit generative AI to only resolving baseline customer facing issues that should be handled by humans, it will be a net negative. Rather, payers should focus on identifying areas where AI can leverage its unique capabilities to drive efficiencies and unlock the underlying causes of member friction to improve their digital experience.

This can only happen if generative AI is developed using secure, managed data that remains fully compliant with healthcare compliance standards. According to Laura Craft, VP Analyst at Gartner, “AI governance is necessary, especially for clinical applications of the technology. However, because new AI techniques are largely new territory for most [health delivery organizations], there is a lack of common rules, processes and guidelines for eager entrepreneurs to follow as they design their pilots.”

With proper governance, healthcare organizations can protect themselves from liabilities and ensure the technology adds positive user and business value.

How generative AI can improve member experiences with their payers

Clear alignment between value and functionality is key to driving value with generative AI. Let’s take a look at a number of ways the technology can improve member experiences and drive efficiencies across the payer organization.

Streamlining administrative workflows

Generative AI and automation can significantly accelerate and streamline rote administrative tasks facing many healthcare employees. This includes performing configurations at a much quicker and more efficient level than currently, processing data about 90% of the way, then using human review to complete the final 10%.

Improving member experiences

Member dissatisfaction can be costly, not only for the payer, but for all parties involved in that experience. 83% of patients report poor communication as the worst part of their experience. By leveraging technologies like natural language processing (NLP), predictive analytics, and speech recognition, healthcare payers can build digital experiences that enhance patient communications and drive positive sentiment.

Key to leveraging AI to improve member experiences is learning how to balance the technology with human input. If a member needs answers that only a human can provide, AI could prove to be a negative. It’s important to strategically balance the benefits of the technology and devoting resources to those tasks that only humans can perform.

Reducing billing and coding errors

With tens of billions of dollars lost due to coding errors, improving the coding process with generative AI can present a major value-add for payers, providers, and patients alike. For example, non-technical personnel can input data, and the generative AI translates it into the necessary coding syntax.

This would require a large enough data set across multiple types of customers to generate an effective solution. However, by automating and improving coding and billing practices, you can avoid erroneous claims denials, address objections and issues, and accelerate incoming revenue.

How to maximize the advantages of generative AI in healthcare

Now that we’ve discussed the benefits generative AI can offer payers, let’s look at how to maximize these advantages practically. Here’s the approach we use at 3Pillar.

Don’t adopt AI for its own sake

While generative AI is a trend and businesses across industries are rushing to implement it, not everyone is doing so in a way that drives user and business value. For healthcare organizations, the risk inherent in new technology, not to mention the resource drain in implementing value-less functionality, can cause problems.

Align features with user value

Think about the fastest path for generative AI to increase value among members. Can it retrieve information faster and create a more seamless experience for members? Can it accelerate claims adjudication and free up internal resources? Can it reduce input errors and back-and-forth with providers?

Don’t think about what AI can do in general, rather what it can do for you and your user and member base. When you focus on solving for need and accelerating value, you can leverage this technology in an ROI-positive way.

Take the time to get it right

Remember: generative AI is relatively new, and its capabilities are rapidly evolving and changing. Implementing it without considering the intricacies, challenges, and drawbacks will lead to risk exposure within your organization.

While there’s currently a rush to ship AI-driven capabilities, it’s important that your healthcare organization take the time to get it right. And with far fewer bugs and problems than a rush job, you won’t have to re-invest resources in fixing problems.

Partner with experienced technology practitioners

The key to success with generative AI is to work with someone who’s done this before. Not only should you find a technology partner with AI experience, but you need to work with someone who’s navigated all the technological advancements over the past several decades, and is familiar with hype cycles and extracting value from new functionalities.

3Pillar has a track record of success both inside and outside of healthcare, guiding companies not only through the strategic process, but tactically implementing new technologies to quickly drive user and business value. Learn more about 3Pillar’s healthcare expertise here.