Dr Sebastian Schneeweiss on How Rapid-Cycle Analytics Can Identify High-Risk, High-Cost Patients

Rapid-cycle anlytics of real-world evidence has an advantage over current strategies for analyzing real-world evidence because it is efficient, transparent, and scientifically validated, Sebastian Schneeweiss, MD, ScD, professor of medicine and epidemiology, Harvard Medical School and Brigham and Women's Hospital, and cofounder of Aetion.

Transcript

Rapid-cycle analytics of real-world evidence has been labeled a promising approach for identifying high-risk, high-cost patients. How does this approach improve upon how evidence is currently analyzed?

So, health plans need this type of evidence all the time, and they have hundreds of analysts going through the data, usually claims data and sometimes electronic health record data, in order to identify such patients and intervene on those patients. What is different between the traditional approach and rapid-cycle analytics is that in the traditional approach, you have line programming happening. People have statistical software packages and they write codes, and they write codes over and over again. That is not very transparent; you don’t know exactly what was done, you trust the analyst. It’s not very efficient, it’s not fast, it’s not replicatable, it’s not transparent, and these are all issues that translate into the usability of the findings. It’s not saying these findings are wrong, necessarily, they’re just less useful for the key stakeholders.

What rapid cycle analytics is doing is we take a platform and put the platform on top of the data, and then whatever question comes along, we can use this analytic platform in order to analyze these questions about who are the high-risk patients, what is the most effective treatment, what is the most safe treatment in these patients. We can do this much faster now because the software is already connected with the data. We can do this much more transparently, we can immediately produce reports on what kind of coding exactly was used, and things like that.

Decision makers really appreciate the fact that they get the findings fast, that they get the findings scientifically validated, and they get the findings transparently. That is key for decision making, we think.