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Generative AI & customer expectations in the healthcare industry

Mallesh Kalary,

Head of Healthcare, Life Sciences
and Insurance 

Published: April 22, 2024

Generative AI. We’ve all seen the term plastered across headlines across the world, promising to revolutionize everything from marketing to medicine by improving efficiency, reducing costs, and fueling innovation. We've seen its creative spark ignite the content creation industry, churning out everything from catchy social media posts to mesmerizing videos and photographs to realistic product descriptions. And the excitement for what it could potentially do across industries is at a fever pitch. 

The COVID-19 pandemic brought the gaps that exist in the current healthcare ecosystem to the fore. Cost pressures, labor shortages, inflation, and the lingering effects of the pandemic are challenges that affect affordability and access to care for consumers, as well as risks to profitability for payers. Will generative AI be the catalyst that reshapes the industry?

In this blog, we discuss the current state of the healthcare industry and the role that generative AI could potentially play in its transformation.

But first, let’s take a step back

And look at the challenges that the healthcare industry currently faces. Rising costs, changing expectations of employee and member experience, providers’ frustrations with long and complicated claims processing systems, labor shortages, and fraudulent activity are just some of them that come to mind immediately. Addressing them requires a fresh perspective – in fact, McKinsey and Company believe that accelerating innovation in care delivery, improving productivity, and driving organizational growth could create value of over $1 trillion for the industry as a whole.

This is where we see the opportunity to harness the power of generative AI. That is not to say that the industry has not used artificial intelligence in the past – they have, but their applications have been limited to specific functions, limited by data inputs and the tasks that it was algorithmically designed for. Think chatbots and virtual assistants that address rudimentary inquiries, automation of administrative tasks, handwriting recognition in paper forms, and the like.

So, how is generative AI different?

Generative AI is different from traditional AI models in that it is more responsive and more adaptable. Even better, it can understand natural language and generate realistic data. With a large enough data set, it can learn, predict patterns, and create new data and multimodal content, which can be delivered as images, text, or audio. 

For healthcare payers, specifically, generative AI may be most useful in situations that require sifting through massive amounts of documentation and data for specific information, summarizing reports and important information, automating certain processes such as specific facets of underwriting, spotting patterns to make predictions, and detecting fraudulent claims. Imagine AI-powered chatbots that answer member questions 24/7 or personalized health tips delivered via text message. Imagine having the ability to scour through mountains of information to respond to queries in a matter of seconds. Imagine being able to consolidate and summarize data from multiple sources to be able to predict needs and personalize offerings. Generative AI offers payers the ability to do it all and more. 

From our perspective, we see four major areas where generative AI will drive results for payers: 

  • AI-based search – From market research to searching through documents to find precise information about queries to managing knowledge and Q&A, generative AI empowers payers with a simpler and faster search experience using conversational language, consolidating data, and formulating precise responses to queries. 
  • Conversational AI – Self-service chat tools and chatbots, custom workflows, and advanced integrations – conversational AI uses natural language processing to understand human language and naturally interact with humans. It can be used to automate tasks that are done by humans, reducing costs and margin for error, and improving productivity and operational efficiency.
  • Enterprise automation – Leveraging generative AI, enterprise automation tailors experiences across various functions like content creation and customer service by analyzing data to offer personalized recommendations and automate report generation, creating report content and generating letters, freeing healthcare professionals for strategic initiatives, and improving payer-provider interactions.
  • Call center modernization – Harnessing generative AI, call center modernization transforms payer contact centers by facilitating faster resolutions through unstructured inquiry analysis, proactive support via predictive data analysis, and expedited service using AI-driven chatbots, resulting in cost savings and enhanced member satisfaction.

The art of possible 

The impact of generative AI even extends to drug discovery, medical device development, and care delivery. Consider a world where we can predict disease patterns and offer personalized offerings (payer and provider) that nip the disease in the bud in the earliest stages. Or having the ability to consolidate the findings of clinical trials across the globe to generate audit reports, with a fraction of the effort that would be needed to do it manually, thereby accelerating the discovery process. Could we empower caregivers with all the vital information about a patient’s health, summarized and presented for a clear understanding of the case, without having to go through scores of reports, notes, and scans? Or track patient recovery post-surgery, in real-time, and act proactively to prevent complications? Can we create a healthcare system that operates seamlessly for all stakeholders – providers, payers, and patients? With generative AI, it’s not out of the realm of possibility to do so. Automating routine and repetitive tasks, streamlining operations, reducing the margin for error as well as costs, and improving experiences for everyone in the ecosystem. All of this translates to more time dedicated to what truly matters: delivering quality care that is accessible and affordable.

Okay, but what does this have to do with Virtusa?

Virtusa's expertise extends beyond engineering. We're a major player in healthcare and life sciences, innovating the tools that will shape tomorrow's healthcare landscape. Generative AI is exciting, but we know (being engineers at heart) that real-world use needs a deep understanding of healthcare's complexities.

That's why we did four things:

  • Talked to hundreds of healthcare clients: We listened to their needs to become a trusted partner for leveraging generative AI.
  • Built a skilled workforce: We trained leaders and held developer hackathons to create a culture of exploration with generative AI.
  • Honed our expertise in the technology: Through our Center of Excellence, we test ideas, build prototypes, and analyze results to ensure our solutions deliver real value.
  • Focused on continuous learning: Through our generative AI guild, we encourage any employee who wants to learn and experiment with this exciting new technology.

Over the last year, we have been part of some amazing pilot implementations across almost all of our customers as we rapidly innovate together. We have built chatbots to help field engineers with medical equipment, summarized clinical trials and reduced their cost, automated workflows using unstructured data, and essentially did things that were unimaginable a year and a half ago.

Through these efforts, we’ve seen first-hand the power of generative AI across diverse areas. We are excited to unleash this powerful technology and see what it can do for our clients in healthcare.


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