Sramana Mitra: How do you see that playing out? Are you saying that physicians around the world start sharing case studies on how they have treated certain cases and what results they have gotten in some sort of a central exchange? Then the system does analysis and recommends specific instances. Is that what you’re saying?
Charlie Lougheed: Yes. Data within Explorys has two forms. One form is completely de-identified. What that de-identified form allows organizations to do is see patterns that are similar to theirs, expressed across a very large population. You have this statistical relevance. On top of that, one of the things that we provide is a technology platform that substrate and process a massive amount of data to do just what you said—identify patterns and correlations, define complex measures that are used to compare care and outcomes, as well as look at predictive models to determine if you have population that is borderline sick. Which of these patients are moving towards sick and what can you specifically do about them? Having that massive de-identified data allows you to build these very interesting models and see these trends.
The second type of data that we have is very much identifiable because it’s only used by that healthcare system. It’s just their data. In those cases, they need that because they need to be able to know which patients need additional care or outreach. Another example is when a patient is admitted to the hospital with congested heart failure. They treat that patient. Each patient is not equal with regards to their risk. What processes, outreaches, and activities can you do post-discharge for that patient? One of the things that we do is help identify those patients that are at high or medium risk of readmission and allow them to apply different care models.
You said it well though that a lot of this is looking at the data differently, deriving these patterns, and having a shared meaning of the truth. One version of the truth relates to what does a controlled heart failure patient population look like. What’s the definition of great, good, and bad care and outcomes?
Sramana Mitra: If you look around the different problems that are out there and given your expertise, where would you point entrepreneurs who are starting out now to look at in terms of identifying pain points to solve that are unsolved, white space areas?
Charlie Lougheed: Within Big Data, Explorys wouldn’t be what it is today if we hadn’t had some interesting outside perspective. We call it naïve perspective. Combine that with deep subject matter domain expertise and understanding. My message for healthcare around Big Data is you have to figure out how to marry both because the lessons that are learned outside of the healthcare industry are very applicable here. I gave an example of risk. A brokerage house has to manage risk in one way, which includes understanding the portfolio, understanding their customers in that portfolio, and the behaviors and trends.
Healthcare providers have to do something a little bit different than that but a lot of the same concepts hold true. I think organizations that can help bridge that thinking and perspective and who have great technical background as well as are domain-specific are the ones that are going to continue to thrive. My advice is as you’re forming your company, look for that magical mix. It’s out there. It’s a little bit elusive but it’s out there and you can find it.
Sramana Mitra: Great. Thank you for your time.