As part of a new interview series with healthcare leaders across the country, the Olive team had the chance to interview industry leader Aaron Black about the biggest challenges facing healthcare today and the potential for data to transform the industry. Previous to his current role as Chief Data Officer at Inova, he was the Director of Informatics dealing with genomics. Before that, he lived in Columbus, Ohio for 13 years, working at Nationwide Children’s Hospital and is a Miami University Graduate.
Q: To begin, we saw you’re an Ohio native and have worked as a consultant and manager in a few different industries prior to healthcare. What drove you to get involved with the healthcare space?
It was somewhat serendipitous. I was working in consulting in Columbus, Ohio and there was a company that was doing mental health billing. They were an EMR company, and they were the only ones in Ohio who could bill Medicaid and Medicare. They were growing so quickly that they didn’t have enough people to implement their software. I was a data person and was doing a lot of conversion software implementations at the time – the area was exploding, so that’s why I got into the field.
Once I was in the industry, I quickly observed healthcare was facing a critical accounting challenge – the way the industry billed, the way it charged – it didn’t match common practice across other industries. And with my accounting background, the current processes just didn’t make sense to me. I saw a misalignment there and thought this was an area where I could have an impact.
Q: Can you tell us about the Inova Translational Medicine Institute that you’re currently leading? What are a couple of examples of ways you’ve seen data analytics fuel innovation within the Institute?
The Institute is part of the Inova Health System, which is a community health system outside of Washington D.C., and was originally founded to look at the research that was being done in the genomics space. At the Institute we focus on more than sequencing individuals, we are considering the expression of those genes or microbiome, for example. Including that scope of work has allowed us to evolve and take the data that we’re now able to get from our patients and apply it to clinical care. We find ourselves frequently meeting and addressing challenges on how we make our data part of how we tackle the harder problems when it comes to predicting, preventing and treating people after they’ve gotten sick.
We’re leveraging analytics to sequence individuals and very large sets of data. We have custom applications that run, analyze and make sense of the collected data. Typically, we’re looking for a needle in the haystack, so we use literature and bring all kinds of data assets together, and then use the minds of the scientists and doctors that are treating the patients to understand if this is something we can actually treat, which is where we meet a distinct challenge – just because you find something doesn’t necessarily mean you can treat it. This work is really the application of genetics in practice to improve the health of our patients.
Q: What do you think has been the biggest impact in healthcare coming from data science?
Two distinct impacts come to mind. One is the ability to make discoveries a typical analyst or human couldn’t make on their own without the help of data science. Today, in healthcare we manage vast and unwieldy data sets that Excel or other traditional data analytics tools can’t handle with the power needed to analyze the data efficiently and effectively. If you can build software with the expertise of a multi-disciplinary group of clinicians, statisticians, and software engineers, then you can analyze data in ways we haven’t been able to do previously.
Secondly, data science is a motivator to accelerate the collection of data – it’s not just a transaction anymore. It’s no longer collected with the sole purpose of billing – it’s collected so we can use it to improve care, and not just for that specific individual – also for individuals that will be treated 5-10 years from now. This is a long game. How are we going to use this data to win the war on a specific disease, for instance, 5-10 years from now with the help of faster machines and better data? This is where we’re going to see the biggest impact, and it’s already started to happen. We’ve been collecting this data for over 10 years now, and we can look at that from a biological level and compare it to the patient outcomes, which will in-turn accelerate the adoption of data science throughout healthcare.
Q: Looking into the future, how do you see artificial intelligence helping you accomplish more with data science?
The term artificial intelligence is so often used as a branded term today, it’s easily misconstrued. For me, the machine learning and analytics aspects of AI is where I see the industry heading. In the future I see machines working alongside humans – discovering how to take the repetitive actions out, the ones that don’t scale. Analytics and machine learning will work in tandem to take on the repetitive, easier tasks, while people tackle the harder problems – like the rare diseases and the cases that just don’t make sense to us today. This advancement is going to refocus our people – the doctors and nurses – to take on the more challenging cases.
We’ll really change the way our healthcare system works, what we spend our money and time on, and where we spend our focus: the areas where we can drive change and have the biggest impact.
Q: What do you think is the key to improving interoperability in healthcare?
It’s incentives. Incentivizing people to interoperate. It’s not a technology problem. What’s the incentive for a provider to share their data with another provider when a patient is coming to them? If they share that data, they might lose market share. Today, the industry fails to find an inherent benefit without further motivation.
On the technology side, what’s the incentive for a provider to strive for interoperability outside of their vendor base? Interoperability means a lot of things too – people can call a fax or a courier interoperability, so how are we defining the term and how, as an industry, will we determine its success? And it can’t be everything. As an industry our challenge is to align incentives to that definition of success, then it must become a part of everything we do, rather than simply checking a box.