It’s wintertime, so naturally we’re thinking about snowflakes. More specifically, Snowflake: the cloud-based data storage software company that made the largest software IPO ever back in 2020, hit a $100 billion market cap, and later plunged more than 50%.
Snowflake’s healthcare and life sciences data cloud provides health systems, payers, pharmaceuticals, and health tech companies with technology to analyze their data and get insight into their businesses.
Jesse Cugliotta, the global industry go-to-market lead for healthcare and life sciences at Snowflake, sat down with Healthcare Brew to chat about how the healthcare industry should use data and how he thinks data will affect healthcare in 2023.
This interview has been edited and condensed for clarity.
Health systems haven’t been the quickest to utilize data in the same way other industries have. Why is that?
A lot of their energy is going into the delivery of care itself versus building data and analytics efficiency, historically. Now that’s changing. The entire industry had its world rocked during Covid. It was essentially trying to deal with huge potential influxes of patients without a lot of predictability as to when or where they would show up, all while dealing with both personnel and supply shortages. Everyone recognized that now’s the time to try new things, because we don’t really have any other choice in this sort of scenario.
What are some ways health systems can use their data to improve care delivery?
Staffing is always a key challenge these days. There are things that can be done from a data analytic standpoint that can allow you to make the best decisions at any given time, and a big part of that is predicting the volume of patients that could potentially be coming in the door. Folks are looking at third-party data sources that have a predictive element to healthcare outcomes, commonly referred to as social determinants of health. There are statistics to say that around 80% of an individual’s health outcomes is determined by nonmedical factors, like consumer spending habits, which neighborhood you live in, [and] how far away you are from a grocery store with healthy options. Those things are directly correlated with health outcomes.
So, you can use those data sets to figure out how many patients will need to be treated at a certain time?
Yeah, and what could potentially be expected in terms of the severity.
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Got it. And that would also help in terms of things like supplies—knowing how much you need to order and being prepared for the types of procedures you’re gonna have to do, right?
Yeah. One of the areas we’re starting to see a lot of interest in is if data can be shared across enterprises to better predict demand for certain products and better prepare for potential shortages as they arise. If I know that I’m going to be in a supply-constrained environment, I have to be very particular about how I deploy specific products to the individual health systems or pharmacies to support them. Something that we’ve seen a lot of is the ability to use these data platforms and real-time sharing of that data as a way to mitigate some of these supply chain challenges.
How far away are we from health systems actually doing these things, like using data to predict patient flow?
The early adopters have started to come on board and put in place technologies like this. But I think we’ve really just scratched the surface when we look at the broader industry in terms of what’s possible. Healthcare providers in particular tend to lag behind some of the other players, either on the payer side or the life sciences side, because they’ve primarily been resource-constrained.
What role do you predict data will play in healthcare in 2023?
We’re seeing a lot of interest around unstructured data, which is any sort of data that isn’t well structured into a table—things like images and X-rays. Eighty percent of the world’s healthcare data exists in this format. Let’s say you’re a researcher in non-small cell lung cancer, and you want to identify every example that’s showing a specific type of tumor. You could go through thousands of images and look for that, but there are technologies now that can actually do this in an automated fashion. They can do things like image and natural language processing to pull these key elements out of these unstructured datasets, and they can do so across thousands of documents simultaneously. Those are the types of innovations that we’re starting to see right now—where the industry is finding better ways to get its arm around some of this huge volume of unstructured data.