Generative AI and analytics in Healthcare with Prashant Sarpamale

Generative AI and analytics in healthcare represent a powerful synergy, where generative AI's ability to create data and insights aligns with analytics to drive data-driven healthcare improvements, from personalized treatment plans to operational efficiency enhancements. In this week’s CDO insights we have with us Prashant Sarpamale, the CEO at Althea.ai. He is an executive with extensive experience in conceptualizing and leading Data & AI initiatives at scale within the Healthcare Payer and Provider segment. A results-focused leader with a strong focus on innovation and harnessing the full potential of teams. He talks about how healthcare's transformation through data-driven technologies and Generative AI, inquiring about the origins of these discussions and seeking examples of successful use cases. He also unwinds the differentiation between AI and Generative AI's productivity enhancements, considers a hypothetical impact on past Covid-19 conversations, and talks about recent observations and future adoption predictions in healthcare. AIM: How has the evolution of data-driven technologies in healthcare transformed the way data is structured and utilized, from unstructured data a decade ago to its widespread application in day-to-day healthcare practices, including by doctors and other healthcare professionals, and how does this relate to the emergence of generative AI in healthcare? Prashanth Sarpamale: Back in 2012 when I joined the healthcare industry armed with a lot of knowledge and data and analytics but with absolutely no knowledge in healthcare, it was a short introduction when looking at the state of maturity, from a healthcare perspective for analytics. It was primarily a B2B industry and not so much a B2C and that has repercussions in terms of how data is employed. It was a data rich industry and there were a lot of quantitative methods employed, but primarily in a very structured fashion. You can think about a lot of data sets in the healthcare industry and they've traditionally been extremely structured. You can think of HCC, CPT codes. You can think about how we go about billing, diagnosing diseases etc, and traditionally, all those applications, how you go about setting rates, and about measuring things etc, have been very structured and that was the way how things were done for a long time. And that was what was driving the state of analytics at that time. But somewhere around the time when Obama care got passed the entire focus started shifting from B2B to B2C and sort of placing the consumer first. So the entire concept of the Triple Aim came into focus, which was on number one around quality, number two patient outcomes and number three around cost. So to drive that and to put consumer experience first, you've got to put yourself in the consumer shoes. Understand what the consumer is thinking and the consumer is a very amorphous being. In terms of understanding perception, behavior, longitudinal consumer data, etc, all these are very retail, analytical concepts, which are slightly more probabilistic rather than being very deterministic in terms of how the industry and the data sets. And everything was sort of structured that sort of created the shift to analytics. And predictive and prescriptive analytics, sort of drove a lot of the transformation from a technology perspective, etc. Because now you have to understand digital, surveys, and unstructured data because a lot of these data sets are coming in ways and shapes which you're not used to, which are not structured. So you have to move from relational databases to non-relational databases. So that sort of drove a lot of those changes and started making the move towards more and more sophisticated ways of doing analytics. And then finally now we are at a point wherein we are saying “We can drive diagnosis, we can improve drug development, etc.” So, I don't know whether we'll get there because the industry sometimes gets its own way. But it's exciting to see that journey, that sort of gives you a sense for how you sort of evolved as a healthcare industry. AIM: How did the conversation around Generative AI in healthcare begin? Were problem statements in the healthcare industry the starting point for considering how Generative AI could be applied, or was it initially driven by speculative interest and hype? Please share insights on the origins of these discussions. Prashanth Sarpamale: I think a lot of us in the industry sort of started off in the same way that a lot of people externally have started off. A lot of us have been observing the buzz and seeing the movements in technology. I think this all started out as a huge improvement from a large language model perspective. As a technologist, you start taking a look at what these models do, how they are different from previous iterations and generations and how they've sort of improved the state of the art. But what is critical is, how you apply it into the healthcare industr

Generative AI and analytics in Healthcare with Prashant Sarpamale

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