October 6, 2024

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How AI Is Transforming Healthcare Teams

How AI Is Transforming Healthcare Teams

Amit Garg is CEO of Hilabs, a company that cleans dirty data to unlock its hidden potential for healthcare transformation.

Conversations about AI are everywhere—whether it’s in the news, research or boardrooms. It is discussed by coworkers around the watercooler (and in Teams chats), by friends at parties and by family at gatherings.

As an AI technologist, I am genuinely fascinated by these discussions, and I hear people express a wide range of opinions and emotions. There is excitement, curiosity and healthy skepticism. However, increasingly I am hearing something far more jarring—fear. This fear isn’t of AI bringing about a doomsday future. It is far more personal, raising questions like, “What is going to happen to me? What will my role be in the AI-driven future?”

Since generative AI has made this technology more accessible, there has been a huge appetite for novel AI-driven solutions. In the field I work in, healthcare, potential solutions range from developing new drugs to enhancing personalized medicine. The underlying technology for these use cases is rapidly evolving, but there are still limited examples of it being used in the real world. Understanding how the nature of human work will evolve is still a mystery that healthcare and other industries are anxious to solve.

Searching For The Right AI Use Case

In the discussions about future AI use cases, there is a domain where AI has already been implemented that is frequently overlooked—the automation of administrative tasks. The technology for these applications is mature, and teams can quickly measure success and recognize ROI.

In healthcare, such solutions are not directly involved in patient care, so the risk is also relatively low. Yet, low risk does not mean low impact. These use cases have dramatically transformed respective business operations. As a result, AI is unlocking vast amounts of human capital, not replacing it, while allowing people to focus on higher-order strategic work. An excellent example of this, and one that helps patients, is ensuring health plans have accurate data on the providers in their network (known as provider data management).

AI Solving Problems For Patients Today

When patients go to their health plan’s website to find a doctor, the information listed is frequently wrong. A HiLabs study published in JAMA examined health plan provider directory entries for over 40% of U.S. physicians across five large national health plans and found inconsistencies in 81% of physician entries examined.

This creates a cascade of issues that can affect access to care, lead to surprise billing and mislead consumers when selecting health plans. Bad provider data trickles down even further to impact claims adjudication, quality measurement and meeting regulatory requirements.

Provider data inaccuracies stem from a large volume of data that changes constantly and is challenged by data to exist in various formats from multiple sources. The complexity of managing this data has required extensive manual interventions from seasoned subject matter experts.

Through an ensemble of AI methodologies, an AI engine can mimic these experts. For example, natural language processing (NLP) allows AI to read and understand human language. In provider data management specifically, this can allow AI to accomplish tasks such as reading provider websites and determining if they are indeed accepting new patients.

Another method is symbolic reasoning. This refers to creating a knowledge base of rules and axioms a seasoned analyst would know to consider. An example would be when data inconsistencies or gaps indicate that a provider has likely retired. Additionally, pattern mining, which looks at thousands of data points at once and creates weights for their reliability, can be applied to an AI algorithm. In provider data, this translates to knowing the location at which a provider is actually practicing. Together, these capabilities can operate like a human subject matter expert, except more quickly, more accurately and at scale.

At first glance, this is exciting, and the impact this can have is profound. But as you sit with it more, the fear may start to creep in. What will happen to these highly trained human experts? We can now see how this is playing out.

The Impact Of AI And Human Capital Transformation

Rather than replacing humans, my firsthand experience has seen teams at health plans engaging in higher-level strategy and innovation. Team members have the capacity to conduct research and development to further their provider relations strategy. They deal with data escalations more swiftly and can proactively watch and respond to market trends.

The unlocking of this human capital is extending beyond health plans to their providers. Rapid direct communication and feedback to providers on data quality can lead to less administrative work for those providers and cleaner data being exchanged throughout the ecosystem overall.

Putting It All Together

While artificial intelligence begins to take on tasks previously done by humans, it opens up the capacity for the transformation of human capital. Seeing what has happened with the use of AI in the healthcare industry for provider data is an exciting example of what’s possible: Tedious tasks and data errors are being reduced while humans are engaging in more impactful work.

This shift will bring about new challenges. We will have to think strategically in a way we haven’t before. We will need to be thoughtful about how we now spend our time. But I believe these changes shouldn’t frighten us because AI ultimately won’t replace us—it will help us become better versions of ourselves.


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