The Data Problem Holding Back Healthcare AI with Arun Rangamani | EXL

It all starts with data.

We kind of assume, or we have data to prove, 50% of the data generated by healthcare is still not touched.

Imagine that in a bank or in a financial services industry, a consumer's 50% data is not touched, is unheard of.

But in healthcare, industry experts, the CTOs, large healthcare organizations already confessed, 45-50% data never gets

used

for any kind of insights because it's not possible to thread them together.

So for us to have any kind of AI-based impact on the industry, it starts with getting the data right, which means a lot of effort that needs to go into preparing the data, making it ready.

So data prep will be the primary factor to decide whether you're ready for any kind of transformation.

The second important aspect is all the complex workflows.

So target those workflows which are most important.

and prepare the data around it so that the context and the use cases for AI-based transformation is real.

So those two are the most important primary factors in preparing for a transformation journey.

So get your data house in order essentially, and really find and focus initially on some of the use cases that are ripe for the most impact and expand from there.

That makes a lot of sense.

Maybe you could give us some concrete examples of the types of areas when you talk about a use case that you see that are so ready for AI transformation.

I

have a few of them, but the most important one is The encounters which happen in patients visit hospitals or physician offices, typically and traditionally, last three or four decades, the data which gets generated is typically a claim.

It is a structured information set.

It probably will have 200 to 300 attributes in it, but it does not truly contain all the clinical medical information set.

there is another set of data which is called medical records there are other unstructured data formats available which were not available for analysis or insights earlier because it took a lot of effort to translate the data because those technologies were not available a few years back but currently with the advent of ai with data transformation techniques and how to actually parse through those medical records.

And medical records are typically 600, 700 pages long.

And able to integrate that and use it for analysis, basically unstructured data to structured data to analysis is available now.

So I feel that that will be the big inflection point positively for the industry, particularly understanding the clinical decisions made in a process, because it has both a financial and a clinical impact to the organization, both the providers or the payer.

To have that information set from the structured and unstructured data will be so useful to control waste and abuse.

Just keep in mind, healthcare, as you were mentioning earlier, is a $5.3 trillion industry.