Successful use of predictive analytics gives us the ability to minimize unwanted future events and maximize future health without unnecessary expenditure of resources, according to Vice President and Medical Director of DaVita Health Economics and Outcomes Research, Steven Brunelli, MD. Listen to this podcast, in which Ryan Weir interviews Dr. Brunelli on predictive analytics. Dr. Brunelli discusses why predictive analytics are so important; what makes patients with kidney disease well-suited to predictive analytics; how to take a dataset and make it beneficial for patients, physicians and health plans; how an ideal CKD model is defined; how predictive analytics support value-based care arrangements; and finally, how predictive analytics might be used in health care in the future. Listen and read more DaVita Medical Insights here (https://blogs.davita.com/medical-insights/?utm_source=blubrry&utm_medium=social&utm_term=display_organic&utm_content=dmi_podcasthost&utm_campaign=davitapulse20).
Podcast Transcript:
Ryan Weir: 00:34 Hello everyone, and welcome to the DaVita Medical Insights Podcast. I'm your host Ryan Weir, and I'm part of DaVita's communications team. Today, we'll dig into predictive analytics with Dr. Steve Brunelli, Vice President for DaVita Clinical Research. Dr. Brunelli, thank you so much for joining us today.
Dr. Steve Brunelli: 00:49 Thanks for having me. It's a pleasure to be here and I'm excited to talk about predictive analytics, which is something that I think is going to be transformative in health care and in kidney care, in particular.
Ryan Weir: 01:00 That's great. Let's dive right in. In health care, everyone is talking about predictive analytics. Could you tell me why predictive analytics are so important and why now?
Dr. Steve Brunelli: 01:11 All right, I'll take that question in two parts. So the first part is: why predictive analytics? The answer is that predictive analytics let us get the right treatment to the right patient at the right time, and that's critically important. As our armamentarium of therapies grow, it's important to be able to target those therapies in efficient ways and in ways that will maximize the benefit to patients. Predictive analytics give us an increasing sophistication in how we deliver those services to patients.
Dr. Steve Brunelli: 01:48 To understand why now, I think it helps to kind of get a historic perspective. So predictive analytics did not spring full form from the head of Zeus. They didn't fall from the sky or come down on stone tablets. They're really an extension and an acceleration in the sophistication of a paradigm that we've used for many years. I liken it to the origins of rock and roll in the 1950s or hip hop in the 1980s. Those didn't derive de novo. They were extensions of prior musical traditions that suddenly accelerated and became exciting and new. That's what we're seeing in predictive analytics.
Dr. Steve Brunelli: 02:29 So what are the permissive conditions that allow for that? The first is we have, by historic standards, an unprecedented amount of data at our fingertips. As electronic health records have increased in their size and their scope, doctors used to scribble notes on index cards and stick them in a drawer in their office, and now everything is on databases in codified fields where people can easily find and index them. Those systems are becoming increasingly interconnected. We have the ability to connect not only various points in health care, doctor's notes, but also labs, observations of the payers like claims. So we have at our disposal now a level of information that didn't exist in prior eras.
Dr. Steve Brunelli: 03:24 At the same time, there's been an explosion of computing power. So the iPhone 5,
