As part of a new interview series with healthcare leaders across the country, the Olive team had the chance to interview Brian Carlson about the biggest challenges facing healthcare today. Previous to his current role as Senior Director of Patient Experience at Vanderbilt University Medical Center, he was the COO/CEO of a physician group practice and a Practice Manager at Northwestern. Before that, he received a master’s degree in health services administration and an MBA from Xavier University.

Q: To begin, tell us about your background in healthcare and how you became Senior Director of Patient Experience at Vanderbilt University Medical Center.

I took the traditional route of college followed by graduate school, and in college I found healthcare was something I was very interested in. I considered doing pre-med, but ultimately got my undergraduate degree in psychology and went straight to graduate school where I got a master’s degree in health services administration and an MBA.

One of the greatest things about the program was the year-long required fellowship at a health system. That fellowship was where I could get mentored by a seasoned administrator and gain experience with how our complex healthcare system works in a protected environment. I didn’t have decision making authority, but I was put on projects to help advance the mission of the organization and got a lot of experience doing so. I think any administrator entering the field should really go through a residency or fellowship program.

From there, I got my first job in group practice management and then moved on to be COO/CEO of another physician group practice and then I was recruited to Vanderbilt in 2007. I started as an administrator for the Eye Institute and moved into the patient experience role in 2014, where I’m helping the system figure out how to create a consistent experience for the patients and families that need our help.

Q: In your opinion, what are some of the untapped opportunities to improve patient experience today?

The field of patient experience is evolving and I’m starting to think of it as just, “experience.” 

What I mean by that is that healthcare is complex to start with and dependent on the skills and expertise of our workforce to deliver care. So, the experience our workforce has every day has a profound impact on the care they’re able to deliver. In my mind, I’m broadening the term of experience because we need to consider the relationship between the patients and families that walk through our doors, and the experience our workforce is having every single day as they do their jobs. 

Healthcare is a calling – people come into this profession because they want to help people. But sometimes we get in our own way and make it difficult for those who are really skilled to do the most effective job that they can. The untapped potential is looking into the experience of our workforce and how can we help make that experience better, so they can do the best job possible.

Q: If you could eliminate one of the healthcare industry’s challenges overnight, which one would it be?

If I had to choose one challenge to eliminate, I immediately think of our need to, in a consistent fashion, share data across organizations and across individuals. We have so many restrictions on the way we can share data – and we have so much of it. If we could unmask that data, in a secure and safe way,  yet make it available whether it’s payer, provider, patient, or consumer space, and really cumulate that data, we could put some profound learnings into place to understand what it is that drives us as humans. 

Ultimately: How can we anticipate ahead of time if someone’s heading down a path, and how can we prevent that from happening? Thus decreasing the cost of healthcare.

Other industries have figured out how to share and use data in a constructive way to learn about their users. They’re trying to sell a product, but were trying to help an individual, so the opportunity is significant.

Q: How do you see artificial intelligence in healthcare impacting the patient experience?

Our ability to use our data in an effective fashion is what will enable us to learn from those past experiences. At the root level, it is the machine taking data to understand and predict what will happen in the future to improve care. Think from an experience standpoint – AI can help both internally and externally. 

On the internal side, how do we make our workforce processes more streamlined by using AI to automate repetitive tasks, to use the machine to generate some very basic administrative functions that today require people to do it manually, or become backlogged as employees never have the capacity to work through them. They do not always know what to work first, or they need to hunt and find additional information to assist them. Can we get the right data in front of the right eyes when it is needed? This could be in everything from eligibility checks to claims processing and to the front office functions. 

Within the clinical side, the possibilities are limitless from using AI to accurately diagnose and reduce errors to developing new medicines and treatments.  

On the external side, let’s consider the consumer space. B2C companies use data to predict habits around who will need what and when. In the healthcare space, you can start to imagine the experience where we know as a health system someone’s potential needs in a way that prevents, for instance, their need to go to the emergency room or prevents them from having to receive additional care down the road.

We recently conducted a study here at Vanderbilt, and the number one piece of feedback we received from our patients is that they expect that we already know them. I think our society has a growing expectation that if I provide you data or insights about me I expect you to do something with it.  There’s an expectation that healthcare is technologically advanced and an assumption that we have data to make predictions. While we’re heading down that road clinically with genetics and DNA, from an experience standpoint, we’re not there yet. The challenge to the industry is, how do we create a system of learning that can advance the skills of our providers to help predict and prevent problems in the future?

Q: Tell us about a person who mentored, inspired, or impacted you during your career.

I’ve been lucky to have so many individuals mentor me and impact me throughout my career, the difficult part would be choosing just one. But the common thread between them all is, availability – they’ve all willingly made themselves available to me over both successes and failures throughout my career. They were there to listen and understand, and then provided a direction or pose a question to help me move forward. Throughout my career, I knew I could reach out and call them, and they’d always answer the phone. They also built trust, and that trust was a two-way street. I knew I could be vulnerable with them and in turn, I knew they would always be honest with me. Lastly, they took pride in my work and I think got fulfillment in my success. As I’ve mentored people in my career, I’ve tried to bring those same attributes to the table, as well.

Subscribe to OliveReads here to read more about healthcare trends and insights from industry leaders.

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. 

Subscribe to OliveReads here to read more about healthcare trends and insights from industry leaders.